Feature Selection : Evaluating Model-Independent Feature Importance for Predictors with Dichotomous Categorical Responses
John Pauline Pineda
December 14, 2022
1. Table of Contents
This project explores different model-independent feature importance metrics for predictors with dichotomous categorical responses using various helpful packages in R. Metrics applied in the analysis to evaluate feature importance for numeric predictors included the Area under the Receiver Operating Characteristics Curve (AUROC), Absolute T-Test Statistic, Maximal Information Coefficient and Relief Values, while those for factor predictors included the Volcano Plot Using Fisher’s Exact Test and Volcano Plot Using Gain Ratio. All results were consolidated in a Summary presented at the end of the document.
Model-independent feature importance determines the degree of relationship, usefulness and contribution of the independent variables (features) with respect to the dependent variables (target responses), outside the context of the particular model structure being applied. Analyzing the importance metrics of features allow the discovery and inclusion of only relevant features, reducing the number of meaningful variables in the model thereby providing more targeted insights into the data, speeding up the processing time, boosting its predictive performance and improving the interpretability of the results. The algorithms applied in this study (mostly contained in the caret package, stats package, minerva package and CORElearn packages) attempt to assign scores to input features based on their relevance at discriminating or predicting the target variable.
1.1 Sample Data
The Solubility dataset from the AppliedPredictiveModeling package was used for this illustrated example. The original numeric response was transformed to simulate a dichotomous categorical variable.
Preliminary dataset assessment:
[A] 1267 rows (observations)
[A.1] Train Set = 951 observations
[A.2] Test Set = 316 observations
[B] 229 columns (variables)
[B.1] 1/229 response = Log_Solubility_Class variable (factor)
[B.1.1] Levels = Log_Solubility_Class=Low < Log_Solubility_Class=High
[B.2] 228/229 predictors = All remaining variables (208/228 factor + 20/228 numeric)
Code Chunk | Output
##################################
# Loading R libraries
##################################
library(AppliedPredictiveModeling)
library(caret)
library(rpart)
library(lattice)
library(dplyr)
library(tidyr)
library(moments)
library(skimr)
library(RANN)
library(pls)
library(corrplot)
library(tidyverse)
library(lares)
library(DMwR2)
library(gridExtra)
library(rattle)
library(rpart.plot)
library(RColorBrewer)
library(stats)
library(nnet)
library(elasticnet)
library(earth)
library(party)
library(kernlab)
library(randomForest)
library(Cubist)
library(pROC)
library(mda)
library(klaR)
library(pamr)
library(minerva)
library(CORElearn)
##################################
# Loading source and
# formulating the train set
##################################
data(solubility)
Solubility_Train <- as.data.frame(cbind(solTrainY,solTrainX))
Solubility_Test <- as.data.frame(cbind(solTestY,solTestX))
##################################
# Applying dichotomization and
# defining the response variable
##################################
Solubility_Train$Log_Solubility_Class <- ifelse(Solubility_Train$solTrainY<mean(Solubility_Train$solTrainY),
"Low","High")
Solubility_Train$Log_Solubility_Class <- factor(Solubility_Train$Log_Solubility_Class,
levels = c("Low","High"))
Solubility_Test$Log_Solubility_Class <- ifelse(Solubility_Test$solTestY<mean(Solubility_Train$solTrainY),
"Low","High")
Solubility_Test$Log_Solubility_Class <- factor(Solubility_Test$Log_Solubility_Class,
levels = c("Low","High"))
Solubility_Train$solTrainY <- NULL
Solubility_Test$solTestY <- NULL
##################################
# Performing a general exploration of the train set
##################################
dim(Solubility_Train)## [1] 951 229str(Solubility_Train)## 'data.frame': 951 obs. of 229 variables:
## $ FP001 : int 0 0 1 0 0 1 0 1 1 1 ...
## $ FP002 : int 1 1 1 0 0 0 1 0 0 1 ...
## $ FP003 : int 0 0 1 1 1 1 0 1 1 1 ...
## $ FP004 : int 0 1 1 0 1 1 1 1 1 1 ...
## $ FP005 : int 1 1 1 0 1 0 1 0 0 1 ...
## $ FP006 : int 0 1 0 0 1 0 0 0 1 1 ...
## $ FP007 : int 0 1 0 1 0 0 0 1 1 1 ...
## $ FP008 : int 1 1 1 0 0 0 1 0 0 0 ...
## $ FP009 : int 0 0 0 0 1 1 1 0 1 0 ...
## $ FP010 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP011 : int 0 1 0 0 0 0 0 0 1 0 ...
## $ FP012 : int 0 0 0 0 0 1 0 1 0 0 ...
## $ FP013 : int 0 0 0 0 1 0 1 0 0 0 ...
## $ FP014 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ FP015 : int 1 1 1 1 1 1 1 1 1 1 ...
## $ FP016 : int 0 1 0 0 1 1 0 1 0 0 ...
## $ FP017 : int 0 0 1 1 0 0 0 0 1 1 ...
## $ FP018 : int 0 1 0 0 0 0 0 0 0 0 ...
## $ FP019 : int 1 0 0 0 1 0 1 0 0 0 ...
## $ FP020 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP021 : int 0 0 0 0 0 1 0 0 1 0 ...
## $ FP022 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ FP023 : int 0 0 0 1 0 0 0 0 1 0 ...
## $ FP024 : int 1 0 0 0 1 0 0 0 0 0 ...
## $ FP025 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP026 : int 1 0 0 0 0 0 1 0 0 0 ...
## $ FP027 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ FP028 : int 0 1 0 0 0 0 0 0 1 1 ...
## $ FP029 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP030 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ FP031 : int 0 0 0 0 0 0 0 1 0 0 ...
## $ FP032 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP033 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP034 : int 0 0 0 0 1 0 0 0 0 1 ...
## $ FP035 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ FP036 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP037 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ FP038 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP039 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP040 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP041 : int 0 0 0 1 0 0 0 0 1 0 ...
## $ FP042 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP043 : int 0 1 0 0 0 0 0 0 0 0 ...
## $ FP044 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP045 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP046 : int 0 1 0 0 0 0 1 0 0 1 ...
## $ FP047 : int 0 1 1 0 0 0 1 0 0 0 ...
## $ FP048 : int 0 0 0 0 0 0 0 1 0 0 ...
## $ FP049 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ FP050 : int 0 0 0 0 0 0 0 1 0 1 ...
## $ FP051 : int 0 1 0 0 0 0 0 0 0 0 ...
## $ FP052 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ FP053 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ FP054 : int 0 0 0 1 0 0 0 0 1 1 ...
## $ FP055 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP056 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP057 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ FP058 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ FP059 : int 0 0 0 0 0 0 0 1 0 0 ...
## $ FP060 : int 0 1 1 0 0 0 0 1 1 0 ...
## $ FP061 : int 0 0 1 0 0 0 0 1 1 0 ...
## $ FP062 : int 0 0 1 0 0 1 0 1 1 1 ...
## $ FP063 : int 1 1 0 0 1 1 1 0 0 1 ...
## $ FP064 : int 0 1 1 0 1 1 0 1 0 0 ...
## $ FP065 : int 1 1 0 0 1 0 1 0 1 1 ...
## $ FP066 : int 1 0 1 1 1 1 1 1 1 1 ...
## $ FP067 : int 1 1 0 0 1 1 1 0 0 1 ...
## $ FP068 : int 0 1 0 0 1 1 1 0 0 1 ...
## $ FP069 : int 1 0 1 1 1 1 0 1 1 0 ...
## $ FP070 : int 1 1 0 1 0 0 1 0 1 0 ...
## $ FP071 : int 0 0 0 0 0 0 1 0 1 1 ...
## $ FP072 : int 0 1 1 0 0 1 0 1 1 1 ...
## $ FP073 : int 0 1 1 0 0 0 0 0 1 0 ...
## $ FP074 : int 0 1 0 0 0 0 0 0 1 0 ...
## $ FP075 : int 0 1 0 0 1 1 1 0 0 1 ...
## $ FP076 : int 1 1 0 0 0 0 1 0 1 1 ...
## $ FP077 : int 0 1 0 1 0 0 0 1 1 1 ...
## $ FP078 : int 0 1 0 0 0 0 0 0 1 0 ...
## $ FP079 : int 1 1 1 1 1 0 1 0 1 1 ...
## $ FP080 : int 0 1 0 0 1 1 1 1 0 0 ...
## $ FP081 : int 0 0 1 1 0 0 0 1 1 1 ...
## $ FP082 : int 1 1 1 0 1 1 1 0 1 1 ...
## $ FP083 : int 0 0 0 0 1 0 0 0 0 1 ...
## $ FP084 : int 1 1 0 0 1 0 1 0 0 0 ...
## $ FP085 : int 0 1 0 0 0 0 1 0 0 0 ...
## $ FP086 : int 0 0 0 1 1 0 0 1 1 1 ...
## $ FP087 : int 1 1 1 1 1 0 1 0 1 1 ...
## $ FP088 : int 0 1 0 0 0 0 0 1 1 0 ...
## $ FP089 : int 1 1 0 0 0 0 1 0 0 0 ...
## $ FP090 : int 0 1 0 1 0 0 0 1 1 1 ...
## $ FP091 : int 1 1 0 0 1 0 1 0 0 1 ...
## $ FP092 : int 0 0 0 0 1 1 1 0 1 0 ...
## $ FP093 : int 0 1 0 1 0 0 0 1 1 1 ...
## $ FP094 : int 0 0 0 0 1 0 0 1 0 0 ...
## $ FP095 : int 0 0 0 0 0 0 0 0 1 1 ...
## $ FP096 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ FP097 : int 1 1 0 0 0 0 1 0 1 0 ...
## $ FP098 : int 0 0 1 0 0 0 0 1 0 0 ...
## $ FP099 : int 0 0 0 0 0 0 0 0 1 0 ...
## [list output truncated]summary(Solubility_Train)## FP001 FP002 FP003 FP004
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.4932 Mean :0.5394 Mean :0.4364 Mean :0.5846
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP005 FP006 FP007 FP008
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :1.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.5794 Mean :0.4006 Mean :0.3638 Mean :0.326
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP009 FP010 FP011 FP012
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.2797 Mean :0.1788 Mean :0.2145 Mean :0.1767
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP013 FP014 FP015 FP016
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.1661 Mean :0.1609 Mean :0.8601 Mean :0.1462
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP017 FP018 FP019 FP020
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.1441 Mean :0.1314 Mean :0.122 Mean :0.1199
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP021 FP022 FP023 FP024
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.1209 Mean :0.1041 Mean :0.123 Mean :0.1125
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP025 FP026 FP027 FP028
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.1157 Mean :0.08412 Mean :0.09779 Mean :0.1062
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.0000
## FP029 FP030 FP031 FP032
## Min. :0.000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.102 Mean :0.09359 Mean :0.08938 Mean :0.07361
## 3rd Qu.:0.000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP033 FP034 FP035 FP036
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.0694 Mean :0.07992 Mean :0.07256 Mean :0.07571
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP037 FP038 FP039 FP040
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.07045 Mean :0.08622 Mean :0.07466 Mean :0.06835
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP041 FP042 FP043 FP044
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06309 Mean :0.05678 Mean :0.06625 Mean :0.05994
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP045 FP046 FP047 FP048
## Min. :0.00000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.05573 Mean :0.3155 Mean :0.266 Mean :0.1241
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP049 FP050 FP051 FP052
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.122 Mean :0.1125 Mean :0.1094 Mean :0.09148
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## FP053 FP054 FP055 FP056
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.09359 Mean :0.07571 Mean :0.05363 Mean :0.06519
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP057 FP058 FP059 FP060
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1199 Mean :0.1136 Mean :0.05468 Mean :0.4816
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP061 FP062 FP063 FP064
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.4469 Mean :0.4374 Mean :0.4259 Mean :0.4164
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP065 FP066 FP067 FP068
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.5931 Mean :0.6099 Mean :0.3796 Mean :0.3617
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP069 FP070 FP071 FP072
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :1.0000
## Mean :0.3617 Mean :0.3554 Mean :0.327 Mean :0.6583
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP073 FP074 FP075 FP076
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3102 Mean :0.3249 Mean :0.3386 Mean :0.3281
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP077 FP078 FP079 FP080
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.3207 Mean :0.3039 Mean :0.6898 Mean :0.3028
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP081 FP082 FP083 FP084
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :1.000 Median :0.0000 Median :0.000
## Mean :0.2787 Mean :0.714 Mean :0.2734 Mean :0.286
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.000
## FP085 FP086 FP087 FP088
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.2555 Mean :0.2692 Mean :0.7266 Mean :0.2629
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP089 FP090 FP091 FP092
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.000
## Mean :0.2471 Mean :0.2492 Mean :0.225 Mean :0.244
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.000
## FP093 FP094 FP095 FP096
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.244 Mean :0.2313 Mean :0.2198 Mean :0.2177
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP097 FP098 FP099 FP100
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.2355 Mean :0.2376 Mean :0.2271 Mean :0.2313
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP101 FP102 FP103 FP104
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.2366 Mean :0.2019 Mean :0.2187 Mean :0.2229
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP105 FP106 FP107 FP108
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.2156 Mean :0.1914 Mean :0.2114 Mean :0.205
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP109 FP110 FP111 FP112
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1767 Mean :0.2061 Mean :0.1966 Mean :0.1945
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP113 FP114 FP115 FP116
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1956 Mean :0.1556 Mean :0.1788 Mean :0.1924
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP117 FP118 FP119 FP120
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.1788 Mean :0.1924 Mean :0.163 Mean :0.1661
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP121 FP122 FP123 FP124
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.000 Median :0.0000 Median :0.0000
## Mean :0.1399 Mean :0.164 Mean :0.1672 Mean :0.1619
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP125 FP126 FP127 FP128
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1556 Mean :0.1483 Mean :0.1399 Mean :0.1483
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP129 FP130 FP131 FP132
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1388 Mean :0.1052 Mean :0.1262 Mean :0.1251
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP133 FP134 FP135 FP136
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1262 Mean :0.1272 Mean :0.1262 Mean :0.1209
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP137 FP138 FP139 FP140
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1157 Mean :0.1115 Mean :0.08202 Mean :0.1115
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP141 FP142 FP143 FP144
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1167 Mean :0.1094 Mean :0.08097 Mean :0.1041
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP145 FP146 FP147 FP148
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.000 Median :0.0000 Median :0.00000
## Mean :0.1041 Mean :0.103 Mean :0.1052 Mean :0.08728
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.00000
## FP149 FP150 FP151 FP152
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.09043 Mean :0.07886 Mean :0.05573 Mean :0.08202
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP153 FP154 FP155 FP156
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.07781 Mean :0.03785 Mean :0.0694 Mean :0.07045
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## FP157 FP158 FP159 FP160
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06204 Mean :0.05363 Mean :0.07045 Mean :0.06835
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP161 FP162 FP163 FP164
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.06625 Mean :0.4953 Mean :0.4763 Mean :0.6278
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP165 FP166 FP167 FP168
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.3491 Mean :0.3312 Mean :0.3281 Mean :0.6656
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP169 FP170 FP171 FP172
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.000 Median :0.0000 Median :0.0000
## Mean :0.1861 Mean :0.184 Mean :0.1693 Mean :0.1514
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP173 FP174 FP175 FP176
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.000 Median :0.0000 Median :0.0000 Median :0.000
## Mean :0.142 Mean :0.1304 Mean :0.1346 Mean :0.122
## 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP177 FP178 FP179 FP180
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1209 Mean :0.1209 Mean :0.09779 Mean :0.1073
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP181 FP182 FP183 FP184
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.09359 Mean :0.09884 Mean :0.07571 Mean :0.08412
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP185 FP186 FP187 FP188
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.08517 Mean :0.07676 Mean :0.07256 Mean :0.06835
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP189 FP190 FP191 FP192
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.07676 Mean :0.07256 Mean :0.07045 Mean :0.06099
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP193 FP194 FP195 FP196
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06204 Mean :0.05889 Mean :0.06099 Mean :0.05678
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP197 FP198 FP199 FP200
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.05258 Mean :0.05678 Mean :0.04732 Mean :0.04942
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP201 FP202 FP203 FP204
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.05258 Mean :0.2576 Mean :0.1146 Mean :0.09884
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## FP205 FP206 FP207 FP208
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.07781 Mean :0.05994 Mean :0.05678 Mean :0.1125
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.0000
## MolWeight NumAtoms NumNonHAtoms NumBonds
## Min. : 46.09 Min. : 5.00 Min. : 2.00 Min. : 4.00
## 1st Qu.:122.61 1st Qu.:17.00 1st Qu.: 8.00 1st Qu.:17.00
## Median :179.23 Median :22.00 Median :12.00 Median :23.00
## Mean :201.65 Mean :25.51 Mean :13.16 Mean :25.91
## 3rd Qu.:264.34 3rd Qu.:31.00 3rd Qu.:17.00 3rd Qu.:31.50
## Max. :665.81 Max. :94.00 Max. :47.00 Max. :97.00
## NumNonHBonds NumMultBonds NumRotBonds NumDblBonds
## Min. : 1.00 Min. : 0.000 Min. : 0.000 Min. :0.000
## 1st Qu.: 8.00 1st Qu.: 1.000 1st Qu.: 0.000 1st Qu.:0.000
## Median :12.00 Median : 6.000 Median : 2.000 Median :1.000
## Mean :13.56 Mean : 6.148 Mean : 2.251 Mean :1.006
## 3rd Qu.:18.00 3rd Qu.:10.000 3rd Qu.: 3.500 3rd Qu.:2.000
## Max. :50.00 Max. :25.000 Max. :16.000 Max. :7.000
## NumAromaticBonds NumHydrogen NumCarbon NumNitrogen
## Min. : 0.000 Min. : 0.00 Min. : 1.000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.: 7.00 1st Qu.: 6.000 1st Qu.:0.0000
## Median : 6.000 Median :11.00 Median : 9.000 Median :0.0000
## Mean : 5.121 Mean :12.35 Mean : 9.893 Mean :0.8128
## 3rd Qu.: 6.000 3rd Qu.:16.00 3rd Qu.:12.000 3rd Qu.:1.0000
## Max. :25.000 Max. :47.00 Max. :33.000 Max. :6.0000
## NumOxygen NumSulfer NumChlorine NumHalogen
## Min. : 0.000 Min. :0.000 Min. : 0.0000 Min. : 0.0000
## 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.0000 1st Qu.: 0.0000
## Median : 1.000 Median :0.000 Median : 0.0000 Median : 0.0000
## Mean : 1.574 Mean :0.164 Mean : 0.5563 Mean : 0.6982
## 3rd Qu.: 2.000 3rd Qu.:0.000 3rd Qu.: 0.0000 3rd Qu.: 1.0000
## Max. :13.000 Max. :4.000 Max. :10.0000 Max. :10.0000
## NumRings HydrophilicFactor SurfaceArea1 SurfaceArea2
## Min. :0.000 Min. :-0.98500 Min. : 0.00 Min. : 0.00
## 1st Qu.:0.000 1st Qu.:-0.76300 1st Qu.: 9.23 1st Qu.: 10.63
## Median :1.000 Median :-0.31400 Median : 29.10 Median : 33.12
## Mean :1.402 Mean :-0.02059 Mean : 36.46 Mean : 40.23
## 3rd Qu.:2.000 3rd Qu.: 0.31300 3rd Qu.: 53.28 3rd Qu.: 60.66
## Max. :7.000 Max. :13.48300 Max. :331.94 Max. :331.94
## Log_Solubility_Class
## Low :427
## High:524
##
##
##
## ##################################
# Performing a general exploration of the test set
##################################
dim(Solubility_Test)## [1] 316 229str(Solubility_Test)## 'data.frame': 316 obs. of 229 variables:
## $ FP001 : int 1 1 0 0 1 1 1 0 1 0 ...
## $ FP002 : int 0 0 1 0 1 0 0 0 0 1 ...
## $ FP003 : int 0 1 0 1 0 0 0 0 1 0 ...
## $ FP004 : int 1 1 0 0 1 1 1 1 1 0 ...
## $ FP005 : int 0 0 1 0 1 0 0 0 0 1 ...
## $ FP006 : int 0 1 0 1 1 0 0 0 0 0 ...
## $ FP007 : int 0 0 0 0 0 0 0 1 1 0 ...
## $ FP008 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ FP009 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP010 : int 1 0 1 0 0 0 0 0 0 0 ...
## $ FP011 : int 0 1 0 0 1 0 0 0 0 0 ...
## $ FP012 : int 0 1 0 0 0 1 0 1 0 0 ...
## $ FP013 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP014 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP015 : int 1 1 0 1 1 1 1 1 1 1 ...
## $ FP016 : int 0 1 0 0 0 0 0 1 0 0 ...
## $ FP017 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP018 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP019 : int 0 0 0 0 1 0 0 0 0 1 ...
## $ FP020 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ FP021 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP022 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP023 : int 0 0 0 0 0 0 1 0 0 0 ...
## $ FP024 : int 0 0 0 0 1 0 0 0 0 1 ...
## $ FP025 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP026 : int 0 0 0 0 0 0 0 0 0 1 ...
## $ FP027 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ FP028 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ FP029 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP030 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ FP031 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP032 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP033 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP034 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP035 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP036 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP037 : int 0 0 0 0 0 0 0 0 1 0 ...
## $ FP038 : int 1 0 0 0 0 0 0 0 0 0 ...
## $ FP039 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP040 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ FP041 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP042 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP043 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP044 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP045 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP046 : int 0 0 1 0 0 0 0 0 0 1 ...
## $ FP047 : int 0 0 0 0 1 0 0 0 0 0 ...
## $ FP048 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP049 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP050 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP051 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP052 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP053 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP054 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP055 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP056 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP057 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP058 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP059 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP060 : int 1 1 1 0 0 1 0 1 0 0 ...
## $ FP061 : int 1 1 1 0 0 1 0 0 0 0 ...
## $ FP062 : int 1 1 0 0 1 1 1 0 1 0 ...
## $ FP063 : int 0 1 0 1 1 0 0 0 0 1 ...
## $ FP064 : int 1 1 0 0 0 0 0 0 1 0 ...
## $ FP065 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP066 : int 0 1 0 1 0 1 0 0 1 1 ...
## $ FP067 : int 0 1 0 1 1 0 0 0 0 1 ...
## $ FP068 : int 0 1 0 1 1 0 0 0 0 0 ...
## $ FP069 : int 0 0 0 0 0 0 0 0 1 1 ...
## $ FP070 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP071 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP072 : int 1 1 1 0 1 1 1 1 1 0 ...
## $ FP073 : int 1 0 1 0 0 0 0 0 0 0 ...
## $ FP074 : int 0 0 1 0 0 0 0 0 1 0 ...
## $ FP075 : int 0 1 0 1 0 0 0 1 0 0 ...
## $ FP076 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP077 : int 0 0 0 1 0 0 0 1 0 0 ...
## $ FP078 : int 0 0 1 0 0 0 0 0 0 0 ...
## $ FP079 : int 0 0 1 1 1 0 0 0 0 1 ...
## $ FP080 : int 1 1 0 1 0 0 0 1 0 0 ...
## $ FP081 : int 0 0 0 1 0 0 0 0 1 0 ...
## $ FP082 : int 0 0 1 0 1 0 0 0 0 1 ...
## $ FP083 : int 0 1 0 1 1 0 0 0 0 0 ...
## $ FP084 : int 0 0 0 1 1 0 0 1 0 1 ...
## $ FP085 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP086 : int 0 0 0 1 0 0 0 0 0 0 ...
## $ FP087 : int 0 0 1 1 1 0 0 1 0 1 ...
## $ FP088 : int 1 0 0 0 0 0 0 1 1 0 ...
## $ FP089 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP090 : int 0 0 0 1 0 0 0 1 0 0 ...
## $ FP091 : int 0 0 0 1 1 0 0 0 0 0 ...
## $ FP092 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP093 : int 0 0 0 1 0 0 0 1 0 0 ...
## $ FP094 : int 0 1 0 0 0 0 0 0 1 0 ...
## $ FP095 : int 0 0 1 1 0 0 0 0 0 0 ...
## $ FP096 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP097 : int 0 0 0 0 0 0 0 0 0 0 ...
## $ FP098 : int 1 1 0 0 0 1 0 0 0 0 ...
## $ FP099 : int 0 0 0 0 0 0 0 0 0 0 ...
## [list output truncated]summary(Solubility_Test)## FP001 FP002 FP003 FP004
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :1.0000 Median :0.000 Median :1.0000
## Mean :0.4684 Mean :0.5854 Mean :0.443 Mean :0.5316
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP005 FP006 FP007 FP008
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.6171 Mean :0.3513 Mean :0.3544 Mean :0.3608
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP009 FP010 FP011 FP012
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.000 Median :0.0000 Median :0.0000
## Mean :0.2627 Mean :0.193 Mean :0.1741 Mean :0.1677
## 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP013 FP014 FP015 FP016
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.1646 Mean :0.1582 Mean :0.8291 Mean :0.1424
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP017 FP018 FP019 FP020
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.0000
## Mean :0.1487 Mean :0.08544 Mean :0.1139 Mean :0.1076
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP021 FP022 FP023 FP024
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1076 Mean :0.1171 Mean :0.08544 Mean :0.0981
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP025 FP026 FP027 FP028
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.07911 Mean :0.1171 Mean :0.07911 Mean :0.05696
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP029 FP030 FP031 FP032
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.0000
## Mean :0.05063 Mean :0.08228 Mean :0.0981 Mean :0.1297
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP033 FP034 FP035 FP036
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.1203 Mean :0.06646 Mean :0.0981 Mean :0.06013
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## FP037 FP038 FP039 FP040
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.09494 Mean :0.03165 Mean :0.06329 Mean :0.05696
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP041 FP042 FP043 FP044
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.06013 Mean :0.06013 Mean :0.0443 Mean :0.06013
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## FP045 FP046 FP047 FP048
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.06329 Mean :0.3259 Mean :0.2975 Mean :0.1139
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP049 FP050 FP051 FP052
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.1076 Mean :0.1139 Mean :0.05696 Mean :0.1044
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP053 FP054 FP055 FP056
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.06013 Mean :0.0981 Mean :0.09177 Mean :0.06329
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP057 FP058 FP059 FP060
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1234 Mean :0.1361 Mean :0.0443 Mean :0.4525
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP061 FP062 FP063 FP064
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.3924 Mean :0.4272 Mean :0.3576 Mean :0.3892
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP065 FP066 FP067 FP068
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :1.0000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.5981 Mean :0.6171 Mean :0.3259 Mean :0.2911
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP069 FP070 FP071 FP072
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.3734 Mean :0.3323 Mean :0.3449 Mean :0.6456
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP073 FP074 FP075 FP076
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.2911 Mean :0.3259 Mean :0.2563 Mean :0.3165
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP077 FP078 FP079 FP080
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.307 Mean :0.3101 Mean :0.7278 Mean :0.2627
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP081 FP082 FP083 FP084
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.000 Median :1.0000 Median :0.0000 Median :0.0000
## Mean :0.288 Mean :0.7437 Mean :0.2532 Mean :0.2247
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP085 FP086 FP087 FP088
## Min. :0.000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:1.0000 1st Qu.:0.0000
## Median :0.000 Median :0.0000 Median :1.0000 Median :0.0000
## Mean :0.269 Mean :0.2722 Mean :0.7627 Mean :0.2437
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP089 FP090 FP091 FP092
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.2532 Mean :0.2278 Mean :0.231 Mean :0.2184
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP093 FP094 FP095 FP096
## Min. :0.0000 Min. :0.00 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.00 Median :0.0000 Median :0.0000
## Mean :0.2152 Mean :0.25 Mean :0.2057 Mean :0.1867
## 3rd Qu.:0.0000 3rd Qu.:0.25 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.00 Max. :1.0000 Max. :1.0000
## FP097 FP098 FP099 FP100
## Min. :0.0000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.2089 Mean :0.2025 Mean :0.212 Mean :0.1804
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP101 FP102 FP103 FP104
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1772 Mean :0.1456 Mean :0.2184 Mean :0.1835
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP105 FP106 FP107 FP108
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.2152 Mean :0.1361 Mean :0.1962 Mean :0.1804
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP109 FP110 FP111 FP112
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1741 Mean :0.1646 Mean :0.1804 Mean :0.1772
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP113 FP114 FP115 FP116
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1646 Mean :0.1772 Mean :0.1582 Mean :0.1487
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP117 FP118 FP119 FP120
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1709 Mean :0.1171 Mean :0.1677 Mean :0.1551
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP121 FP122 FP123 FP124
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1076 Mean :0.1361 Mean :0.1456 Mean :0.1329
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP125 FP126 FP127 FP128
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1203 Mean :0.1139 Mean :0.1487 Mean :0.1076
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP129 FP130 FP131 FP132
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.0000
## Mean :0.1392 Mean :0.08228 Mean :0.1076 Mean :0.1266
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP133 FP134 FP135 FP136
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.1361 Mean :0.08544 Mean :0.06329 Mean :0.1013
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.0000
## FP137 FP138 FP139 FP140
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.08861 Mean :0.08228 Mean :0.06329 Mean :0.08861
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP141 FP142 FP143 FP144
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.06962 Mean :0.09494 Mean :0.0538 Mean :0.09177
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## FP145 FP146 FP147 FP148
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06329 Mean :0.09177 Mean :0.06962 Mean :0.07911
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP149 FP150 FP151 FP152
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.0000
## Mean :0.08228 Mean :0.06646 Mean :0.03165 Mean :0.0538
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.0000
## FP153 FP154 FP155 FP156
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.03481 Mean :0.03165 Mean :0.06646 Mean :0.04747
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP157 FP158 FP159 FP160
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.05696 Mean :0.07911 Mean :0.03481 Mean :0.03481
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP161 FP162 FP163 FP164
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :1.0000 Median :0.0000 Median :1.0000
## Mean :0.03481 Mean :0.5316 Mean :0.4525 Mean :0.6551
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP165 FP166 FP167 FP168
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.3196 Mean :0.3386 Mean :0.3006 Mean :0.7152
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP169 FP170 FP171 FP172
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1867 Mean :0.1551 Mean :0.1297 Mean :0.1487
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP173 FP174 FP175 FP176
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.0000
## Mean :0.1361 Mean :0.1551 Mean :0.1329 Mean :0.1076
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP177 FP178 FP179 FP180
## Min. :0.0000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.1013 Mean :0.1076 Mean :0.1392 Mean :0.06962
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## FP181 FP182 FP183 FP184
## Min. :0.0000 Min. :0.00000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.0000 Median :0.00000
## Mean :0.1044 Mean :0.07595 Mean :0.1329 Mean :0.09494
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.0000 Max. :1.00000
## FP185 FP186 FP187 FP188
## Min. :0.0000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.0981 Mean :0.06013 Mean :0.06646 Mean :0.06962
## 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP189 FP190 FP191 FP192
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.04114 Mean :0.0538 Mean :0.05696 Mean :0.06962
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP193 FP194 FP195 FP196
## Min. :0.00000 Min. :0.00000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.00000 Median :0.00000 Median :0.00000
## Mean :0.06962 Mean :0.06646 Mean :0.05063 Mean :0.06962
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.00000 Max. :1.00000 Max. :1.00000
## FP197 FP198 FP199 FP200
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.06329 Mean :0.0443 Mean :0.07278 Mean :0.06329
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP201 FP202 FP203 FP204
## Min. :0.00000 Min. :0.0000 Min. :0.0000 Min. :0.00000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000
## Median :0.00000 Median :0.0000 Median :0.0000 Median :0.00000
## Mean :0.04114 Mean :0.2658 Mean :0.1361 Mean :0.09494
## 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## FP205 FP206 FP207 FP208
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.0000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.0000
## Mean :0.07911 Mean :0.05063 Mean :0.0443 Mean :0.1361
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## MolWeight NumAtoms NumNonHAtoms NumBonds NumNonHBonds
## Min. : 56.07 Min. : 5.0 Min. : 3.00 Min. : 4 Min. : 2.0
## 1st Qu.:121.91 1st Qu.:17.0 1st Qu.: 8.00 1st Qu.:16 1st Qu.: 8.0
## Median :170.11 Median :22.0 Median :11.00 Median :23 Median :12.0
## Mean :194.12 Mean :24.6 Mean :12.71 Mean :25 Mean :13.1
## 3rd Qu.:253.82 3rd Qu.:29.0 3rd Qu.:16.00 3rd Qu.:30 3rd Qu.:17.0
## Max. :478.92 Max. :68.0 Max. :33.00 Max. :71 Max. :36.0
## NumMultBonds NumRotBonds NumDblBonds NumAromaticBonds
## Min. : 0.000 Min. : 0.000 Min. :0.0000 Min. : 0.000
## 1st Qu.: 1.000 1st Qu.: 0.000 1st Qu.:0.0000 1st Qu.: 0.000
## Median : 6.000 Median : 1.000 Median :1.0000 Median : 6.000
## Mean : 6.313 Mean : 1.949 Mean :0.8892 Mean : 5.399
## 3rd Qu.:10.000 3rd Qu.: 3.000 3rd Qu.:1.0000 3rd Qu.:10.000
## Max. :27.000 Max. :16.000 Max. :6.0000 Max. :27.000
## NumHydrogen NumCarbon NumNitrogen NumOxygen
## Min. : 0.0 Min. : 1.000 Min. :0.0000 Min. :0.000
## 1st Qu.: 7.0 1st Qu.: 6.000 1st Qu.:0.0000 1st Qu.:0.000
## Median :11.0 Median : 8.000 Median :0.0000 Median :1.000
## Mean :11.9 Mean : 9.785 Mean :0.7089 Mean :1.389
## 3rd Qu.:15.0 3rd Qu.:12.000 3rd Qu.:1.0000 3rd Qu.:2.000
## Max. :40.0 Max. :24.000 Max. :6.0000 Max. :9.000
## NumSulfer NumChlorine NumHalogen NumRings
## Min. :0.0000 Min. :0.000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:1.000
## Median :0.0000 Median :0.000 Median :0.0000 Median :1.000
## Mean :0.1013 Mean :0.557 Mean :0.7089 Mean :1.399
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.0000 3rd Qu.:2.000
## Max. :3.0000 Max. :9.000 Max. :9.0000 Max. :6.000
## HydrophilicFactor SurfaceArea1 SurfaceArea2 Log_Solubility_Class
## Min. :-0.9860 Min. : 0.00 Min. : 0.00 Low :143
## 1st Qu.:-0.7670 1st Qu.: 9.23 1st Qu.: 9.23 High:173
## Median :-0.3970 Median : 26.30 Median : 26.30
## Mean :-0.1022 Mean : 32.76 Mean : 35.04
## 3rd Qu.: 0.2140 3rd Qu.: 49.55 3rd Qu.: 52.32
## Max. : 5.0000 Max. :201.85 Max. :201.85##################################
# Formulating a data type assessment summary
##################################
PDA <- Solubility_Train
(PDA.Summary <- data.frame(
Column.Index=c(1:length(names(PDA))),
Column.Name= names(PDA),
Column.Type=sapply(PDA, function(x) class(x)),
row.names=NULL)
)## Column.Index Column.Name Column.Type
## 1 1 FP001 integer
## 2 2 FP002 integer
## 3 3 FP003 integer
## 4 4 FP004 integer
## 5 5 FP005 integer
## 6 6 FP006 integer
## 7 7 FP007 integer
## 8 8 FP008 integer
## 9 9 FP009 integer
## 10 10 FP010 integer
## 11 11 FP011 integer
## 12 12 FP012 integer
## 13 13 FP013 integer
## 14 14 FP014 integer
## 15 15 FP015 integer
## 16 16 FP016 integer
## 17 17 FP017 integer
## 18 18 FP018 integer
## 19 19 FP019 integer
## 20 20 FP020 integer
## 21 21 FP021 integer
## 22 22 FP022 integer
## 23 23 FP023 integer
## 24 24 FP024 integer
## 25 25 FP025 integer
## 26 26 FP026 integer
## 27 27 FP027 integer
## 28 28 FP028 integer
## 29 29 FP029 integer
## 30 30 FP030 integer
## 31 31 FP031 integer
## 32 32 FP032 integer
## 33 33 FP033 integer
## 34 34 FP034 integer
## 35 35 FP035 integer
## 36 36 FP036 integer
## 37 37 FP037 integer
## 38 38 FP038 integer
## 39 39 FP039 integer
## 40 40 FP040 integer
## 41 41 FP041 integer
## 42 42 FP042 integer
## 43 43 FP043 integer
## 44 44 FP044 integer
## 45 45 FP045 integer
## 46 46 FP046 integer
## 47 47 FP047 integer
## 48 48 FP048 integer
## 49 49 FP049 integer
## 50 50 FP050 integer
## 51 51 FP051 integer
## 52 52 FP052 integer
## 53 53 FP053 integer
## 54 54 FP054 integer
## 55 55 FP055 integer
## 56 56 FP056 integer
## 57 57 FP057 integer
## 58 58 FP058 integer
## 59 59 FP059 integer
## 60 60 FP060 integer
## 61 61 FP061 integer
## 62 62 FP062 integer
## 63 63 FP063 integer
## 64 64 FP064 integer
## 65 65 FP065 integer
## 66 66 FP066 integer
## 67 67 FP067 integer
## 68 68 FP068 integer
## 69 69 FP069 integer
## 70 70 FP070 integer
## 71 71 FP071 integer
## 72 72 FP072 integer
## 73 73 FP073 integer
## 74 74 FP074 integer
## 75 75 FP075 integer
## 76 76 FP076 integer
## 77 77 FP077 integer
## 78 78 FP078 integer
## 79 79 FP079 integer
## 80 80 FP080 integer
## 81 81 FP081 integer
## 82 82 FP082 integer
## 83 83 FP083 integer
## 84 84 FP084 integer
## 85 85 FP085 integer
## 86 86 FP086 integer
## 87 87 FP087 integer
## 88 88 FP088 integer
## 89 89 FP089 integer
## 90 90 FP090 integer
## 91 91 FP091 integer
## 92 92 FP092 integer
## 93 93 FP093 integer
## 94 94 FP094 integer
## 95 95 FP095 integer
## 96 96 FP096 integer
## 97 97 FP097 integer
## 98 98 FP098 integer
## 99 99 FP099 integer
## 100 100 FP100 integer
## 101 101 FP101 integer
## 102 102 FP102 integer
## 103 103 FP103 integer
## 104 104 FP104 integer
## 105 105 FP105 integer
## 106 106 FP106 integer
## 107 107 FP107 integer
## 108 108 FP108 integer
## 109 109 FP109 integer
## 110 110 FP110 integer
## 111 111 FP111 integer
## 112 112 FP112 integer
## 113 113 FP113 integer
## 114 114 FP114 integer
## 115 115 FP115 integer
## 116 116 FP116 integer
## 117 117 FP117 integer
## 118 118 FP118 integer
## 119 119 FP119 integer
## 120 120 FP120 integer
## 121 121 FP121 integer
## 122 122 FP122 integer
## 123 123 FP123 integer
## 124 124 FP124 integer
## 125 125 FP125 integer
## 126 126 FP126 integer
## 127 127 FP127 integer
## 128 128 FP128 integer
## 129 129 FP129 integer
## 130 130 FP130 integer
## 131 131 FP131 integer
## 132 132 FP132 integer
## 133 133 FP133 integer
## 134 134 FP134 integer
## 135 135 FP135 integer
## 136 136 FP136 integer
## 137 137 FP137 integer
## 138 138 FP138 integer
## 139 139 FP139 integer
## 140 140 FP140 integer
## 141 141 FP141 integer
## 142 142 FP142 integer
## 143 143 FP143 integer
## 144 144 FP144 integer
## 145 145 FP145 integer
## 146 146 FP146 integer
## 147 147 FP147 integer
## 148 148 FP148 integer
## 149 149 FP149 integer
## 150 150 FP150 integer
## 151 151 FP151 integer
## 152 152 FP152 integer
## 153 153 FP153 integer
## 154 154 FP154 integer
## 155 155 FP155 integer
## 156 156 FP156 integer
## 157 157 FP157 integer
## 158 158 FP158 integer
## 159 159 FP159 integer
## 160 160 FP160 integer
## 161 161 FP161 integer
## 162 162 FP162 integer
## 163 163 FP163 integer
## 164 164 FP164 integer
## 165 165 FP165 integer
## 166 166 FP166 integer
## 167 167 FP167 integer
## 168 168 FP168 integer
## 169 169 FP169 integer
## 170 170 FP170 integer
## 171 171 FP171 integer
## 172 172 FP172 integer
## 173 173 FP173 integer
## 174 174 FP174 integer
## 175 175 FP175 integer
## 176 176 FP176 integer
## 177 177 FP177 integer
## 178 178 FP178 integer
## 179 179 FP179 integer
## 180 180 FP180 integer
## 181 181 FP181 integer
## 182 182 FP182 integer
## 183 183 FP183 integer
## 184 184 FP184 integer
## 185 185 FP185 integer
## 186 186 FP186 integer
## 187 187 FP187 integer
## 188 188 FP188 integer
## 189 189 FP189 integer
## 190 190 FP190 integer
## 191 191 FP191 integer
## 192 192 FP192 integer
## 193 193 FP193 integer
## 194 194 FP194 integer
## 195 195 FP195 integer
## 196 196 FP196 integer
## 197 197 FP197 integer
## 198 198 FP198 integer
## 199 199 FP199 integer
## 200 200 FP200 integer
## 201 201 FP201 integer
## 202 202 FP202 integer
## 203 203 FP203 integer
## 204 204 FP204 integer
## 205 205 FP205 integer
## 206 206 FP206 integer
## 207 207 FP207 integer
## 208 208 FP208 integer
## 209 209 MolWeight numeric
## 210 210 NumAtoms integer
## 211 211 NumNonHAtoms integer
## 212 212 NumBonds integer
## 213 213 NumNonHBonds integer
## 214 214 NumMultBonds integer
## 215 215 NumRotBonds integer
## 216 216 NumDblBonds integer
## 217 217 NumAromaticBonds integer
## 218 218 NumHydrogen integer
## 219 219 NumCarbon integer
## 220 220 NumNitrogen integer
## 221 221 NumOxygen integer
## 222 222 NumSulfer integer
## 223 223 NumChlorine integer
## 224 224 NumHalogen integer
## 225 225 NumRings integer
## 226 226 HydrophilicFactor numeric
## 227 227 SurfaceArea1 numeric
## 228 228 SurfaceArea2 numeric
## 229 229 Log_Solubility_Class factor1.2 Data Quality Assessment
[A] No missing observations noted for any variable.
[B] Low variance observed for 127 variables with First.Second.Mode.Ratio>5.
[B.1]-[B.33] FP013 to FP045 variables (factor)
[B.34]-[B.45] FP048 to FP059 variables (factor)
[B.46] FP114 variable (factor)
[B.47]-[B.50] FP119 to FP122 variable (factor)
[B.51]-[B.88] FP124 to FP161 variables (factor)
[B.89]-[B.118] FP172 to FP201 variables (factor)
[B.119]-[B.124] FP203 to FP208 variables (factor)
[B.125] NumSulfer variable (numeric)
[B.126] NumChlorine variable (numeric)
[B.127] NumHalogen variable (numeric)
[C] Low variance observed for 4 variables with Unique.Count.Ratio<0.01.
[C.1] NumDblBonds variable (numeric)
[C.2] NumNitrogen variable (numeric)
[C.3] NumSulfer variable (numeric)
[C.4] NumRings variable (numeric)
[D] High skewness observed for 3 variables with Skewness>3 or Skewness<(-3).
[D.1] NumSulfer variable (numeric)
[D.2] NumChlorine variable (numeric)
[D.3] HydrophilicFactor variable (numeric)
Code Chunk | Output
##################################
# Loading dataset
##################################
DQA <- Solubility_Train
##################################
# Formulating an overall data quality assessment summary
##################################
(DQA.Summary <- data.frame(
Column.Index=c(1:length(names(DQA))),
Column.Name= names(DQA),
Column.Type=sapply(DQA, function(x) class(x)),
Row.Count=sapply(DQA, function(x) nrow(DQA)),
NA.Count=sapply(DQA,function(x)sum(is.na(x))),
Fill.Rate=sapply(DQA,function(x)format(round((sum(!is.na(x))/nrow(DQA)),3),nsmall=3)),
row.names=NULL)
)## Column.Index Column.Name Column.Type Row.Count NA.Count Fill.Rate
## 1 1 FP001 integer 951 0 1.000
## 2 2 FP002 integer 951 0 1.000
## 3 3 FP003 integer 951 0 1.000
## 4 4 FP004 integer 951 0 1.000
## 5 5 FP005 integer 951 0 1.000
## 6 6 FP006 integer 951 0 1.000
## 7 7 FP007 integer 951 0 1.000
## 8 8 FP008 integer 951 0 1.000
## 9 9 FP009 integer 951 0 1.000
## 10 10 FP010 integer 951 0 1.000
## 11 11 FP011 integer 951 0 1.000
## 12 12 FP012 integer 951 0 1.000
## 13 13 FP013 integer 951 0 1.000
## 14 14 FP014 integer 951 0 1.000
## 15 15 FP015 integer 951 0 1.000
## 16 16 FP016 integer 951 0 1.000
## 17 17 FP017 integer 951 0 1.000
## 18 18 FP018 integer 951 0 1.000
## 19 19 FP019 integer 951 0 1.000
## 20 20 FP020 integer 951 0 1.000
## 21 21 FP021 integer 951 0 1.000
## 22 22 FP022 integer 951 0 1.000
## 23 23 FP023 integer 951 0 1.000
## 24 24 FP024 integer 951 0 1.000
## 25 25 FP025 integer 951 0 1.000
## 26 26 FP026 integer 951 0 1.000
## 27 27 FP027 integer 951 0 1.000
## 28 28 FP028 integer 951 0 1.000
## 29 29 FP029 integer 951 0 1.000
## 30 30 FP030 integer 951 0 1.000
## 31 31 FP031 integer 951 0 1.000
## 32 32 FP032 integer 951 0 1.000
## 33 33 FP033 integer 951 0 1.000
## 34 34 FP034 integer 951 0 1.000
## 35 35 FP035 integer 951 0 1.000
## 36 36 FP036 integer 951 0 1.000
## 37 37 FP037 integer 951 0 1.000
## 38 38 FP038 integer 951 0 1.000
## 39 39 FP039 integer 951 0 1.000
## 40 40 FP040 integer 951 0 1.000
## 41 41 FP041 integer 951 0 1.000
## 42 42 FP042 integer 951 0 1.000
## 43 43 FP043 integer 951 0 1.000
## 44 44 FP044 integer 951 0 1.000
## 45 45 FP045 integer 951 0 1.000
## 46 46 FP046 integer 951 0 1.000
## 47 47 FP047 integer 951 0 1.000
## 48 48 FP048 integer 951 0 1.000
## 49 49 FP049 integer 951 0 1.000
## 50 50 FP050 integer 951 0 1.000
## 51 51 FP051 integer 951 0 1.000
## 52 52 FP052 integer 951 0 1.000
## 53 53 FP053 integer 951 0 1.000
## 54 54 FP054 integer 951 0 1.000
## 55 55 FP055 integer 951 0 1.000
## 56 56 FP056 integer 951 0 1.000
## 57 57 FP057 integer 951 0 1.000
## 58 58 FP058 integer 951 0 1.000
## 59 59 FP059 integer 951 0 1.000
## 60 60 FP060 integer 951 0 1.000
## 61 61 FP061 integer 951 0 1.000
## 62 62 FP062 integer 951 0 1.000
## 63 63 FP063 integer 951 0 1.000
## 64 64 FP064 integer 951 0 1.000
## 65 65 FP065 integer 951 0 1.000
## 66 66 FP066 integer 951 0 1.000
## 67 67 FP067 integer 951 0 1.000
## 68 68 FP068 integer 951 0 1.000
## 69 69 FP069 integer 951 0 1.000
## 70 70 FP070 integer 951 0 1.000
## 71 71 FP071 integer 951 0 1.000
## 72 72 FP072 integer 951 0 1.000
## 73 73 FP073 integer 951 0 1.000
## 74 74 FP074 integer 951 0 1.000
## 75 75 FP075 integer 951 0 1.000
## 76 76 FP076 integer 951 0 1.000
## 77 77 FP077 integer 951 0 1.000
## 78 78 FP078 integer 951 0 1.000
## 79 79 FP079 integer 951 0 1.000
## 80 80 FP080 integer 951 0 1.000
## 81 81 FP081 integer 951 0 1.000
## 82 82 FP082 integer 951 0 1.000
## 83 83 FP083 integer 951 0 1.000
## 84 84 FP084 integer 951 0 1.000
## 85 85 FP085 integer 951 0 1.000
## 86 86 FP086 integer 951 0 1.000
## 87 87 FP087 integer 951 0 1.000
## 88 88 FP088 integer 951 0 1.000
## 89 89 FP089 integer 951 0 1.000
## 90 90 FP090 integer 951 0 1.000
## 91 91 FP091 integer 951 0 1.000
## 92 92 FP092 integer 951 0 1.000
## 93 93 FP093 integer 951 0 1.000
## 94 94 FP094 integer 951 0 1.000
## 95 95 FP095 integer 951 0 1.000
## 96 96 FP096 integer 951 0 1.000
## 97 97 FP097 integer 951 0 1.000
## 98 98 FP098 integer 951 0 1.000
## 99 99 FP099 integer 951 0 1.000
## 100 100 FP100 integer 951 0 1.000
## 101 101 FP101 integer 951 0 1.000
## 102 102 FP102 integer 951 0 1.000
## 103 103 FP103 integer 951 0 1.000
## 104 104 FP104 integer 951 0 1.000
## 105 105 FP105 integer 951 0 1.000
## 106 106 FP106 integer 951 0 1.000
## 107 107 FP107 integer 951 0 1.000
## 108 108 FP108 integer 951 0 1.000
## 109 109 FP109 integer 951 0 1.000
## 110 110 FP110 integer 951 0 1.000
## 111 111 FP111 integer 951 0 1.000
## 112 112 FP112 integer 951 0 1.000
## 113 113 FP113 integer 951 0 1.000
## 114 114 FP114 integer 951 0 1.000
## 115 115 FP115 integer 951 0 1.000
## 116 116 FP116 integer 951 0 1.000
## 117 117 FP117 integer 951 0 1.000
## 118 118 FP118 integer 951 0 1.000
## 119 119 FP119 integer 951 0 1.000
## 120 120 FP120 integer 951 0 1.000
## 121 121 FP121 integer 951 0 1.000
## 122 122 FP122 integer 951 0 1.000
## 123 123 FP123 integer 951 0 1.000
## 124 124 FP124 integer 951 0 1.000
## 125 125 FP125 integer 951 0 1.000
## 126 126 FP126 integer 951 0 1.000
## 127 127 FP127 integer 951 0 1.000
## 128 128 FP128 integer 951 0 1.000
## 129 129 FP129 integer 951 0 1.000
## 130 130 FP130 integer 951 0 1.000
## 131 131 FP131 integer 951 0 1.000
## 132 132 FP132 integer 951 0 1.000
## 133 133 FP133 integer 951 0 1.000
## 134 134 FP134 integer 951 0 1.000
## 135 135 FP135 integer 951 0 1.000
## 136 136 FP136 integer 951 0 1.000
## 137 137 FP137 integer 951 0 1.000
## 138 138 FP138 integer 951 0 1.000
## 139 139 FP139 integer 951 0 1.000
## 140 140 FP140 integer 951 0 1.000
## 141 141 FP141 integer 951 0 1.000
## 142 142 FP142 integer 951 0 1.000
## 143 143 FP143 integer 951 0 1.000
## 144 144 FP144 integer 951 0 1.000
## 145 145 FP145 integer 951 0 1.000
## 146 146 FP146 integer 951 0 1.000
## 147 147 FP147 integer 951 0 1.000
## 148 148 FP148 integer 951 0 1.000
## 149 149 FP149 integer 951 0 1.000
## 150 150 FP150 integer 951 0 1.000
## 151 151 FP151 integer 951 0 1.000
## 152 152 FP152 integer 951 0 1.000
## 153 153 FP153 integer 951 0 1.000
## 154 154 FP154 integer 951 0 1.000
## 155 155 FP155 integer 951 0 1.000
## 156 156 FP156 integer 951 0 1.000
## 157 157 FP157 integer 951 0 1.000
## 158 158 FP158 integer 951 0 1.000
## 159 159 FP159 integer 951 0 1.000
## 160 160 FP160 integer 951 0 1.000
## 161 161 FP161 integer 951 0 1.000
## 162 162 FP162 integer 951 0 1.000
## 163 163 FP163 integer 951 0 1.000
## 164 164 FP164 integer 951 0 1.000
## 165 165 FP165 integer 951 0 1.000
## 166 166 FP166 integer 951 0 1.000
## 167 167 FP167 integer 951 0 1.000
## 168 168 FP168 integer 951 0 1.000
## 169 169 FP169 integer 951 0 1.000
## 170 170 FP170 integer 951 0 1.000
## 171 171 FP171 integer 951 0 1.000
## 172 172 FP172 integer 951 0 1.000
## 173 173 FP173 integer 951 0 1.000
## 174 174 FP174 integer 951 0 1.000
## 175 175 FP175 integer 951 0 1.000
## 176 176 FP176 integer 951 0 1.000
## 177 177 FP177 integer 951 0 1.000
## 178 178 FP178 integer 951 0 1.000
## 179 179 FP179 integer 951 0 1.000
## 180 180 FP180 integer 951 0 1.000
## 181 181 FP181 integer 951 0 1.000
## 182 182 FP182 integer 951 0 1.000
## 183 183 FP183 integer 951 0 1.000
## 184 184 FP184 integer 951 0 1.000
## 185 185 FP185 integer 951 0 1.000
## 186 186 FP186 integer 951 0 1.000
## 187 187 FP187 integer 951 0 1.000
## 188 188 FP188 integer 951 0 1.000
## 189 189 FP189 integer 951 0 1.000
## 190 190 FP190 integer 951 0 1.000
## 191 191 FP191 integer 951 0 1.000
## 192 192 FP192 integer 951 0 1.000
## 193 193 FP193 integer 951 0 1.000
## 194 194 FP194 integer 951 0 1.000
## 195 195 FP195 integer 951 0 1.000
## 196 196 FP196 integer 951 0 1.000
## 197 197 FP197 integer 951 0 1.000
## 198 198 FP198 integer 951 0 1.000
## 199 199 FP199 integer 951 0 1.000
## 200 200 FP200 integer 951 0 1.000
## 201 201 FP201 integer 951 0 1.000
## 202 202 FP202 integer 951 0 1.000
## 203 203 FP203 integer 951 0 1.000
## 204 204 FP204 integer 951 0 1.000
## 205 205 FP205 integer 951 0 1.000
## 206 206 FP206 integer 951 0 1.000
## 207 207 FP207 integer 951 0 1.000
## 208 208 FP208 integer 951 0 1.000
## 209 209 MolWeight numeric 951 0 1.000
## 210 210 NumAtoms integer 951 0 1.000
## 211 211 NumNonHAtoms integer 951 0 1.000
## 212 212 NumBonds integer 951 0 1.000
## 213 213 NumNonHBonds integer 951 0 1.000
## 214 214 NumMultBonds integer 951 0 1.000
## 215 215 NumRotBonds integer 951 0 1.000
## 216 216 NumDblBonds integer 951 0 1.000
## 217 217 NumAromaticBonds integer 951 0 1.000
## 218 218 NumHydrogen integer 951 0 1.000
## 219 219 NumCarbon integer 951 0 1.000
## 220 220 NumNitrogen integer 951 0 1.000
## 221 221 NumOxygen integer 951 0 1.000
## 222 222 NumSulfer integer 951 0 1.000
## 223 223 NumChlorine integer 951 0 1.000
## 224 224 NumHalogen integer 951 0 1.000
## 225 225 NumRings integer 951 0 1.000
## 226 226 HydrophilicFactor numeric 951 0 1.000
## 227 227 SurfaceArea1 numeric 951 0 1.000
## 228 228 SurfaceArea2 numeric 951 0 1.000
## 229 229 Log_Solubility_Class factor 951 0 1.000##################################
# Listing all predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DQA.Predictors.Numeric <- DQA.Predictors[,-(grep("FP", names(DQA.Predictors)))]
if (length(names(DQA.Predictors.Numeric))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Numeric))),
" numeric predictor variable(s)."))
} else {
print("There are no numeric predictor variables.")
}## [1] "There are 20 numeric predictor variable(s)."##################################
# Listing all factor predictors
##################################
DQA.Predictors.Factor <-as.data.frame(lapply(DQA.Predictors[(grep("FP", names(DQA.Predictors)))],factor))
if (length(names(DQA.Predictors.Factor))>0) {
print(paste0("There are ",
(length(names(DQA.Predictors.Factor))),
" factor predictor variable(s)."))
} else {
print("There are no factor predictor variables.")
}## [1] "There are 208 factor predictor variable(s)."##################################
# Formulating a data quality assessment summary for factor predictors
##################################
if (length(names(DQA.Predictors.Factor))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = x[!(x %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return("x"),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Factor.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Factor),
Column.Type=sapply(DQA.Predictors.Factor, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Factor, function(x) length(unique(x))),
First.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(FirstModes(x)[1])),
Second.Mode.Value=sapply(DQA.Predictors.Factor, function(x) as.character(SecondModes(x)[1])),
First.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Factor, function(x) sum(na.omit(x) == SecondModes(x)[1])),
Unique.Count.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Factor)),3), nsmall=3)),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Factor, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
row.names=NULL)
)
} ## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 1 FP001 factor 2 0 1
## 2 FP002 factor 2 1 0
## 3 FP003 factor 2 0 1
## 4 FP004 factor 2 1 0
## 5 FP005 factor 2 1 0
## 6 FP006 factor 2 0 1
## 7 FP007 factor 2 0 1
## 8 FP008 factor 2 0 1
## 9 FP009 factor 2 0 1
## 10 FP010 factor 2 0 1
## 11 FP011 factor 2 0 1
## 12 FP012 factor 2 0 1
## 13 FP013 factor 2 0 1
## 14 FP014 factor 2 0 1
## 15 FP015 factor 2 1 0
## 16 FP016 factor 2 0 1
## 17 FP017 factor 2 0 1
## 18 FP018 factor 2 0 1
## 19 FP019 factor 2 0 1
## 20 FP020 factor 2 0 1
## 21 FP021 factor 2 0 1
## 22 FP022 factor 2 0 1
## 23 FP023 factor 2 0 1
## 24 FP024 factor 2 0 1
## 25 FP025 factor 2 0 1
## 26 FP026 factor 2 0 1
## 27 FP027 factor 2 0 1
## 28 FP028 factor 2 0 1
## 29 FP029 factor 2 0 1
## 30 FP030 factor 2 0 1
## 31 FP031 factor 2 0 1
## 32 FP032 factor 2 0 1
## 33 FP033 factor 2 0 1
## 34 FP034 factor 2 0 1
## 35 FP035 factor 2 0 1
## 36 FP036 factor 2 0 1
## 37 FP037 factor 2 0 1
## 38 FP038 factor 2 0 1
## 39 FP039 factor 2 0 1
## 40 FP040 factor 2 0 1
## 41 FP041 factor 2 0 1
## 42 FP042 factor 2 0 1
## 43 FP043 factor 2 0 1
## 44 FP044 factor 2 0 1
## 45 FP045 factor 2 0 1
## 46 FP046 factor 2 0 1
## 47 FP047 factor 2 0 1
## 48 FP048 factor 2 0 1
## 49 FP049 factor 2 0 1
## 50 FP050 factor 2 0 1
## 51 FP051 factor 2 0 1
## 52 FP052 factor 2 0 1
## 53 FP053 factor 2 0 1
## 54 FP054 factor 2 0 1
## 55 FP055 factor 2 0 1
## 56 FP056 factor 2 0 1
## 57 FP057 factor 2 0 1
## 58 FP058 factor 2 0 1
## 59 FP059 factor 2 0 1
## 60 FP060 factor 2 0 1
## 61 FP061 factor 2 0 1
## 62 FP062 factor 2 0 1
## 63 FP063 factor 2 0 1
## 64 FP064 factor 2 0 1
## 65 FP065 factor 2 1 0
## 66 FP066 factor 2 1 0
## 67 FP067 factor 2 0 1
## 68 FP068 factor 2 0 1
## 69 FP069 factor 2 0 1
## 70 FP070 factor 2 0 1
## 71 FP071 factor 2 0 1
## 72 FP072 factor 2 1 0
## 73 FP073 factor 2 0 1
## 74 FP074 factor 2 0 1
## 75 FP075 factor 2 0 1
## 76 FP076 factor 2 0 1
## 77 FP077 factor 2 0 1
## 78 FP078 factor 2 0 1
## 79 FP079 factor 2 1 0
## 80 FP080 factor 2 0 1
## 81 FP081 factor 2 0 1
## 82 FP082 factor 2 1 0
## 83 FP083 factor 2 0 1
## 84 FP084 factor 2 0 1
## 85 FP085 factor 2 0 1
## 86 FP086 factor 2 0 1
## 87 FP087 factor 2 1 0
## 88 FP088 factor 2 0 1
## 89 FP089 factor 2 0 1
## 90 FP090 factor 2 0 1
## 91 FP091 factor 2 0 1
## 92 FP092 factor 2 0 1
## 93 FP093 factor 2 0 1
## 94 FP094 factor 2 0 1
## 95 FP095 factor 2 0 1
## 96 FP096 factor 2 0 1
## 97 FP097 factor 2 0 1
## 98 FP098 factor 2 0 1
## 99 FP099 factor 2 0 1
## 100 FP100 factor 2 0 1
## 101 FP101 factor 2 0 1
## 102 FP102 factor 2 0 1
## 103 FP103 factor 2 0 1
## 104 FP104 factor 2 0 1
## 105 FP105 factor 2 0 1
## 106 FP106 factor 2 0 1
## 107 FP107 factor 2 0 1
## 108 FP108 factor 2 0 1
## 109 FP109 factor 2 0 1
## 110 FP110 factor 2 0 1
## 111 FP111 factor 2 0 1
## 112 FP112 factor 2 0 1
## 113 FP113 factor 2 0 1
## 114 FP114 factor 2 0 1
## 115 FP115 factor 2 0 1
## 116 FP116 factor 2 0 1
## 117 FP117 factor 2 0 1
## 118 FP118 factor 2 0 1
## 119 FP119 factor 2 0 1
## 120 FP120 factor 2 0 1
## 121 FP121 factor 2 0 1
## 122 FP122 factor 2 0 1
## 123 FP123 factor 2 0 1
## 124 FP124 factor 2 0 1
## 125 FP125 factor 2 0 1
## 126 FP126 factor 2 0 1
## 127 FP127 factor 2 0 1
## 128 FP128 factor 2 0 1
## 129 FP129 factor 2 0 1
## 130 FP130 factor 2 0 1
## 131 FP131 factor 2 0 1
## 132 FP132 factor 2 0 1
## 133 FP133 factor 2 0 1
## 134 FP134 factor 2 0 1
## 135 FP135 factor 2 0 1
## 136 FP136 factor 2 0 1
## 137 FP137 factor 2 0 1
## 138 FP138 factor 2 0 1
## 139 FP139 factor 2 0 1
## 140 FP140 factor 2 0 1
## 141 FP141 factor 2 0 1
## 142 FP142 factor 2 0 1
## 143 FP143 factor 2 0 1
## 144 FP144 factor 2 0 1
## 145 FP145 factor 2 0 1
## 146 FP146 factor 2 0 1
## 147 FP147 factor 2 0 1
## 148 FP148 factor 2 0 1
## 149 FP149 factor 2 0 1
## 150 FP150 factor 2 0 1
## 151 FP151 factor 2 0 1
## 152 FP152 factor 2 0 1
## 153 FP153 factor 2 0 1
## 154 FP154 factor 2 0 1
## 155 FP155 factor 2 0 1
## 156 FP156 factor 2 0 1
## 157 FP157 factor 2 0 1
## 158 FP158 factor 2 0 1
## 159 FP159 factor 2 0 1
## 160 FP160 factor 2 0 1
## 161 FP161 factor 2 0 1
## 162 FP162 factor 2 0 1
## 163 FP163 factor 2 0 1
## 164 FP164 factor 2 1 0
## 165 FP165 factor 2 0 1
## 166 FP166 factor 2 0 1
## 167 FP167 factor 2 0 1
## 168 FP168 factor 2 1 0
## 169 FP169 factor 2 0 1
## 170 FP170 factor 2 0 1
## 171 FP171 factor 2 0 1
## 172 FP172 factor 2 0 1
## 173 FP173 factor 2 0 1
## 174 FP174 factor 2 0 1
## 175 FP175 factor 2 0 1
## 176 FP176 factor 2 0 1
## 177 FP177 factor 2 0 1
## 178 FP178 factor 2 0 1
## 179 FP179 factor 2 0 1
## 180 FP180 factor 2 0 1
## 181 FP181 factor 2 0 1
## 182 FP182 factor 2 0 1
## 183 FP183 factor 2 0 1
## 184 FP184 factor 2 0 1
## 185 FP185 factor 2 0 1
## 186 FP186 factor 2 0 1
## 187 FP187 factor 2 0 1
## 188 FP188 factor 2 0 1
## 189 FP189 factor 2 0 1
## 190 FP190 factor 2 0 1
## 191 FP191 factor 2 0 1
## 192 FP192 factor 2 0 1
## 193 FP193 factor 2 0 1
## 194 FP194 factor 2 0 1
## 195 FP195 factor 2 0 1
## 196 FP196 factor 2 0 1
## 197 FP197 factor 2 0 1
## 198 FP198 factor 2 0 1
## 199 FP199 factor 2 0 1
## 200 FP200 factor 2 0 1
## 201 FP201 factor 2 0 1
## 202 FP202 factor 2 0 1
## 203 FP203 factor 2 0 1
## 204 FP204 factor 2 0 1
## 205 FP205 factor 2 0 1
## 206 FP206 factor 2 0 1
## 207 FP207 factor 2 0 1
## 208 FP208 factor 2 0 1
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio
## 1 482 469 0.002
## 2 513 438 0.002
## 3 536 415 0.002
## 4 556 395 0.002
## 5 551 400 0.002
## 6 570 381 0.002
## 7 605 346 0.002
## 8 641 310 0.002
## 9 685 266 0.002
## 10 781 170 0.002
## 11 747 204 0.002
## 12 783 168 0.002
## 13 793 158 0.002
## 14 798 153 0.002
## 15 818 133 0.002
## 16 812 139 0.002
## 17 814 137 0.002
## 18 826 125 0.002
## 19 835 116 0.002
## 20 837 114 0.002
## 21 836 115 0.002
## 22 852 99 0.002
## 23 834 117 0.002
## 24 844 107 0.002
## 25 841 110 0.002
## 26 871 80 0.002
## 27 858 93 0.002
## 28 850 101 0.002
## 29 854 97 0.002
## 30 862 89 0.002
## 31 866 85 0.002
## 32 881 70 0.002
## 33 885 66 0.002
## 34 875 76 0.002
## 35 882 69 0.002
## 36 879 72 0.002
## 37 884 67 0.002
## 38 869 82 0.002
## 39 880 71 0.002
## 40 886 65 0.002
## 41 891 60 0.002
## 42 897 54 0.002
## 43 888 63 0.002
## 44 894 57 0.002
## 45 898 53 0.002
## 46 651 300 0.002
## 47 698 253 0.002
## 48 833 118 0.002
## 49 835 116 0.002
## 50 844 107 0.002
## 51 847 104 0.002
## 52 864 87 0.002
## 53 862 89 0.002
## 54 879 72 0.002
## 55 900 51 0.002
## 56 889 62 0.002
## 57 837 114 0.002
## 58 843 108 0.002
## 59 899 52 0.002
## 60 493 458 0.002
## 61 526 425 0.002
## 62 535 416 0.002
## 63 546 405 0.002
## 64 555 396 0.002
## 65 564 387 0.002
## 66 580 371 0.002
## 67 590 361 0.002
## 68 607 344 0.002
## 69 607 344 0.002
## 70 613 338 0.002
## 71 640 311 0.002
## 72 626 325 0.002
## 73 656 295 0.002
## 74 642 309 0.002
## 75 629 322 0.002
## 76 639 312 0.002
## 77 646 305 0.002
## 78 662 289 0.002
## 79 656 295 0.002
## 80 663 288 0.002
## 81 686 265 0.002
## 82 679 272 0.002
## 83 691 260 0.002
## 84 679 272 0.002
## 85 708 243 0.002
## 86 695 256 0.002
## 87 691 260 0.002
## 88 701 250 0.002
## 89 716 235 0.002
## 90 714 237 0.002
## 91 737 214 0.002
## 92 719 232 0.002
## 93 719 232 0.002
## 94 731 220 0.002
## 95 742 209 0.002
## 96 744 207 0.002
## 97 727 224 0.002
## 98 725 226 0.002
## 99 735 216 0.002
## 100 731 220 0.002
## 101 726 225 0.002
## 102 759 192 0.002
## 103 743 208 0.002
## 104 739 212 0.002
## 105 746 205 0.002
## 106 769 182 0.002
## 107 750 201 0.002
## 108 756 195 0.002
## 109 783 168 0.002
## 110 755 196 0.002
## 111 764 187 0.002
## 112 766 185 0.002
## 113 765 186 0.002
## 114 803 148 0.002
## 115 781 170 0.002
## 116 768 183 0.002
## 117 781 170 0.002
## 118 768 183 0.002
## 119 796 155 0.002
## 120 793 158 0.002
## 121 818 133 0.002
## 122 795 156 0.002
## 123 792 159 0.002
## 124 797 154 0.002
## 125 803 148 0.002
## 126 810 141 0.002
## 127 818 133 0.002
## 128 810 141 0.002
## 129 819 132 0.002
## 130 851 100 0.002
## 131 831 120 0.002
## 132 832 119 0.002
## 133 831 120 0.002
## 134 830 121 0.002
## 135 831 120 0.002
## 136 836 115 0.002
## 137 841 110 0.002
## 138 845 106 0.002
## 139 873 78 0.002
## 140 845 106 0.002
## 141 840 111 0.002
## 142 847 104 0.002
## 143 874 77 0.002
## 144 852 99 0.002
## 145 852 99 0.002
## 146 853 98 0.002
## 147 851 100 0.002
## 148 868 83 0.002
## 149 865 86 0.002
## 150 876 75 0.002
## 151 898 53 0.002
## 152 873 78 0.002
## 153 877 74 0.002
## 154 915 36 0.002
## 155 885 66 0.002
## 156 884 67 0.002
## 157 892 59 0.002
## 158 900 51 0.002
## 159 884 67 0.002
## 160 886 65 0.002
## 161 888 63 0.002
## 162 480 471 0.002
## 163 498 453 0.002
## 164 597 354 0.002
## 165 619 332 0.002
## 166 636 315 0.002
## 167 639 312 0.002
## 168 633 318 0.002
## 169 774 177 0.002
## 170 776 175 0.002
## 171 790 161 0.002
## 172 807 144 0.002
## 173 816 135 0.002
## 174 827 124 0.002
## 175 823 128 0.002
## 176 835 116 0.002
## 177 836 115 0.002
## 178 836 115 0.002
## 179 858 93 0.002
## 180 849 102 0.002
## 181 862 89 0.002
## 182 857 94 0.002
## 183 879 72 0.002
## 184 871 80 0.002
## 185 870 81 0.002
## 186 878 73 0.002
## 187 882 69 0.002
## 188 886 65 0.002
## 189 878 73 0.002
## 190 882 69 0.002
## 191 884 67 0.002
## 192 893 58 0.002
## 193 892 59 0.002
## 194 895 56 0.002
## 195 893 58 0.002
## 196 897 54 0.002
## 197 901 50 0.002
## 198 897 54 0.002
## 199 906 45 0.002
## 200 904 47 0.002
## 201 901 50 0.002
## 202 706 245 0.002
## 203 842 109 0.002
## 204 857 94 0.002
## 205 877 74 0.002
## 206 894 57 0.002
## 207 897 54 0.002
## 208 844 107 0.002
## First.Second.Mode.Ratio
## 1 1.028
## 2 1.171
## 3 1.292
## 4 1.408
## 5 1.378
## 6 1.496
## 7 1.749
## 8 2.068
## 9 2.575
## 10 4.594
## 11 3.662
## 12 4.661
## 13 5.019
## 14 5.216
## 15 6.150
## 16 5.842
## 17 5.942
## 18 6.608
## 19 7.198
## 20 7.342
## 21 7.270
## 22 8.606
## 23 7.128
## 24 7.888
## 25 7.645
## 26 10.887
## 27 9.226
## 28 8.416
## 29 8.804
## 30 9.685
## 31 10.188
## 32 12.586
## 33 13.409
## 34 11.513
## 35 12.783
## 36 12.208
## 37 13.194
## 38 10.598
## 39 12.394
## 40 13.631
## 41 14.850
## 42 16.611
## 43 14.095
## 44 15.684
## 45 16.943
## 46 2.170
## 47 2.759
## 48 7.059
## 49 7.198
## 50 7.888
## 51 8.144
## 52 9.931
## 53 9.685
## 54 12.208
## 55 17.647
## 56 14.339
## 57 7.342
## 58 7.806
## 59 17.288
## 60 1.076
## 61 1.238
## 62 1.286
## 63 1.348
## 64 1.402
## 65 1.457
## 66 1.563
## 67 1.634
## 68 1.765
## 69 1.765
## 70 1.814
## 71 2.058
## 72 1.926
## 73 2.224
## 74 2.078
## 75 1.953
## 76 2.048
## 77 2.118
## 78 2.291
## 79 2.224
## 80 2.302
## 81 2.589
## 82 2.496
## 83 2.658
## 84 2.496
## 85 2.914
## 86 2.715
## 87 2.658
## 88 2.804
## 89 3.047
## 90 3.013
## 91 3.444
## 92 3.099
## 93 3.099
## 94 3.323
## 95 3.550
## 96 3.594
## 97 3.246
## 98 3.208
## 99 3.403
## 100 3.323
## 101 3.227
## 102 3.953
## 103 3.572
## 104 3.486
## 105 3.639
## 106 4.225
## 107 3.731
## 108 3.877
## 109 4.661
## 110 3.852
## 111 4.086
## 112 4.141
## 113 4.113
## 114 5.426
## 115 4.594
## 116 4.197
## 117 4.594
## 118 4.197
## 119 5.135
## 120 5.019
## 121 6.150
## 122 5.096
## 123 4.981
## 124 5.175
## 125 5.426
## 126 5.745
## 127 6.150
## 128 5.745
## 129 6.205
## 130 8.510
## 131 6.925
## 132 6.992
## 133 6.925
## 134 6.860
## 135 6.925
## 136 7.270
## 137 7.645
## 138 7.972
## 139 11.192
## 140 7.972
## 141 7.568
## 142 8.144
## 143 11.351
## 144 8.606
## 145 8.606
## 146 8.704
## 147 8.510
## 148 10.458
## 149 10.058
## 150 11.680
## 151 16.943
## 152 11.192
## 153 11.851
## 154 25.417
## 155 13.409
## 156 13.194
## 157 15.119
## 158 17.647
## 159 13.194
## 160 13.631
## 161 14.095
## 162 1.019
## 163 1.099
## 164 1.686
## 165 1.864
## 166 2.019
## 167 2.048
## 168 1.991
## 169 4.373
## 170 4.434
## 171 4.907
## 172 5.604
## 173 6.044
## 174 6.669
## 175 6.430
## 176 7.198
## 177 7.270
## 178 7.270
## 179 9.226
## 180 8.324
## 181 9.685
## 182 9.117
## 183 12.208
## 184 10.887
## 185 10.741
## 186 12.027
## 187 12.783
## 188 13.631
## 189 12.027
## 190 12.783
## 191 13.194
## 192 15.397
## 193 15.119
## 194 15.982
## 195 15.397
## 196 16.611
## 197 18.020
## 198 16.611
## 199 20.133
## 200 19.234
## 201 18.020
## 202 2.882
## 203 7.725
## 204 9.117
## 205 11.851
## 206 15.684
## 207 16.611
## 208 7.888##################################
# Formulating a data quality assessment summary for numeric predictors
##################################
if (length(names(DQA.Predictors.Numeric))>0) {
##################################
# Formulating a function to determine the first mode
##################################
FirstModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
ux[tab == max(tab)]
}
##################################
# Formulating a function to determine the second mode
##################################
SecondModes <- function(x) {
ux <- unique(na.omit(x))
tab <- tabulate(match(x, ux))
fm = ux[tab == max(tab)]
sm = na.omit(x)[!(na.omit(x) %in% fm)]
usm <- unique(sm)
tabsm <- tabulate(match(sm, usm))
ifelse(is.na(usm[tabsm == max(tabsm)])==TRUE,
return(0.00001),
return(usm[tabsm == max(tabsm)]))
}
(DQA.Predictors.Numeric.Summary <- data.frame(
Column.Name= names(DQA.Predictors.Numeric),
Column.Type=sapply(DQA.Predictors.Numeric, function(x) class(x)),
Unique.Count=sapply(DQA.Predictors.Numeric, function(x) length(unique(x))),
Unique.Count.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((length(unique(x))/nrow(DQA.Predictors.Numeric)),3), nsmall=3)),
First.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((FirstModes(x)[1]),3),nsmall=3)),
Second.Mode.Value=sapply(DQA.Predictors.Numeric, function(x) format(round((SecondModes(x)[1]),3),nsmall=3)),
First.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == FirstModes(x)[1])),
Second.Mode.Count=sapply(DQA.Predictors.Numeric, function(x) sum(na.omit(x) == SecondModes(x)[1])),
First.Second.Mode.Ratio=sapply(DQA.Predictors.Numeric, function(x) format(round((sum(na.omit(x) == FirstModes(x)[1])/sum(na.omit(x) == SecondModes(x)[1])),3), nsmall=3)),
Minimum=sapply(DQA.Predictors.Numeric, function(x) format(round(min(x,na.rm = TRUE),3), nsmall=3)),
Mean=sapply(DQA.Predictors.Numeric, function(x) format(round(mean(x,na.rm = TRUE),3), nsmall=3)),
Median=sapply(DQA.Predictors.Numeric, function(x) format(round(median(x,na.rm = TRUE),3), nsmall=3)),
Maximum=sapply(DQA.Predictors.Numeric, function(x) format(round(max(x,na.rm = TRUE),3), nsmall=3)),
Skewness=sapply(DQA.Predictors.Numeric, function(x) format(round(skewness(x,na.rm = TRUE),3), nsmall=3)),
Kurtosis=sapply(DQA.Predictors.Numeric, function(x) format(round(kurtosis(x,na.rm = TRUE),3), nsmall=3)),
Percentile25th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.25,na.rm = TRUE),3), nsmall=3)),
Percentile75th=sapply(DQA.Predictors.Numeric, function(x) format(round(quantile(x,probs=0.75,na.rm = TRUE),3), nsmall=3)),
row.names=NULL)
)
}## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 1 MolWeight numeric 646 0.679
## 2 NumAtoms integer 66 0.069
## 3 NumNonHAtoms integer 36 0.038
## 4 NumBonds integer 72 0.076
## 5 NumNonHBonds integer 39 0.041
## 6 NumMultBonds integer 25 0.026
## 7 NumRotBonds integer 15 0.016
## 8 NumDblBonds integer 8 0.008
## 9 NumAromaticBonds integer 16 0.017
## 10 NumHydrogen integer 41 0.043
## 11 NumCarbon integer 28 0.029
## 12 NumNitrogen integer 7 0.007
## 13 NumOxygen integer 11 0.012
## 14 NumSulfer integer 5 0.005
## 15 NumChlorine integer 11 0.012
## 16 NumHalogen integer 11 0.012
## 17 NumRings integer 8 0.008
## 18 HydrophilicFactor numeric 369 0.388
## 19 SurfaceArea1 numeric 252 0.265
## 20 SurfaceArea2 numeric 287 0.302
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 1 102.200 116.230 16 14
## 2 22.000 24.000 73 51
## 3 8.000 11.000 104 73
## 4 23.000 19.000 69 56
## 5 8.000 7.000 82 66
## 6 0.000 7.000 158 122
## 7 0.000 1.000 272 186
## 8 0.000 1.000 427 268
## 9 0.000 6.000 400 302
## 10 12.000 8.000 83 79
## 11 6.000 7.000 105 97
## 12 0.000 1.000 546 191
## 13 0.000 2.000 325 218
## 14 0.000 1.000 830 96
## 15 0.000 1.000 750 81
## 16 0.000 1.000 685 107
## 17 1.000 0.000 323 260
## 18 -0.828 -0.158 21 20
## 19 0.000 20.230 218 76
## 20 0.000 20.230 211 75
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 1 1.143 46.090 201.654 179.230 665.810 0.988 3.945
## 2 1.431 5.000 25.507 22.000 94.000 1.364 5.523
## 3 1.425 2.000 13.161 12.000 47.000 0.993 4.129
## 4 1.232 4.000 25.909 23.000 97.000 1.360 5.408
## 5 1.242 1.000 13.563 12.000 50.000 0.969 3.842
## 6 1.295 0.000 6.148 6.000 25.000 0.670 3.053
## 7 1.462 0.000 2.251 2.000 16.000 1.577 6.437
## 8 1.593 0.000 1.006 1.000 7.000 1.360 4.760
## 9 1.325 0.000 5.121 6.000 25.000 0.796 3.241
## 10 1.051 0.000 12.346 11.000 47.000 1.262 5.261
## 11 1.082 1.000 9.893 9.000 33.000 0.927 3.616
## 12 2.859 0.000 0.813 0.000 6.000 1.554 4.831
## 13 1.491 0.000 1.574 1.000 13.000 1.772 8.494
## 14 8.646 0.000 0.164 0.000 4.000 3.842 21.526
## 15 9.259 0.000 0.556 0.000 10.000 3.178 13.780
## 16 6.402 0.000 0.698 0.000 10.000 2.691 10.808
## 17 1.242 0.000 1.402 1.000 7.000 1.034 3.875
## 18 1.050 -0.985 -0.021 -0.314 13.483 3.404 27.504
## 19 2.868 0.000 36.459 29.100 331.940 1.714 9.714
## 20 2.813 0.000 40.234 33.120 331.940 1.475 7.485
## Percentile25th Percentile75th
## 1 122.605 264.340
## 2 17.000 31.000
## 3 8.000 17.000
## 4 17.000 31.500
## 5 8.000 18.000
## 6 1.000 10.000
## 7 0.000 3.500
## 8 0.000 2.000
## 9 0.000 6.000
## 10 7.000 16.000
## 11 6.000 12.000
## 12 0.000 1.000
## 13 0.000 2.000
## 14 0.000 0.000
## 15 0.000 0.000
## 16 0.000 1.000
## 17 0.000 2.000
## 18 -0.763 0.313
## 19 9.230 53.280
## 20 10.630 60.660##################################
# Identifying potential data quality issues
##################################
##################################
# Checking for missing observations
##################################
if ((nrow(DQA.Summary[DQA.Summary$NA.Count>0,]))>0){
print(paste0("Missing observations noted for ",
(nrow(DQA.Summary[DQA.Summary$NA.Count>0,])),
" variable(s) with NA.Count>0 and Fill.Rate<1.0."))
DQA.Summary[DQA.Summary$NA.Count>0,]
} else {
print("No missing observations noted.")
}## [1] "No missing observations noted."##################################
# Checking for zero or near-zero variance predictors
##################################
if (length(names(DQA.Predictors.Factor))==0) {
print("No factor predictors noted.")
} else if (nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,])),
" factor variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Factor.Summary[as.numeric(as.character(DQA.Predictors.Factor.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance factor predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 124 factor variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count First.Mode.Value Second.Mode.Value
## 13 FP013 factor 2 0 1
## 14 FP014 factor 2 0 1
## 15 FP015 factor 2 1 0
## 16 FP016 factor 2 0 1
## 17 FP017 factor 2 0 1
## 18 FP018 factor 2 0 1
## 19 FP019 factor 2 0 1
## 20 FP020 factor 2 0 1
## 21 FP021 factor 2 0 1
## 22 FP022 factor 2 0 1
## 23 FP023 factor 2 0 1
## 24 FP024 factor 2 0 1
## 25 FP025 factor 2 0 1
## 26 FP026 factor 2 0 1
## 27 FP027 factor 2 0 1
## 28 FP028 factor 2 0 1
## 29 FP029 factor 2 0 1
## 30 FP030 factor 2 0 1
## 31 FP031 factor 2 0 1
## 32 FP032 factor 2 0 1
## 33 FP033 factor 2 0 1
## 34 FP034 factor 2 0 1
## 35 FP035 factor 2 0 1
## 36 FP036 factor 2 0 1
## 37 FP037 factor 2 0 1
## 38 FP038 factor 2 0 1
## 39 FP039 factor 2 0 1
## 40 FP040 factor 2 0 1
## 41 FP041 factor 2 0 1
## 42 FP042 factor 2 0 1
## 43 FP043 factor 2 0 1
## 44 FP044 factor 2 0 1
## 45 FP045 factor 2 0 1
## 48 FP048 factor 2 0 1
## 49 FP049 factor 2 0 1
## 50 FP050 factor 2 0 1
## 51 FP051 factor 2 0 1
## 52 FP052 factor 2 0 1
## 53 FP053 factor 2 0 1
## 54 FP054 factor 2 0 1
## 55 FP055 factor 2 0 1
## 56 FP056 factor 2 0 1
## 57 FP057 factor 2 0 1
## 58 FP058 factor 2 0 1
## 59 FP059 factor 2 0 1
## 114 FP114 factor 2 0 1
## 119 FP119 factor 2 0 1
## 120 FP120 factor 2 0 1
## 121 FP121 factor 2 0 1
## 122 FP122 factor 2 0 1
## 124 FP124 factor 2 0 1
## 125 FP125 factor 2 0 1
## 126 FP126 factor 2 0 1
## 127 FP127 factor 2 0 1
## 128 FP128 factor 2 0 1
## 129 FP129 factor 2 0 1
## 130 FP130 factor 2 0 1
## 131 FP131 factor 2 0 1
## 132 FP132 factor 2 0 1
## 133 FP133 factor 2 0 1
## 134 FP134 factor 2 0 1
## 135 FP135 factor 2 0 1
## 136 FP136 factor 2 0 1
## 137 FP137 factor 2 0 1
## 138 FP138 factor 2 0 1
## 139 FP139 factor 2 0 1
## 140 FP140 factor 2 0 1
## 141 FP141 factor 2 0 1
## 142 FP142 factor 2 0 1
## 143 FP143 factor 2 0 1
## 144 FP144 factor 2 0 1
## 145 FP145 factor 2 0 1
## 146 FP146 factor 2 0 1
## 147 FP147 factor 2 0 1
## 148 FP148 factor 2 0 1
## 149 FP149 factor 2 0 1
## 150 FP150 factor 2 0 1
## 151 FP151 factor 2 0 1
## 152 FP152 factor 2 0 1
## 153 FP153 factor 2 0 1
## 154 FP154 factor 2 0 1
## 155 FP155 factor 2 0 1
## 156 FP156 factor 2 0 1
## 157 FP157 factor 2 0 1
## 158 FP158 factor 2 0 1
## 159 FP159 factor 2 0 1
## 160 FP160 factor 2 0 1
## 161 FP161 factor 2 0 1
## 172 FP172 factor 2 0 1
## 173 FP173 factor 2 0 1
## 174 FP174 factor 2 0 1
## 175 FP175 factor 2 0 1
## 176 FP176 factor 2 0 1
## 177 FP177 factor 2 0 1
## 178 FP178 factor 2 0 1
## 179 FP179 factor 2 0 1
## 180 FP180 factor 2 0 1
## 181 FP181 factor 2 0 1
## 182 FP182 factor 2 0 1
## 183 FP183 factor 2 0 1
## 184 FP184 factor 2 0 1
## 185 FP185 factor 2 0 1
## 186 FP186 factor 2 0 1
## 187 FP187 factor 2 0 1
## 188 FP188 factor 2 0 1
## 189 FP189 factor 2 0 1
## 190 FP190 factor 2 0 1
## 191 FP191 factor 2 0 1
## 192 FP192 factor 2 0 1
## 193 FP193 factor 2 0 1
## 194 FP194 factor 2 0 1
## 195 FP195 factor 2 0 1
## 196 FP196 factor 2 0 1
## 197 FP197 factor 2 0 1
## 198 FP198 factor 2 0 1
## 199 FP199 factor 2 0 1
## 200 FP200 factor 2 0 1
## 201 FP201 factor 2 0 1
## 203 FP203 factor 2 0 1
## 204 FP204 factor 2 0 1
## 205 FP205 factor 2 0 1
## 206 FP206 factor 2 0 1
## 207 FP207 factor 2 0 1
## 208 FP208 factor 2 0 1
## First.Mode.Count Second.Mode.Count Unique.Count.Ratio
## 13 793 158 0.002
## 14 798 153 0.002
## 15 818 133 0.002
## 16 812 139 0.002
## 17 814 137 0.002
## 18 826 125 0.002
## 19 835 116 0.002
## 20 837 114 0.002
## 21 836 115 0.002
## 22 852 99 0.002
## 23 834 117 0.002
## 24 844 107 0.002
## 25 841 110 0.002
## 26 871 80 0.002
## 27 858 93 0.002
## 28 850 101 0.002
## 29 854 97 0.002
## 30 862 89 0.002
## 31 866 85 0.002
## 32 881 70 0.002
## 33 885 66 0.002
## 34 875 76 0.002
## 35 882 69 0.002
## 36 879 72 0.002
## 37 884 67 0.002
## 38 869 82 0.002
## 39 880 71 0.002
## 40 886 65 0.002
## 41 891 60 0.002
## 42 897 54 0.002
## 43 888 63 0.002
## 44 894 57 0.002
## 45 898 53 0.002
## 48 833 118 0.002
## 49 835 116 0.002
## 50 844 107 0.002
## 51 847 104 0.002
## 52 864 87 0.002
## 53 862 89 0.002
## 54 879 72 0.002
## 55 900 51 0.002
## 56 889 62 0.002
## 57 837 114 0.002
## 58 843 108 0.002
## 59 899 52 0.002
## 114 803 148 0.002
## 119 796 155 0.002
## 120 793 158 0.002
## 121 818 133 0.002
## 122 795 156 0.002
## 124 797 154 0.002
## 125 803 148 0.002
## 126 810 141 0.002
## 127 818 133 0.002
## 128 810 141 0.002
## 129 819 132 0.002
## 130 851 100 0.002
## 131 831 120 0.002
## 132 832 119 0.002
## 133 831 120 0.002
## 134 830 121 0.002
## 135 831 120 0.002
## 136 836 115 0.002
## 137 841 110 0.002
## 138 845 106 0.002
## 139 873 78 0.002
## 140 845 106 0.002
## 141 840 111 0.002
## 142 847 104 0.002
## 143 874 77 0.002
## 144 852 99 0.002
## 145 852 99 0.002
## 146 853 98 0.002
## 147 851 100 0.002
## 148 868 83 0.002
## 149 865 86 0.002
## 150 876 75 0.002
## 151 898 53 0.002
## 152 873 78 0.002
## 153 877 74 0.002
## 154 915 36 0.002
## 155 885 66 0.002
## 156 884 67 0.002
## 157 892 59 0.002
## 158 900 51 0.002
## 159 884 67 0.002
## 160 886 65 0.002
## 161 888 63 0.002
## 172 807 144 0.002
## 173 816 135 0.002
## 174 827 124 0.002
## 175 823 128 0.002
## 176 835 116 0.002
## 177 836 115 0.002
## 178 836 115 0.002
## 179 858 93 0.002
## 180 849 102 0.002
## 181 862 89 0.002
## 182 857 94 0.002
## 183 879 72 0.002
## 184 871 80 0.002
## 185 870 81 0.002
## 186 878 73 0.002
## 187 882 69 0.002
## 188 886 65 0.002
## 189 878 73 0.002
## 190 882 69 0.002
## 191 884 67 0.002
## 192 893 58 0.002
## 193 892 59 0.002
## 194 895 56 0.002
## 195 893 58 0.002
## 196 897 54 0.002
## 197 901 50 0.002
## 198 897 54 0.002
## 199 906 45 0.002
## 200 904 47 0.002
## 201 901 50 0.002
## 203 842 109 0.002
## 204 857 94 0.002
## 205 877 74 0.002
## 206 894 57 0.002
## 207 897 54 0.002
## 208 844 107 0.002
## First.Second.Mode.Ratio
## 13 5.019
## 14 5.216
## 15 6.150
## 16 5.842
## 17 5.942
## 18 6.608
## 19 7.198
## 20 7.342
## 21 7.270
## 22 8.606
## 23 7.128
## 24 7.888
## 25 7.645
## 26 10.887
## 27 9.226
## 28 8.416
## 29 8.804
## 30 9.685
## 31 10.188
## 32 12.586
## 33 13.409
## 34 11.513
## 35 12.783
## 36 12.208
## 37 13.194
## 38 10.598
## 39 12.394
## 40 13.631
## 41 14.850
## 42 16.611
## 43 14.095
## 44 15.684
## 45 16.943
## 48 7.059
## 49 7.198
## 50 7.888
## 51 8.144
## 52 9.931
## 53 9.685
## 54 12.208
## 55 17.647
## 56 14.339
## 57 7.342
## 58 7.806
## 59 17.288
## 114 5.426
## 119 5.135
## 120 5.019
## 121 6.150
## 122 5.096
## 124 5.175
## 125 5.426
## 126 5.745
## 127 6.150
## 128 5.745
## 129 6.205
## 130 8.510
## 131 6.925
## 132 6.992
## 133 6.925
## 134 6.860
## 135 6.925
## 136 7.270
## 137 7.645
## 138 7.972
## 139 11.192
## 140 7.972
## 141 7.568
## 142 8.144
## 143 11.351
## 144 8.606
## 145 8.606
## 146 8.704
## 147 8.510
## 148 10.458
## 149 10.058
## 150 11.680
## 151 16.943
## 152 11.192
## 153 11.851
## 154 25.417
## 155 13.409
## 156 13.194
## 157 15.119
## 158 17.647
## 159 13.194
## 160 13.631
## 161 14.095
## 172 5.604
## 173 6.044
## 174 6.669
## 175 6.430
## 176 7.198
## 177 7.270
## 178 7.270
## 179 9.226
## 180 8.324
## 181 9.685
## 182 9.117
## 183 12.208
## 184 10.887
## 185 10.741
## 186 12.027
## 187 12.783
## 188 13.631
## 189 12.027
## 190 12.783
## 191 13.194
## 192 15.397
## 193 15.119
## 194 15.982
## 195 15.397
## 196 16.611
## 197 18.020
## 198 16.611
## 199 20.133
## 200 19.234
## 201 18.020
## 203 7.725
## 204 9.117
## 205 11.851
## 206 15.684
## 207 16.611
## 208 7.888if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,])),
" numeric variable(s) with First.Second.Mode.Ratio>5."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$First.Second.Mode.Ratio))>5,]
} else {
print("No low variance numeric predictors due to high first-second mode ratio noted.")
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>5."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 14 NumSulfer integer 5 0.005 0.000
## 15 NumChlorine integer 11 0.012 0.000
## 16 NumHalogen integer 11 0.012 0.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 14 1.000 830 96 8.646
## 15 1.000 750 81 9.259
## 16 1.000 685 107 6.402
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 14 0.000 0.164 0.000 4.000 3.842 21.526 0.000 0.000
## 15 0.000 0.556 0.000 10.000 3.178 13.780 0.000 0.000
## 16 0.000 0.698 0.000 10.000 2.691 10.808 0.000 1.000if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])>0){
print(paste0("Low variance observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,])),
" numeric variable(s) with Unique.Count.Ratio<0.01."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Unique.Count.Ratio))<0.01,]
} else {
print("No low variance numeric predictors due to low unique count ratio noted.")
}## [1] "Low variance observed for 4 numeric variable(s) with Unique.Count.Ratio<0.01."## Column.Name Column.Type Unique.Count Unique.Count.Ratio First.Mode.Value
## 8 NumDblBonds integer 8 0.008 0.000
## 12 NumNitrogen integer 7 0.007 0.000
## 14 NumSulfer integer 5 0.005 0.000
## 17 NumRings integer 8 0.008 1.000
## Second.Mode.Value First.Mode.Count Second.Mode.Count First.Second.Mode.Ratio
## 8 1.000 427 268 1.593
## 12 1.000 546 191 2.859
## 14 1.000 830 96 8.646
## 17 0.000 323 260 1.242
## Minimum Mean Median Maximum Skewness Kurtosis Percentile25th Percentile75th
## 8 0.000 1.006 1.000 7.000 1.360 4.760 0.000 2.000
## 12 0.000 0.813 0.000 6.000 1.554 4.831 0.000 1.000
## 14 0.000 0.164 0.000 4.000 3.842 21.526 0.000 0.000
## 17 0.000 1.402 1.000 7.000 1.034 3.875 0.000 2.000##################################
# Checking for skewed predictors
##################################
if (length(names(DQA.Predictors.Numeric))==0) {
print("No numeric predictors noted.")
} else if (nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])>0){
print(paste0("High skewness observed for ",
(nrow(DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),])),
" numeric variable(s) with Skewness>3 or Skewness<(-3)."))
DQA.Predictors.Numeric.Summary[as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))>3 |
as.numeric(as.character(DQA.Predictors.Numeric.Summary$Skewness))<(-3),]
} else {
print("No skewed numeric predictors noted.")
}## [1] "High skewness observed for 3 numeric variable(s) with Skewness>3 or Skewness<(-3)."## Column.Name Column.Type Unique.Count Unique.Count.Ratio
## 14 NumSulfer integer 5 0.005
## 15 NumChlorine integer 11 0.012
## 18 HydrophilicFactor numeric 369 0.388
## First.Mode.Value Second.Mode.Value First.Mode.Count Second.Mode.Count
## 14 0.000 1.000 830 96
## 15 0.000 1.000 750 81
## 18 -0.828 -0.158 21 20
## First.Second.Mode.Ratio Minimum Mean Median Maximum Skewness Kurtosis
## 14 8.646 0.000 0.164 0.000 4.000 3.842 21.526
## 15 9.259 0.000 0.556 0.000 10.000 3.178 13.780
## 18 1.050 -0.985 -0.021 -0.314 13.483 3.404 27.504
## Percentile25th Percentile75th
## 14 0.000 0.000
## 15 0.000 0.000
## 18 -0.763 0.3131.3 Data Preprocessing
1.3.1 Outlier
[A] Outliers noted for 20 variables with the numeric data visualized through a boxplot including observations classified as suspected outliers using the IQR criterion. The IQR criterion means that all observations above the (75th percentile + 1.5 x IQR) or below the (25th percentile - 1.5 x IQR) are suspected outliers, where IQR is the difference between the third quartile (75th percentile) and first quartile (25th percentile). Outlier treatment for numerical stability remains optional depending on potential model requirements for the subsequent steps.
[A.1] MolWeight variable (8 outliers detected)
[A.2] NumAtoms variable (44 outliers detected)
[A.3] NumNonHAtoms variable (15 outliers detected)
[A.4] NumBonds variable (51 outliers detected)
[A.5] NumNonHBonds variable (18 outliers detected)
[A.6] NumMultBonds variable (6 outliers detected)
[A.7] NumRotBonds variable (23 outliers detected)
[A.8] NumDblBonds variable (3 outliers detected)
[A.9] NumAromaticBonds variable (35 outliers detected)
[A.10] NumHydrogen variable (32 outliers detected)
[A.11] NumCarbon variable (35 outliers detected)
[A.12] NumNitrogen variable (91 outliers detected)
[A.13] NumOxygen variable (36 outliers detected)
[A.14] NumSulfer variable (121 outliers detected)
[A.15] NumChlorine variable (201 outliers detected)
[A.16] NumHalogen variable (99 outliers detected)
[A.17] NumRings variable (4 outliers detected)
[A.18] HydrophilicFactor variable (53 outliers detected)
[A.19] SurfaceArea1 variable (19 outliers detected)
[A.20] SurfaceArea2 variable (12 outliers detected)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,-(grep("FP", names(DPA.Predictors)))]
##################################
# Identifying outliers for the numeric predictors
##################################
OutlierCountList <- c()
for (i in 1:ncol(DPA.Predictors.Numeric)) {
Outliers <- boxplot.stats(DPA.Predictors.Numeric[,i])$out
OutlierCount <- length(Outliers)
OutlierCountList <- append(OutlierCountList,OutlierCount)
OutlierIndices <- which(DPA.Predictors.Numeric[,i] %in% c(Outliers))
boxplot(DPA.Predictors.Numeric[,i],
ylab = names(DPA.Predictors.Numeric)[i],
main = names(DPA.Predictors.Numeric)[i],
horizontal=TRUE)
mtext(paste0(OutlierCount, " Outlier(s) Detected"))
}
OutlierCountSummary <- as.data.frame(cbind(names(DPA.Predictors.Numeric),(OutlierCountList)))
names(OutlierCountSummary) <- c("NumericPredictors","OutlierCount")
OutlierCountSummary$OutlierCount <- as.numeric(as.character(OutlierCountSummary$OutlierCount))
NumericPredictorWithOutlierCount <- nrow(OutlierCountSummary[OutlierCountSummary$OutlierCount>0,])
print(paste0(NumericPredictorWithOutlierCount, " numeric variable(s) were noted with outlier(s)." ))## [1] "20 numeric variable(s) were noted with outlier(s)."##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA.Predictors.Numeric))| Name | DPA.Predictors.Numeric |
| Number of rows | 951 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| numeric | 20 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MolWeight | 0 | 1 | 201.65 | 97.91 | 46.09 | 122.60 | 179.23 | 264.34 | 665.81 | ▇▆▂▁▁ |
| NumAtoms | 0 | 1 | 25.51 | 12.61 | 5.00 | 17.00 | 22.00 | 31.00 | 94.00 | ▇▆▂▁▁ |
| NumNonHAtoms | 0 | 1 | 13.16 | 6.50 | 2.00 | 8.00 | 12.00 | 17.00 | 47.00 | ▇▆▂▁▁ |
| NumBonds | 0 | 1 | 25.91 | 13.48 | 4.00 | 17.00 | 23.00 | 31.50 | 97.00 | ▇▇▂▁▁ |
| NumNonHBonds | 0 | 1 | 13.56 | 7.57 | 1.00 | 8.00 | 12.00 | 18.00 | 50.00 | ▇▇▂▁▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 12.35 | 7.32 | 0.00 | 7.00 | 11.00 | 16.00 | 47.00 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 9.89 | 5.29 | 1.00 | 6.00 | 9.00 | 12.00 | 33.00 | ▇▇▃▁▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric)## [1] 951 201.3.2 Zero and Near-Zero Variance
[A] Low variance noted for 127 variables from the previous data quality assessment using a lower threshold.
[B] Low variance noted for 3 variables using a preprocessing summary from the caret package. The nearZeroVar method using both the freqCut and uniqueCut criteria set at 95/5 and 10, respectively, were applied on the dataset.
[B.1] FP154 variable (factor)
[B.2] FP199 variable (factor)
[B.3] FP200 variable (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Gathering descriptive statistics
##################################
(DPA_Skimmed <- skim(DPA))| Name | DPA |
| Number of rows | 951 |
| Number of columns | 229 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 228 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 524, Low: 427 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FP001 | 0 | 1 | 0.49 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP002 | 0 | 1 | 0.54 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP003 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP004 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP005 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP006 | 0 | 1 | 0.40 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP007 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP008 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP009 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP010 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP011 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP012 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP013 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP014 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP015 | 0 | 1 | 0.86 | 0.35 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| FP016 | 0 | 1 | 0.15 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP017 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP018 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP019 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP020 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP021 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP022 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP023 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP024 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP025 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP026 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP027 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP028 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP029 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP030 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP031 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP032 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP033 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP034 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP035 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP036 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP037 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP038 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP039 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP040 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP041 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP042 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP043 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP044 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP045 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP046 | 0 | 1 | 0.32 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP047 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP048 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP049 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP050 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP051 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP052 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP053 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP054 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP055 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP056 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP057 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP058 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP059 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP060 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP061 | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP062 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP063 | 0 | 1 | 0.43 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP064 | 0 | 1 | 0.42 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP065 | 0 | 1 | 0.59 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP066 | 0 | 1 | 0.61 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP067 | 0 | 1 | 0.38 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP068 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP069 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP070 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP071 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP072 | 0 | 1 | 0.66 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP073 | 0 | 1 | 0.31 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP074 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP075 | 0 | 1 | 0.34 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP076 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP077 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP078 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP079 | 0 | 1 | 0.69 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP080 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP081 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP082 | 0 | 1 | 0.71 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP083 | 0 | 1 | 0.27 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP084 | 0 | 1 | 0.29 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP085 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP086 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP087 | 0 | 1 | 0.73 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP088 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP089 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP090 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP091 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP092 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP093 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP094 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP095 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP096 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP097 | 0 | 1 | 0.24 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP098 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP099 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP100 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP101 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP102 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP103 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP104 | 0 | 1 | 0.22 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP105 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP106 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP107 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP108 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP109 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP110 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP111 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP112 | 0 | 1 | 0.19 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP113 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP114 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP115 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP116 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP117 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP118 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP119 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP120 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP121 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP122 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP123 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP124 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP125 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP126 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP127 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP128 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP129 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP130 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP131 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP132 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP133 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP134 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP135 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP136 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP137 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP138 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP139 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP140 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP141 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP142 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP143 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP144 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP145 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP146 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP147 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP148 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP149 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP150 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP151 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP152 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP153 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP154 | 0 | 1 | 0.04 | 0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP155 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP156 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP157 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP158 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP159 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP160 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP161 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP162 | 0 | 1 | 0.50 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP163 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP164 | 0 | 1 | 0.63 | 0.48 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP165 | 0 | 1 | 0.35 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP166 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP167 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP168 | 0 | 1 | 0.67 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP169 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP170 | 0 | 1 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP171 | 0 | 1 | 0.17 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP172 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP173 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP174 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP175 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP176 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP177 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP178 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP179 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP180 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP181 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP182 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP183 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP184 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP185 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP186 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP187 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP188 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP189 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP190 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP191 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP192 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP193 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP194 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP195 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP196 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP197 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP198 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP199 | 0 | 1 | 0.05 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP200 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP201 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP202 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP203 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP204 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP205 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP206 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP207 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP208 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| MolWeight | 0 | 1 | 201.65 | 97.91 | 46.09 | 122.60 | 179.23 | 264.34 | 665.81 | ▇▆▂▁▁ |
| NumAtoms | 0 | 1 | 25.51 | 12.61 | 5.00 | 17.00 | 22.00 | 31.00 | 94.00 | ▇▆▂▁▁ |
| NumNonHAtoms | 0 | 1 | 13.16 | 6.50 | 2.00 | 8.00 | 12.00 | 17.00 | 47.00 | ▇▆▂▁▁ |
| NumBonds | 0 | 1 | 25.91 | 13.48 | 4.00 | 17.00 | 23.00 | 31.50 | 97.00 | ▇▇▂▁▁ |
| NumNonHBonds | 0 | 1 | 13.56 | 7.57 | 1.00 | 8.00 | 12.00 | 18.00 | 50.00 | ▇▇▂▁▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 12.35 | 7.32 | 0.00 | 7.00 | 11.00 | 16.00 | 47.00 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 9.89 | 5.29 | 1.00 | 6.00 | 9.00 | 12.00 | 33.00 | ▇▇▃▁▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
##################################
# Identifying columns with low variance
###################################
DPA_LowVariance <- nearZeroVar(DPA,
freqCut = 95/5,
uniqueCut = 10,
saveMetrics= TRUE)
(DPA_LowVariance[DPA_LowVariance$nzv,])## freqRatio percentUnique zeroVar nzv
## FP154 25.41667 0.2103049 FALSE TRUE
## FP199 20.13333 0.2103049 FALSE TRUE
## FP200 19.23404 0.2103049 FALSE TRUEif ((nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))==0){
print("No low variance predictors noted.")
} else {
print(paste0("Low variance observed for ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."))
DPA_LowVarianceForRemoval <- (nrow(DPA_LowVariance[DPA_LowVariance$nzv,]))
print(paste0("Low variance can be resolved by removing ",
(nrow(DPA_LowVariance[DPA_LowVariance$nzv,])),
" numeric variable(s)."))
for (j in 1:DPA_LowVarianceForRemoval) {
DPA_LowVarianceRemovedVariable <- rownames(DPA_LowVariance[DPA_LowVariance$nzv,])[j]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LowVarianceRemovedVariable))
}
DPA %>%
skim() %>%
dplyr::filter(skim_variable %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,]))
##################################
# Filtering out columns with low variance
#################################
DPA_ExcludedLowVariance <- DPA[,!names(DPA) %in% rownames(DPA_LowVariance[DPA_LowVariance$nzv,])]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLowVariance_Skimmed <- skim(DPA_ExcludedLowVariance))
}## [1] "Low variance observed for 3 numeric variable(s) with First.Second.Mode.Ratio>4 and Unique.Count.Ratio<0.10."
## [1] "Low variance can be resolved by removing 3 numeric variable(s)."
## [1] "Variable 1 for removal: FP154"
## [1] "Variable 2 for removal: FP199"
## [1] "Variable 3 for removal: FP200"| Name | DPA_ExcludedLowVariance |
| Number of rows | 951 |
| Number of columns | 226 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 225 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 524, Low: 427 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FP001 | 0 | 1 | 0.49 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP002 | 0 | 1 | 0.54 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP003 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP004 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP005 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP006 | 0 | 1 | 0.40 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP007 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP008 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP009 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP010 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP011 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP012 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP013 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP014 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP015 | 0 | 1 | 0.86 | 0.35 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| FP016 | 0 | 1 | 0.15 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP017 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP018 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP019 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP020 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP021 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP022 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP023 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP024 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP025 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP026 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP027 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP028 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP029 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP030 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP031 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP032 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP033 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP034 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP035 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP036 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP037 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP038 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP039 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP040 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP041 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP042 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP043 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP044 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP045 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP046 | 0 | 1 | 0.32 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP047 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP048 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP049 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP050 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP051 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP052 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP053 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP054 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP055 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP056 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP057 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP058 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP059 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP060 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP061 | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP062 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP063 | 0 | 1 | 0.43 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP064 | 0 | 1 | 0.42 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP065 | 0 | 1 | 0.59 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP066 | 0 | 1 | 0.61 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP067 | 0 | 1 | 0.38 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP068 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP069 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP070 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP071 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP072 | 0 | 1 | 0.66 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP073 | 0 | 1 | 0.31 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP074 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP075 | 0 | 1 | 0.34 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP076 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP077 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP078 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP079 | 0 | 1 | 0.69 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP080 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP081 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP082 | 0 | 1 | 0.71 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP083 | 0 | 1 | 0.27 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP084 | 0 | 1 | 0.29 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP085 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP086 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP087 | 0 | 1 | 0.73 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP088 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP089 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP090 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP091 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP092 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP093 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP094 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP095 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP096 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP097 | 0 | 1 | 0.24 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP098 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP099 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP100 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP101 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP102 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP103 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP104 | 0 | 1 | 0.22 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP105 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP106 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP107 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP108 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP109 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP110 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP111 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP112 | 0 | 1 | 0.19 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP113 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP114 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP115 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP116 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP117 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP118 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP119 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP120 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP121 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP122 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP123 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP124 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP125 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP126 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP127 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP128 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP129 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP130 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP131 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP132 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP133 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP134 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP135 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP136 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP137 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP138 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP139 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP140 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP141 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP142 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP143 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP144 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP145 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP146 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP147 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP148 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP149 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP150 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP151 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP152 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP153 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP155 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP156 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP157 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP158 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP159 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP160 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP161 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP162 | 0 | 1 | 0.50 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP163 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP164 | 0 | 1 | 0.63 | 0.48 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP165 | 0 | 1 | 0.35 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP166 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP167 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP168 | 0 | 1 | 0.67 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP169 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP170 | 0 | 1 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP171 | 0 | 1 | 0.17 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP172 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP173 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP174 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP175 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP176 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP177 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP178 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP179 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP180 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP181 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP182 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP183 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP184 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP185 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP186 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP187 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP188 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP189 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP190 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP191 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP192 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP193 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP194 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP195 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP196 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP197 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP198 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP201 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP202 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP203 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP204 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP205 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP206 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP207 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP208 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| MolWeight | 0 | 1 | 201.65 | 97.91 | 46.09 | 122.60 | 179.23 | 264.34 | 665.81 | ▇▆▂▁▁ |
| NumAtoms | 0 | 1 | 25.51 | 12.61 | 5.00 | 17.00 | 22.00 | 31.00 | 94.00 | ▇▆▂▁▁ |
| NumNonHAtoms | 0 | 1 | 13.16 | 6.50 | 2.00 | 8.00 | 12.00 | 17.00 | 47.00 | ▇▆▂▁▁ |
| NumBonds | 0 | 1 | 25.91 | 13.48 | 4.00 | 17.00 | 23.00 | 31.50 | 97.00 | ▇▇▂▁▁ |
| NumNonHBonds | 0 | 1 | 13.56 | 7.57 | 1.00 | 8.00 | 12.00 | 18.00 | 50.00 | ▇▇▂▁▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 12.35 | 7.32 | 0.00 | 7.00 | 11.00 | 16.00 | 47.00 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 9.89 | 5.29 | 1.00 | 6.00 | 9.00 | 12.00 | 33.00 | ▇▇▃▁▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedLowVariance)## [1] 951 2261.3.3 Collinearity
[A] High correlation > 95% were noted for 2 variable pairs as confirmed using the preprocessing summaries from the caret and lares packages.
[A.1] NumNonHAtoms and NumNonHBonds variables (numeric)
[A.2] NumMultBonds and NumAromaticBonds variables (numeric)
[A.3] NumAtoms and NumBonds variables (numeric)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,-(grep("FP", names(DPA.Predictors)))]
##################################
# Visualizing pairwise correlation between predictors
##################################
DPA_CorrelationTest <- cor.mtest(DPA.Predictors.Numeric,
method = "pearson",
conf.level = .95)
corrplot(cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs"),
method = "circle",
type = "upper",
order = "original",
tl.col = "black",
tl.cex = 0.75,
tl.srt = 90,
sig.level = 0.05,
p.mat = DPA_CorrelationTest$p,
insig = "blank")
##################################
# Identifying the highly correlated variables
##################################
DPA_Correlation <- cor(DPA.Predictors.Numeric,
method = "pearson",
use="pairwise.complete.obs")
(DPA_HighlyCorrelatedCount <- sum(abs(DPA_Correlation[upper.tri(DPA_Correlation)]) > 0.95))## [1] 3if (DPA_HighlyCorrelatedCount == 0) {
print("No highly correlated predictors noted.")
} else {
print(paste0("High correlation observed for ",
(DPA_HighlyCorrelatedCount),
" pairs of numeric variable(s) with Correlation.Coefficient>0.95."))
(DPA_HighlyCorrelatedPairs <- corr_cross(DPA.Predictors.Numeric,
max_pvalue = 0.05,
top = DPA_HighlyCorrelatedCount,
rm.na = TRUE,
grid = FALSE
))
}## [1] "High correlation observed for 3 pairs of numeric variable(s) with Correlation.Coefficient>0.95."
if (DPA_HighlyCorrelatedCount > 0) {
DPA_HighlyCorrelated <- findCorrelation(DPA_Correlation, cutoff = 0.95)
(DPA_HighlyCorrelatedForRemoval <- length(DPA_HighlyCorrelated))
print(paste0("High correlation can be resolved by removing ",
(DPA_HighlyCorrelatedForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_HighlyCorrelatedForRemoval) {
DPA_HighlyCorrelatedRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_HighlyCorrelated[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_HighlyCorrelatedRemovedVariable))
}
##################################
# Filtering out columns with high correlation
#################################
DPA_ExcludedHighCorrelation <- DPA[,-DPA_HighlyCorrelated]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedHighCorrelation_Skimmed <- skim(DPA_ExcludedHighCorrelation))
}## [1] "High correlation can be resolved by removing 3 numeric variable(s)."
## [1] "Variable 1 for removal: NumNonHAtoms"
## [1] "Variable 2 for removal: NumBonds"
## [1] "Variable 3 for removal: NumAromaticBonds"| Name | DPA_ExcludedHighCorrelati… |
| Number of rows | 951 |
| Number of columns | 226 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 225 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 524, Low: 427 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FP001 | 0 | 1 | 0.49 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP002 | 0 | 1 | 0.54 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP005 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP006 | 0 | 1 | 0.40 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP007 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP008 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP010 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP011 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP012 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP013 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP014 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP015 | 0 | 1 | 0.86 | 0.35 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| FP016 | 0 | 1 | 0.15 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP017 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP018 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP019 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP020 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP021 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP022 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP023 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP024 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP025 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP026 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP027 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP028 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP029 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP030 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP031 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP032 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP033 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP034 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP035 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP036 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP037 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP038 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP039 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP040 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP041 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP042 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP043 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP044 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP045 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP046 | 0 | 1 | 0.32 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP047 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP048 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP049 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP050 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP051 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP052 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP053 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP054 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP055 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP056 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP057 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP058 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP059 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP060 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP061 | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP062 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP063 | 0 | 1 | 0.43 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP064 | 0 | 1 | 0.42 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP065 | 0 | 1 | 0.59 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP066 | 0 | 1 | 0.61 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP067 | 0 | 1 | 0.38 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP068 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP069 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP070 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP071 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP072 | 0 | 1 | 0.66 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP073 | 0 | 1 | 0.31 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP074 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP075 | 0 | 1 | 0.34 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP076 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP077 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP078 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP079 | 0 | 1 | 0.69 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP080 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP081 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP082 | 0 | 1 | 0.71 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP083 | 0 | 1 | 0.27 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP084 | 0 | 1 | 0.29 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP085 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP086 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP087 | 0 | 1 | 0.73 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP088 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP089 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP090 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP091 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP092 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP093 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP094 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP095 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP096 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP097 | 0 | 1 | 0.24 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP098 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP099 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP100 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP101 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP102 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP103 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP104 | 0 | 1 | 0.22 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP105 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP106 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP107 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP108 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP109 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP110 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP111 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP112 | 0 | 1 | 0.19 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP113 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP114 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP115 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP116 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP117 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP118 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP119 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP120 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP121 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP122 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP123 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP124 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP125 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP126 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP127 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP128 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP129 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP130 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP131 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP132 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP133 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP134 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP135 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP136 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP137 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP138 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP139 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP140 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP141 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP142 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP143 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP144 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP145 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP146 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP147 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP148 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP149 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP150 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP151 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP152 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP153 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP154 | 0 | 1 | 0.04 | 0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP155 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP156 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP157 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP158 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP159 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP160 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP161 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP162 | 0 | 1 | 0.50 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP163 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP164 | 0 | 1 | 0.63 | 0.48 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP165 | 0 | 1 | 0.35 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP166 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP167 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP168 | 0 | 1 | 0.67 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP169 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP170 | 0 | 1 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP171 | 0 | 1 | 0.17 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP172 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP173 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP174 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP175 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP176 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP177 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP178 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP179 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP180 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP181 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP182 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP183 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP184 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP185 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP186 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP187 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP188 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP189 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP190 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP191 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP192 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP193 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP194 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP195 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP196 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP197 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP198 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP199 | 0 | 1 | 0.05 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP200 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP201 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP202 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP203 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP204 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP205 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP206 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP207 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP208 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| MolWeight | 0 | 1 | 201.65 | 97.91 | 46.09 | 122.60 | 179.23 | 264.34 | 665.81 | ▇▆▂▁▁ |
| NumAtoms | 0 | 1 | 25.51 | 12.61 | 5.00 | 17.00 | 22.00 | 31.00 | 94.00 | ▇▆▂▁▁ |
| NumNonHAtoms | 0 | 1 | 13.16 | 6.50 | 2.00 | 8.00 | 12.00 | 17.00 | 47.00 | ▇▆▂▁▁ |
| NumBonds | 0 | 1 | 25.91 | 13.48 | 4.00 | 17.00 | 23.00 | 31.50 | 97.00 | ▇▇▂▁▁ |
| NumNonHBonds | 0 | 1 | 13.56 | 7.57 | 1.00 | 8.00 | 12.00 | 18.00 | 50.00 | ▇▇▂▁▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 12.35 | 7.32 | 0.00 | 7.00 | 11.00 | 16.00 | 47.00 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 9.89 | 5.29 | 1.00 | 6.00 | 9.00 | 12.00 | 33.00 | ▇▇▃▁▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedHighCorrelation)## [1] 951 2261.3.4 Linear Dependencies
[A] Linear dependencies noted for 2 subsets of variables using the preprocessing summary from the caret package applying the findLinearCombos method which utilizes the QR decomposition of a matrix to enumerate sets of linear combinations (if they exist).
[B] Subset 1
[B.1] NumNonHBonds variable (numeric)
[B.2] NumAtoms variable (numeric)
[B.3] NumNonHAtoms variable (numeric)
[B.3] NumBonds variable (numeric)
[C] Subset 2
[C.1] NumHydrogen variable (numeric)
[C.2] NumAtoms variable (numeric)
[C.3] NumNonHAtoms variable (numeric)
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,sapply(DPA.Predictors, is.numeric)]
##################################
# Identifying the linearly dependent variables
##################################
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
(DPA_LinearlyDependentCount <- length(DPA_LinearlyDependent$linearCombos))## [1] 2if (DPA_LinearlyDependentCount == 0) {
print("No linearly dependent predictors noted.")
} else {
print(paste0("Linear dependency observed for ",
(DPA_LinearlyDependentCount),
" subset(s) of numeric variable(s)."))
for (i in 1:DPA_LinearlyDependentCount) {
DPA_LinearlyDependentSubset <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$linearCombos[[i]]]
print(paste0("Linear dependent variable(s) for subset ",
i,
" include: ",
DPA_LinearlyDependentSubset))
}
}## [1] "Linear dependency observed for 2 subset(s) of numeric variable(s)."
## [1] "Linear dependent variable(s) for subset 1 include: NumNonHBonds"
## [2] "Linear dependent variable(s) for subset 1 include: NumAtoms"
## [3] "Linear dependent variable(s) for subset 1 include: NumNonHAtoms"
## [4] "Linear dependent variable(s) for subset 1 include: NumBonds"
## [1] "Linear dependent variable(s) for subset 2 include: NumHydrogen"
## [2] "Linear dependent variable(s) for subset 2 include: NumAtoms"
## [3] "Linear dependent variable(s) for subset 2 include: NumNonHAtoms"##################################
# Identifying the linearly dependent variables for removal
##################################
if (DPA_LinearlyDependentCount > 0) {
DPA_LinearlyDependent <- findLinearCombos(DPA.Predictors.Numeric)
DPA_LinearlyDependentForRemoval <- length(DPA_LinearlyDependent$remove)
print(paste0("Linear dependency can be resolved by removing ",
(DPA_LinearlyDependentForRemoval),
" numeric variable(s)."))
for (j in 1:DPA_LinearlyDependentForRemoval) {
DPA_LinearlyDependentRemovedVariable <- colnames(DPA.Predictors.Numeric)[DPA_LinearlyDependent$remove[j]]
print(paste0("Variable ",
j,
" for removal: ",
DPA_LinearlyDependentRemovedVariable))
}
##################################
# Filtering out columns with linear dependency
#################################
DPA_ExcludedLinearlyDependent <- DPA[,-DPA_LinearlyDependent$remove]
##################################
# Gathering descriptive statistics
##################################
(DPA_ExcludedLinearlyDependent_Skimmed <- skim(DPA_ExcludedLinearlyDependent))
}## [1] "Linear dependency can be resolved by removing 2 numeric variable(s)."
## [1] "Variable 1 for removal: NumNonHBonds"
## [1] "Variable 2 for removal: NumHydrogen"| Name | DPA_ExcludedLinearlyDepen… |
| Number of rows | 951 |
| Number of columns | 227 |
| _______________________ | |
| Column type frequency: | |
| factor | 1 |
| numeric | 226 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 524, Low: 427 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| FP001 | 0 | 1 | 0.49 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP002 | 0 | 1 | 0.54 | 0.50 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP003 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP004 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP005 | 0 | 1 | 0.58 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP006 | 0 | 1 | 0.40 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP007 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP008 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP009 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP010 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP011 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP012 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP013 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP014 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP015 | 0 | 1 | 0.86 | 0.35 | 0.00 | 1.00 | 1.00 | 1.00 | 1.00 | ▁▁▁▁▇ |
| FP016 | 0 | 1 | 0.15 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP017 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP018 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP019 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP020 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP021 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP022 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP023 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP024 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP025 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP026 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP027 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP028 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP029 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP030 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP031 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP032 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP033 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP034 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP035 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP036 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP037 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP038 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP039 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP040 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP041 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP042 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP043 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP044 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP045 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP046 | 0 | 1 | 0.32 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP047 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP048 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP049 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP050 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP051 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP052 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP053 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP054 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP055 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP056 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP057 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP058 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP059 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP060 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP061 | 0 | 1 | 0.45 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP062 | 0 | 1 | 0.44 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP063 | 0 | 1 | 0.43 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP064 | 0 | 1 | 0.42 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▆ |
| FP065 | 0 | 1 | 0.59 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▆▁▁▁▇ |
| FP066 | 0 | 1 | 0.61 | 0.49 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP067 | 0 | 1 | 0.38 | 0.49 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP068 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP069 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP070 | 0 | 1 | 0.36 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP071 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP072 | 0 | 1 | 0.66 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP073 | 0 | 1 | 0.31 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP074 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP075 | 0 | 1 | 0.34 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP076 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP077 | 0 | 1 | 0.32 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP078 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP079 | 0 | 1 | 0.69 | 0.46 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP080 | 0 | 1 | 0.30 | 0.46 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP081 | 0 | 1 | 0.28 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP082 | 0 | 1 | 0.71 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP083 | 0 | 1 | 0.27 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP084 | 0 | 1 | 0.29 | 0.45 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP085 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP086 | 0 | 1 | 0.27 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP087 | 0 | 1 | 0.73 | 0.45 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▃▁▁▁▇ |
| FP088 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP089 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP090 | 0 | 1 | 0.25 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP091 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP092 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP093 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP094 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP095 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP096 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP097 | 0 | 1 | 0.24 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP098 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP099 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP100 | 0 | 1 | 0.23 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP101 | 0 | 1 | 0.24 | 0.43 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP102 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP103 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP104 | 0 | 1 | 0.22 | 0.42 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP105 | 0 | 1 | 0.22 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP106 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP107 | 0 | 1 | 0.21 | 0.41 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP108 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP109 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP110 | 0 | 1 | 0.21 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP111 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP112 | 0 | 1 | 0.19 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP113 | 0 | 1 | 0.20 | 0.40 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP114 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP115 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP116 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP117 | 0 | 1 | 0.18 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP118 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP119 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP120 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP121 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP122 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP123 | 0 | 1 | 0.17 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP124 | 0 | 1 | 0.16 | 0.37 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP125 | 0 | 1 | 0.16 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP126 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP127 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP128 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP129 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP130 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP131 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP132 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP133 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP134 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP135 | 0 | 1 | 0.13 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP136 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP137 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP138 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP139 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP140 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP141 | 0 | 1 | 0.12 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP142 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP143 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP144 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP145 | 0 | 1 | 0.10 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP146 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP147 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP148 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP149 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP150 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP151 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP152 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP153 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP154 | 0 | 1 | 0.04 | 0.19 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP155 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP156 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP157 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP158 | 0 | 1 | 0.05 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP159 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP160 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP161 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP162 | 0 | 1 | 0.50 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP163 | 0 | 1 | 0.48 | 0.50 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▇ |
| FP164 | 0 | 1 | 0.63 | 0.48 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP165 | 0 | 1 | 0.35 | 0.48 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▅ |
| FP166 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP167 | 0 | 1 | 0.33 | 0.47 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP168 | 0 | 1 | 0.67 | 0.47 | 0.00 | 0.00 | 1.00 | 1.00 | 1.00 | ▅▁▁▁▇ |
| FP169 | 0 | 1 | 0.19 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP170 | 0 | 1 | 0.18 | 0.39 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP171 | 0 | 1 | 0.17 | 0.38 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP172 | 0 | 1 | 0.15 | 0.36 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▂ |
| FP173 | 0 | 1 | 0.14 | 0.35 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP174 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP175 | 0 | 1 | 0.13 | 0.34 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP176 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP177 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP178 | 0 | 1 | 0.12 | 0.33 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP179 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP180 | 0 | 1 | 0.11 | 0.31 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP181 | 0 | 1 | 0.09 | 0.29 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP182 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP183 | 0 | 1 | 0.08 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP184 | 0 | 1 | 0.08 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP185 | 0 | 1 | 0.09 | 0.28 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP186 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP187 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP188 | 0 | 1 | 0.07 | 0.25 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP189 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP190 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP191 | 0 | 1 | 0.07 | 0.26 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP192 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP193 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP194 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP195 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP196 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP197 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP198 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP199 | 0 | 1 | 0.05 | 0.21 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP200 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP201 | 0 | 1 | 0.05 | 0.22 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP202 | 0 | 1 | 0.26 | 0.44 | 0.00 | 0.00 | 0.00 | 1.00 | 1.00 | ▇▁▁▁▃ |
| FP203 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP204 | 0 | 1 | 0.10 | 0.30 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP205 | 0 | 1 | 0.08 | 0.27 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP206 | 0 | 1 | 0.06 | 0.24 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP207 | 0 | 1 | 0.06 | 0.23 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| FP208 | 0 | 1 | 0.11 | 0.32 | 0.00 | 0.00 | 0.00 | 0.00 | 1.00 | ▇▁▁▁▁ |
| MolWeight | 0 | 1 | 201.65 | 97.91 | 46.09 | 122.60 | 179.23 | 264.34 | 665.81 | ▇▆▂▁▁ |
| NumAtoms | 0 | 1 | 25.51 | 12.61 | 5.00 | 17.00 | 22.00 | 31.00 | 94.00 | ▇▆▂▁▁ |
| NumNonHAtoms | 0 | 1 | 13.16 | 6.50 | 2.00 | 8.00 | 12.00 | 17.00 | 47.00 | ▇▆▂▁▁ |
| NumBonds | 0 | 1 | 25.91 | 13.48 | 4.00 | 17.00 | 23.00 | 31.50 | 97.00 | ▇▇▂▁▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumCarbon | 0 | 1 | 9.89 | 5.29 | 1.00 | 6.00 | 9.00 | 12.00 | 33.00 | ▇▇▃▁▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA_ExcludedLinearlyDependent)## [1] 951 2271.3.5 Shape Transformation
[A] A number of numeric variables in the dataset were observed to be right-skewed which required shape transformation for data distribution stability. Considering that all numeric variables were strictly positive values, the BoxCox method from the caret package was used to transform their distributional shapes.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,-(grep("FP", names(DPA.Predictors)))]
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
##################################
# Gathering descriptive statistics
##################################
(DPA_BoxCoxTransformedSkimmed <- skim(DPA_BoxCoxTransformed))| Name | DPA_BoxCoxTransformed |
| Number of rows | 951 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| numeric | 20 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MolWeight | 0 | 1 | 5.19 | 0.48 | 3.83 | 4.81 | 5.19 | 5.58 | 6.50 | ▁▆▇▆▁ |
| NumAtoms | 0 | 1 | 3.13 | 0.48 | 1.61 | 2.83 | 3.09 | 3.43 | 4.54 | ▁▃▇▃▁ |
| NumNonHAtoms | 0 | 1 | 2.46 | 0.50 | 0.69 | 2.08 | 2.48 | 2.83 | 3.85 | ▁▃▇▇▁ |
| NumBonds | 0 | 1 | 4.39 | 0.96 | 1.60 | 3.81 | 4.36 | 4.97 | 7.48 | ▁▅▇▃▁ |
| NumNonHBonds | 0 | 1 | 3.21 | 0.95 | 0.00 | 2.58 | 3.22 | 3.91 | 5.93 | ▁▃▇▆▁ |
| NumMultBonds | 0 | 1 | 6.15 | 5.17 | 0.00 | 1.00 | 6.00 | 10.00 | 25.00 | ▇▆▃▁▁ |
| NumRotBonds | 0 | 1 | 2.25 | 2.41 | 0.00 | 0.00 | 2.00 | 3.50 | 16.00 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 1.01 | 1.21 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 5.12 | 5.26 | 0.00 | 0.00 | 6.00 | 6.00 | 25.00 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 12.35 | 7.32 | 0.00 | 7.00 | 11.00 | 16.00 | 47.00 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 3.54 | 1.34 | 0.00 | 2.62 | 3.52 | 4.25 | 7.62 | ▂▇▇▃▁ |
| NumNitrogen | 0 | 1 | 0.81 | 1.19 | 0.00 | 0.00 | 0.00 | 1.00 | 6.00 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 1.57 | 1.73 | 0.00 | 0.00 | 1.00 | 2.00 | 13.00 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.16 | 0.49 | 0.00 | 0.00 | 0.00 | 0.00 | 4.00 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.56 | 1.40 | 0.00 | 0.00 | 0.00 | 0.00 | 10.00 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.70 | 1.47 | 0.00 | 0.00 | 0.00 | 1.00 | 10.00 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 1.40 | 1.30 | 0.00 | 0.00 | 1.00 | 2.00 | 7.00 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | -0.02 | 1.13 | -0.98 | -0.76 | -0.31 | 0.31 | 13.48 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 36.46 | 35.29 | 0.00 | 9.23 | 29.10 | 53.28 | 331.94 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 40.23 | 38.12 | 0.00 | 10.63 | 33.12 | 60.66 | 331.94 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA_BoxCoxTransformed)## [1] 951 201.3.6 Centering and Scaling
[A] To maintain numerical stability during modelling, centering and scaling transformations were applied on the transformed numeric variables. The center method from the caret package was implemented which subtracts the average value of a numeric variable to all the values. As a result of centering, the variables had zero mean values. In addition, the scale method, also from the caret package, was applied which performs a center transformation with each value of the variable divided by its standard deviation. Scaling the data coerced the values to have a common standard deviation of one.
Code Chunk | Output
##################################
# Loading dataset
##################################
DPA <- Solubility_Train
##################################
# Listing all predictors
##################################
DPA.Predictors <- DPA[,!names(DPA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
DPA.Predictors.Numeric <- DPA.Predictors[,-(grep("FP", names(DPA.Predictors)))]
##################################
# Applying a Box-Cox transformation
##################################
DPA_BoxCox <- preProcess(DPA.Predictors.Numeric, method = c("BoxCox"))
DPA_BoxCoxTransformed <- predict(DPA_BoxCox, DPA.Predictors.Numeric)
##################################
# Applying a center and scale data transformation
##################################
DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaled <- preProcess(DPA_BoxCoxTransformed, method = c("center","scale"))
DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed <- predict(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaled, DPA_BoxCoxTransformed)
##################################
# Gathering descriptive statistics
##################################
(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformedSkimmed <- skim(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed))| Name | DPA.Predictors.Numeric_Bo… |
| Number of rows | 951 |
| Number of columns | 20 |
| _______________________ | |
| Column type frequency: | |
| numeric | 20 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MolWeight | 0 | 1 | 0 | 1 | -2.84 | -0.80 | -0.01 | 0.80 | 2.72 | ▁▆▇▆▁ |
| NumAtoms | 0 | 1 | 0 | 1 | -3.16 | -0.61 | -0.07 | 0.64 | 2.95 | ▁▃▇▃▁ |
| NumNonHAtoms | 0 | 1 | 0 | 1 | -3.53 | -0.76 | 0.06 | 0.75 | 2.79 | ▁▃▇▇▁ |
| NumBonds | 0 | 1 | 0 | 1 | -2.92 | -0.61 | -0.04 | 0.60 | 3.23 | ▁▅▇▃▁ |
| NumNonHBonds | 0 | 1 | 0 | 1 | -3.38 | -0.67 | 0.01 | 0.74 | 2.86 | ▁▃▇▆▁ |
| NumMultBonds | 0 | 1 | 0 | 1 | -1.19 | -1.00 | -0.03 | 0.74 | 3.65 | ▇▇▃▁▁ |
| NumRotBonds | 0 | 1 | 0 | 1 | -0.93 | -0.93 | -0.10 | 0.52 | 5.71 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 0 | 1 | -0.83 | -0.83 | -0.01 | 0.82 | 4.95 | ▇▂▁▁▁ |
| NumAromaticBonds | 0 | 1 | 0 | 1 | -0.97 | -0.97 | 0.17 | 0.17 | 3.78 | ▇▆▃▁▁ |
| NumHydrogen | 0 | 1 | 0 | 1 | -1.69 | -0.73 | -0.18 | 0.50 | 4.74 | ▇▇▂▁▁ |
| NumCarbon | 0 | 1 | 0 | 1 | -2.64 | -0.69 | -0.01 | 0.54 | 3.06 | ▂▇▇▃▁ |
| NumNitrogen | 0 | 1 | 0 | 1 | -0.69 | -0.69 | -0.69 | 0.16 | 4.37 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 0 | 1 | -0.91 | -0.91 | -0.33 | 0.25 | 6.61 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0 | 1 | -0.34 | -0.34 | -0.34 | -0.34 | 7.86 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0 | 1 | -0.40 | -0.40 | -0.40 | -0.40 | 6.74 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0 | 1 | -0.47 | -0.47 | -0.47 | 0.20 | 6.32 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 0 | 1 | -1.08 | -1.08 | -0.31 | 0.46 | 4.31 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | 0 | 1 | -0.86 | -0.66 | -0.26 | 0.30 | 11.99 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 0 | 1 | -1.03 | -0.77 | -0.21 | 0.48 | 8.37 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 0 | 1 | -1.06 | -0.78 | -0.19 | 0.54 | 7.65 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
###################################
dim(DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed)## [1] 951 201.3.7 Pre-Processed Dataset
[A] 1267 rows (observations)
[A.1] Train Set = 951 observations
[A.2] Test Set = 316 observations
[B] 221 columns (variables)
[B.1] 1/221 response = Log_Solubility_Class variable (factor)
[B.1.1] Levels = Log_Solubility_Class=Low < Log_Solubility_Class=High
[B.2] 220/221 predictors = All remaining variables (205/220 factor + 15/220 numeric)
[C] Pre-processing actions applied:
[C.1] Centering, scaling and shape transformation applied to improve data quality
[C.2] No outlier treatment applied since the high values noted were contextually valid and sensible
[C.3] 3 predictors removed due to zero or near-zero variance
[C.4] 3 predictors removed due to high correlation
[C.5] 2 predictors removed due to linear dependencies
Code Chunk | Output
##################################
# Creating the pre-modelling
# train set
##################################
Log_Solubility_Class <- DPA$Log_Solubility_Class
PMA.Predictors.Factor <- DPA.Predictors[,(grep("FP", names(DPA.Predictors)))]
PMA.Predictors.Factor <- as.data.frame(lapply(PMA.Predictors.Factor,factor))
PMA.Predictors.Numeric <- DPA.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed
PMA_BoxCoxTransformed_CenteredScaledTransformed <- cbind(Log_Solubility_Class,PMA.Predictors.Factor,PMA.Predictors.Numeric)
##################################
# Filtering out columns noted with data quality issues including
# zero and near-zero variance,
# high correlation and linear dependencies
# to create the pre-modelling dataset
##################################
PMA_BoxCoxTransformed_CenteredScaledTransformed_ExcludedLowVariance_ExcludedLinearlyDependent_ExcludedHighCorrelation <- PMA_BoxCoxTransformed_CenteredScaledTransformed[,!names(PMA_BoxCoxTransformed_CenteredScaledTransformed) %in% c("FP154","FP199","FP200","NumNonHBonds","NumHydrogen","NumNonHAtoms","NumAromaticBonds","NumAtoms")]
PMA_PreModelling_Train <- PMA_BoxCoxTransformed_CenteredScaledTransformed_ExcludedLowVariance_ExcludedLinearlyDependent_ExcludedHighCorrelation
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Train_Skimmed <- skim(PMA_PreModelling_Train))| Name | PMA_PreModelling_Train |
| Number of rows | 951 |
| Number of columns | 221 |
| _______________________ | |
| Column type frequency: | |
| factor | 206 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 524, Low: 427 |
| FP001 | 0 | 1 | FALSE | 2 | 0: 482, 1: 469 |
| FP002 | 0 | 1 | FALSE | 2 | 1: 513, 0: 438 |
| FP003 | 0 | 1 | FALSE | 2 | 0: 536, 1: 415 |
| FP004 | 0 | 1 | FALSE | 2 | 1: 556, 0: 395 |
| FP005 | 0 | 1 | FALSE | 2 | 1: 551, 0: 400 |
| FP006 | 0 | 1 | FALSE | 2 | 0: 570, 1: 381 |
| FP007 | 0 | 1 | FALSE | 2 | 0: 605, 1: 346 |
| FP008 | 0 | 1 | FALSE | 2 | 0: 641, 1: 310 |
| FP009 | 0 | 1 | FALSE | 2 | 0: 685, 1: 266 |
| FP010 | 0 | 1 | FALSE | 2 | 0: 781, 1: 170 |
| FP011 | 0 | 1 | FALSE | 2 | 0: 747, 1: 204 |
| FP012 | 0 | 1 | FALSE | 2 | 0: 783, 1: 168 |
| FP013 | 0 | 1 | FALSE | 2 | 0: 793, 1: 158 |
| FP014 | 0 | 1 | FALSE | 2 | 0: 798, 1: 153 |
| FP015 | 0 | 1 | FALSE | 2 | 1: 818, 0: 133 |
| FP016 | 0 | 1 | FALSE | 2 | 0: 812, 1: 139 |
| FP017 | 0 | 1 | FALSE | 2 | 0: 814, 1: 137 |
| FP018 | 0 | 1 | FALSE | 2 | 0: 826, 1: 125 |
| FP019 | 0 | 1 | FALSE | 2 | 0: 835, 1: 116 |
| FP020 | 0 | 1 | FALSE | 2 | 0: 837, 1: 114 |
| FP021 | 0 | 1 | FALSE | 2 | 0: 836, 1: 115 |
| FP022 | 0 | 1 | FALSE | 2 | 0: 852, 1: 99 |
| FP023 | 0 | 1 | FALSE | 2 | 0: 834, 1: 117 |
| FP024 | 0 | 1 | FALSE | 2 | 0: 844, 1: 107 |
| FP025 | 0 | 1 | FALSE | 2 | 0: 841, 1: 110 |
| FP026 | 0 | 1 | FALSE | 2 | 0: 871, 1: 80 |
| FP027 | 0 | 1 | FALSE | 2 | 0: 858, 1: 93 |
| FP028 | 0 | 1 | FALSE | 2 | 0: 850, 1: 101 |
| FP029 | 0 | 1 | FALSE | 2 | 0: 854, 1: 97 |
| FP030 | 0 | 1 | FALSE | 2 | 0: 862, 1: 89 |
| FP031 | 0 | 1 | FALSE | 2 | 0: 866, 1: 85 |
| FP032 | 0 | 1 | FALSE | 2 | 0: 881, 1: 70 |
| FP033 | 0 | 1 | FALSE | 2 | 0: 885, 1: 66 |
| FP034 | 0 | 1 | FALSE | 2 | 0: 875, 1: 76 |
| FP035 | 0 | 1 | FALSE | 2 | 0: 882, 1: 69 |
| FP036 | 0 | 1 | FALSE | 2 | 0: 879, 1: 72 |
| FP037 | 0 | 1 | FALSE | 2 | 0: 884, 1: 67 |
| FP038 | 0 | 1 | FALSE | 2 | 0: 869, 1: 82 |
| FP039 | 0 | 1 | FALSE | 2 | 0: 880, 1: 71 |
| FP040 | 0 | 1 | FALSE | 2 | 0: 886, 1: 65 |
| FP041 | 0 | 1 | FALSE | 2 | 0: 891, 1: 60 |
| FP042 | 0 | 1 | FALSE | 2 | 0: 897, 1: 54 |
| FP043 | 0 | 1 | FALSE | 2 | 0: 888, 1: 63 |
| FP044 | 0 | 1 | FALSE | 2 | 0: 894, 1: 57 |
| FP045 | 0 | 1 | FALSE | 2 | 0: 898, 1: 53 |
| FP046 | 0 | 1 | FALSE | 2 | 0: 651, 1: 300 |
| FP047 | 0 | 1 | FALSE | 2 | 0: 698, 1: 253 |
| FP048 | 0 | 1 | FALSE | 2 | 0: 833, 1: 118 |
| FP049 | 0 | 1 | FALSE | 2 | 0: 835, 1: 116 |
| FP050 | 0 | 1 | FALSE | 2 | 0: 844, 1: 107 |
| FP051 | 0 | 1 | FALSE | 2 | 0: 847, 1: 104 |
| FP052 | 0 | 1 | FALSE | 2 | 0: 864, 1: 87 |
| FP053 | 0 | 1 | FALSE | 2 | 0: 862, 1: 89 |
| FP054 | 0 | 1 | FALSE | 2 | 0: 879, 1: 72 |
| FP055 | 0 | 1 | FALSE | 2 | 0: 900, 1: 51 |
| FP056 | 0 | 1 | FALSE | 2 | 0: 889, 1: 62 |
| FP057 | 0 | 1 | FALSE | 2 | 0: 837, 1: 114 |
| FP058 | 0 | 1 | FALSE | 2 | 0: 843, 1: 108 |
| FP059 | 0 | 1 | FALSE | 2 | 0: 899, 1: 52 |
| FP060 | 0 | 1 | FALSE | 2 | 0: 493, 1: 458 |
| FP061 | 0 | 1 | FALSE | 2 | 0: 526, 1: 425 |
| FP062 | 0 | 1 | FALSE | 2 | 0: 535, 1: 416 |
| FP063 | 0 | 1 | FALSE | 2 | 0: 546, 1: 405 |
| FP064 | 0 | 1 | FALSE | 2 | 0: 555, 1: 396 |
| FP065 | 0 | 1 | FALSE | 2 | 1: 564, 0: 387 |
| FP066 | 0 | 1 | FALSE | 2 | 1: 580, 0: 371 |
| FP067 | 0 | 1 | FALSE | 2 | 0: 590, 1: 361 |
| FP068 | 0 | 1 | FALSE | 2 | 0: 607, 1: 344 |
| FP069 | 0 | 1 | FALSE | 2 | 0: 607, 1: 344 |
| FP070 | 0 | 1 | FALSE | 2 | 0: 613, 1: 338 |
| FP071 | 0 | 1 | FALSE | 2 | 0: 640, 1: 311 |
| FP072 | 0 | 1 | FALSE | 2 | 1: 626, 0: 325 |
| FP073 | 0 | 1 | FALSE | 2 | 0: 656, 1: 295 |
| FP074 | 0 | 1 | FALSE | 2 | 0: 642, 1: 309 |
| FP075 | 0 | 1 | FALSE | 2 | 0: 629, 1: 322 |
| FP076 | 0 | 1 | FALSE | 2 | 0: 639, 1: 312 |
| FP077 | 0 | 1 | FALSE | 2 | 0: 646, 1: 305 |
| FP078 | 0 | 1 | FALSE | 2 | 0: 662, 1: 289 |
| FP079 | 0 | 1 | FALSE | 2 | 1: 656, 0: 295 |
| FP080 | 0 | 1 | FALSE | 2 | 0: 663, 1: 288 |
| FP081 | 0 | 1 | FALSE | 2 | 0: 686, 1: 265 |
| FP082 | 0 | 1 | FALSE | 2 | 1: 679, 0: 272 |
| FP083 | 0 | 1 | FALSE | 2 | 0: 691, 1: 260 |
| FP084 | 0 | 1 | FALSE | 2 | 0: 679, 1: 272 |
| FP085 | 0 | 1 | FALSE | 2 | 0: 708, 1: 243 |
| FP086 | 0 | 1 | FALSE | 2 | 0: 695, 1: 256 |
| FP087 | 0 | 1 | FALSE | 2 | 1: 691, 0: 260 |
| FP088 | 0 | 1 | FALSE | 2 | 0: 701, 1: 250 |
| FP089 | 0 | 1 | FALSE | 2 | 0: 716, 1: 235 |
| FP090 | 0 | 1 | FALSE | 2 | 0: 714, 1: 237 |
| FP091 | 0 | 1 | FALSE | 2 | 0: 737, 1: 214 |
| FP092 | 0 | 1 | FALSE | 2 | 0: 719, 1: 232 |
| FP093 | 0 | 1 | FALSE | 2 | 0: 719, 1: 232 |
| FP094 | 0 | 1 | FALSE | 2 | 0: 731, 1: 220 |
| FP095 | 0 | 1 | FALSE | 2 | 0: 742, 1: 209 |
| FP096 | 0 | 1 | FALSE | 2 | 0: 744, 1: 207 |
| FP097 | 0 | 1 | FALSE | 2 | 0: 727, 1: 224 |
| FP098 | 0 | 1 | FALSE | 2 | 0: 725, 1: 226 |
| FP099 | 0 | 1 | FALSE | 2 | 0: 735, 1: 216 |
| FP100 | 0 | 1 | FALSE | 2 | 0: 731, 1: 220 |
| FP101 | 0 | 1 | FALSE | 2 | 0: 726, 1: 225 |
| FP102 | 0 | 1 | FALSE | 2 | 0: 759, 1: 192 |
| FP103 | 0 | 1 | FALSE | 2 | 0: 743, 1: 208 |
| FP104 | 0 | 1 | FALSE | 2 | 0: 739, 1: 212 |
| FP105 | 0 | 1 | FALSE | 2 | 0: 746, 1: 205 |
| FP106 | 0 | 1 | FALSE | 2 | 0: 769, 1: 182 |
| FP107 | 0 | 1 | FALSE | 2 | 0: 750, 1: 201 |
| FP108 | 0 | 1 | FALSE | 2 | 0: 756, 1: 195 |
| FP109 | 0 | 1 | FALSE | 2 | 0: 783, 1: 168 |
| FP110 | 0 | 1 | FALSE | 2 | 0: 755, 1: 196 |
| FP111 | 0 | 1 | FALSE | 2 | 0: 764, 1: 187 |
| FP112 | 0 | 1 | FALSE | 2 | 0: 766, 1: 185 |
| FP113 | 0 | 1 | FALSE | 2 | 0: 765, 1: 186 |
| FP114 | 0 | 1 | FALSE | 2 | 0: 803, 1: 148 |
| FP115 | 0 | 1 | FALSE | 2 | 0: 781, 1: 170 |
| FP116 | 0 | 1 | FALSE | 2 | 0: 768, 1: 183 |
| FP117 | 0 | 1 | FALSE | 2 | 0: 781, 1: 170 |
| FP118 | 0 | 1 | FALSE | 2 | 0: 768, 1: 183 |
| FP119 | 0 | 1 | FALSE | 2 | 0: 796, 1: 155 |
| FP120 | 0 | 1 | FALSE | 2 | 0: 793, 1: 158 |
| FP121 | 0 | 1 | FALSE | 2 | 0: 818, 1: 133 |
| FP122 | 0 | 1 | FALSE | 2 | 0: 795, 1: 156 |
| FP123 | 0 | 1 | FALSE | 2 | 0: 792, 1: 159 |
| FP124 | 0 | 1 | FALSE | 2 | 0: 797, 1: 154 |
| FP125 | 0 | 1 | FALSE | 2 | 0: 803, 1: 148 |
| FP126 | 0 | 1 | FALSE | 2 | 0: 810, 1: 141 |
| FP127 | 0 | 1 | FALSE | 2 | 0: 818, 1: 133 |
| FP128 | 0 | 1 | FALSE | 2 | 0: 810, 1: 141 |
| FP129 | 0 | 1 | FALSE | 2 | 0: 819, 1: 132 |
| FP130 | 0 | 1 | FALSE | 2 | 0: 851, 1: 100 |
| FP131 | 0 | 1 | FALSE | 2 | 0: 831, 1: 120 |
| FP132 | 0 | 1 | FALSE | 2 | 0: 832, 1: 119 |
| FP133 | 0 | 1 | FALSE | 2 | 0: 831, 1: 120 |
| FP134 | 0 | 1 | FALSE | 2 | 0: 830, 1: 121 |
| FP135 | 0 | 1 | FALSE | 2 | 0: 831, 1: 120 |
| FP136 | 0 | 1 | FALSE | 2 | 0: 836, 1: 115 |
| FP137 | 0 | 1 | FALSE | 2 | 0: 841, 1: 110 |
| FP138 | 0 | 1 | FALSE | 2 | 0: 845, 1: 106 |
| FP139 | 0 | 1 | FALSE | 2 | 0: 873, 1: 78 |
| FP140 | 0 | 1 | FALSE | 2 | 0: 845, 1: 106 |
| FP141 | 0 | 1 | FALSE | 2 | 0: 840, 1: 111 |
| FP142 | 0 | 1 | FALSE | 2 | 0: 847, 1: 104 |
| FP143 | 0 | 1 | FALSE | 2 | 0: 874, 1: 77 |
| FP144 | 0 | 1 | FALSE | 2 | 0: 852, 1: 99 |
| FP145 | 0 | 1 | FALSE | 2 | 0: 852, 1: 99 |
| FP146 | 0 | 1 | FALSE | 2 | 0: 853, 1: 98 |
| FP147 | 0 | 1 | FALSE | 2 | 0: 851, 1: 100 |
| FP148 | 0 | 1 | FALSE | 2 | 0: 868, 1: 83 |
| FP149 | 0 | 1 | FALSE | 2 | 0: 865, 1: 86 |
| FP150 | 0 | 1 | FALSE | 2 | 0: 876, 1: 75 |
| FP151 | 0 | 1 | FALSE | 2 | 0: 898, 1: 53 |
| FP152 | 0 | 1 | FALSE | 2 | 0: 873, 1: 78 |
| FP153 | 0 | 1 | FALSE | 2 | 0: 877, 1: 74 |
| FP155 | 0 | 1 | FALSE | 2 | 0: 885, 1: 66 |
| FP156 | 0 | 1 | FALSE | 2 | 0: 884, 1: 67 |
| FP157 | 0 | 1 | FALSE | 2 | 0: 892, 1: 59 |
| FP158 | 0 | 1 | FALSE | 2 | 0: 900, 1: 51 |
| FP159 | 0 | 1 | FALSE | 2 | 0: 884, 1: 67 |
| FP160 | 0 | 1 | FALSE | 2 | 0: 886, 1: 65 |
| FP161 | 0 | 1 | FALSE | 2 | 0: 888, 1: 63 |
| FP162 | 0 | 1 | FALSE | 2 | 0: 480, 1: 471 |
| FP163 | 0 | 1 | FALSE | 2 | 0: 498, 1: 453 |
| FP164 | 0 | 1 | FALSE | 2 | 1: 597, 0: 354 |
| FP165 | 0 | 1 | FALSE | 2 | 0: 619, 1: 332 |
| FP166 | 0 | 1 | FALSE | 2 | 0: 636, 1: 315 |
| FP167 | 0 | 1 | FALSE | 2 | 0: 639, 1: 312 |
| FP168 | 0 | 1 | FALSE | 2 | 1: 633, 0: 318 |
| FP169 | 0 | 1 | FALSE | 2 | 0: 774, 1: 177 |
| FP170 | 0 | 1 | FALSE | 2 | 0: 776, 1: 175 |
| FP171 | 0 | 1 | FALSE | 2 | 0: 790, 1: 161 |
| FP172 | 0 | 1 | FALSE | 2 | 0: 807, 1: 144 |
| FP173 | 0 | 1 | FALSE | 2 | 0: 816, 1: 135 |
| FP174 | 0 | 1 | FALSE | 2 | 0: 827, 1: 124 |
| FP175 | 0 | 1 | FALSE | 2 | 0: 823, 1: 128 |
| FP176 | 0 | 1 | FALSE | 2 | 0: 835, 1: 116 |
| FP177 | 0 | 1 | FALSE | 2 | 0: 836, 1: 115 |
| FP178 | 0 | 1 | FALSE | 2 | 0: 836, 1: 115 |
| FP179 | 0 | 1 | FALSE | 2 | 0: 858, 1: 93 |
| FP180 | 0 | 1 | FALSE | 2 | 0: 849, 1: 102 |
| FP181 | 0 | 1 | FALSE | 2 | 0: 862, 1: 89 |
| FP182 | 0 | 1 | FALSE | 2 | 0: 857, 1: 94 |
| FP183 | 0 | 1 | FALSE | 2 | 0: 879, 1: 72 |
| FP184 | 0 | 1 | FALSE | 2 | 0: 871, 1: 80 |
| FP185 | 0 | 1 | FALSE | 2 | 0: 870, 1: 81 |
| FP186 | 0 | 1 | FALSE | 2 | 0: 878, 1: 73 |
| FP187 | 0 | 1 | FALSE | 2 | 0: 882, 1: 69 |
| FP188 | 0 | 1 | FALSE | 2 | 0: 886, 1: 65 |
| FP189 | 0 | 1 | FALSE | 2 | 0: 878, 1: 73 |
| FP190 | 0 | 1 | FALSE | 2 | 0: 882, 1: 69 |
| FP191 | 0 | 1 | FALSE | 2 | 0: 884, 1: 67 |
| FP192 | 0 | 1 | FALSE | 2 | 0: 893, 1: 58 |
| FP193 | 0 | 1 | FALSE | 2 | 0: 892, 1: 59 |
| FP194 | 0 | 1 | FALSE | 2 | 0: 895, 1: 56 |
| FP195 | 0 | 1 | FALSE | 2 | 0: 893, 1: 58 |
| FP196 | 0 | 1 | FALSE | 2 | 0: 897, 1: 54 |
| FP197 | 0 | 1 | FALSE | 2 | 0: 901, 1: 50 |
| FP198 | 0 | 1 | FALSE | 2 | 0: 897, 1: 54 |
| FP201 | 0 | 1 | FALSE | 2 | 0: 901, 1: 50 |
| FP202 | 0 | 1 | FALSE | 2 | 0: 706, 1: 245 |
| FP203 | 0 | 1 | FALSE | 2 | 0: 842, 1: 109 |
| FP204 | 0 | 1 | FALSE | 2 | 0: 857, 1: 94 |
| FP205 | 0 | 1 | FALSE | 2 | 0: 877, 1: 74 |
| FP206 | 0 | 1 | FALSE | 2 | 0: 894, 1: 57 |
| FP207 | 0 | 1 | FALSE | 2 | 0: 897, 1: 54 |
| FP208 | 0 | 1 | FALSE | 2 | 0: 844, 1: 107 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MolWeight | 0 | 1 | 0 | 1 | -2.84 | -0.80 | -0.01 | 0.80 | 2.72 | ▁▆▇▆▁ |
| NumBonds | 0 | 1 | 0 | 1 | -2.92 | -0.61 | -0.04 | 0.60 | 3.23 | ▁▅▇▃▁ |
| NumMultBonds | 0 | 1 | 0 | 1 | -1.19 | -1.00 | -0.03 | 0.74 | 3.65 | ▇▇▃▁▁ |
| NumRotBonds | 0 | 1 | 0 | 1 | -0.93 | -0.93 | -0.10 | 0.52 | 5.71 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 0 | 1 | -0.83 | -0.83 | -0.01 | 0.82 | 4.95 | ▇▂▁▁▁ |
| NumCarbon | 0 | 1 | 0 | 1 | -2.64 | -0.69 | -0.01 | 0.54 | 3.06 | ▂▇▇▃▁ |
| NumNitrogen | 0 | 1 | 0 | 1 | -0.69 | -0.69 | -0.69 | 0.16 | 4.37 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 0 | 1 | -0.91 | -0.91 | -0.33 | 0.25 | 6.61 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0 | 1 | -0.34 | -0.34 | -0.34 | -0.34 | 7.86 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0 | 1 | -0.40 | -0.40 | -0.40 | -0.40 | 6.74 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0 | 1 | -0.47 | -0.47 | -0.47 | 0.20 | 6.32 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 0 | 1 | -1.08 | -1.08 | -0.31 | 0.46 | 4.31 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | 0 | 1 | -0.86 | -0.66 | -0.26 | 0.30 | 11.99 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 0 | 1 | -1.03 | -0.77 | -0.21 | 0.48 | 8.37 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 0 | 1 | -1.06 | -0.78 | -0.19 | 0.54 | 7.65 | ▇▂▁▁▁ |
###################################
# Verifying the data dimensions
# for the train set
###################################
dim(PMA_PreModelling_Train)## [1] 951 221##################################
# Formulating the test set
##################################
DPA_Test <- Solubility_Test
DPA_Test.Predictors <- DPA_Test[,!names(DPA_Test) %in% c("Log_Solubility_Class")]
DPA_Test.Predictors.Numeric <- DPA_Test.Predictors[,-(grep("FP", names(DPA_Test.Predictors)))]
DPA_Test_BoxCox <- preProcess(DPA_Test.Predictors.Numeric, method = c("BoxCox"))
DPA_Test_BoxCoxTransformed <- predict(DPA_Test_BoxCox, DPA_Test.Predictors.Numeric)
DPA_Test.Predictors.Numeric_BoxCoxTransformed_CenteredScaled <- preProcess(DPA_Test_BoxCoxTransformed, method = c("center","scale"))
DPA_Test.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed <- predict(DPA_Test.Predictors.Numeric_BoxCoxTransformed_CenteredScaled, DPA_Test_BoxCoxTransformed)
##################################
# Creating the pre-modelling
# test set
##################################
Log_Solubility_Class <- DPA_Test$Log_Solubility_Class
PMA_Test.Predictors.Factor <- DPA_Test.Predictors[,(grep("FP", names(DPA_Test.Predictors)))]
PMA_Test.Predictors.Factor <- as.data.frame(lapply(PMA_Test.Predictors.Factor,factor))
PMA_Test.Predictors.Numeric <- DPA_Test.Predictors.Numeric_BoxCoxTransformed_CenteredScaledTransformed
PMA_Test_BoxCoxTransformed_CenteredScaledTransformed <- cbind(Log_Solubility_Class,PMA_Test.Predictors.Factor,PMA_Test.Predictors.Numeric)
PMA_Test_BoxCoxTransformed_CenteredScaledTransformed_ExcludedLowVariance_ExcludedLinearlyDependent_ExcludedHighCorrelation <- PMA_Test_BoxCoxTransformed_CenteredScaledTransformed[,!names(PMA_Test_BoxCoxTransformed_CenteredScaledTransformed) %in% c("FP154","FP199","FP200","NumNonHBonds","NumHydrogen","NumNonHAtoms","NumAromaticBonds","NumAtoms")]
PMA_PreModelling_Test <- PMA_Test_BoxCoxTransformed_CenteredScaledTransformed_ExcludedLowVariance_ExcludedLinearlyDependent_ExcludedHighCorrelation
##################################
# Gathering descriptive statistics
##################################
(PMA_PreModelling_Test_Skimmed <- skim(PMA_PreModelling_Test))| Name | PMA_PreModelling_Test |
| Number of rows | 316 |
| Number of columns | 221 |
| _______________________ | |
| Column type frequency: | |
| factor | 206 |
| numeric | 15 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| Log_Solubility_Class | 0 | 1 | FALSE | 2 | Hig: 173, Low: 143 |
| FP001 | 0 | 1 | FALSE | 2 | 0: 168, 1: 148 |
| FP002 | 0 | 1 | FALSE | 2 | 1: 185, 0: 131 |
| FP003 | 0 | 1 | FALSE | 2 | 0: 176, 1: 140 |
| FP004 | 0 | 1 | FALSE | 2 | 1: 168, 0: 148 |
| FP005 | 0 | 1 | FALSE | 2 | 1: 195, 0: 121 |
| FP006 | 0 | 1 | FALSE | 2 | 0: 205, 1: 111 |
| FP007 | 0 | 1 | FALSE | 2 | 0: 204, 1: 112 |
| FP008 | 0 | 1 | FALSE | 2 | 0: 202, 1: 114 |
| FP009 | 0 | 1 | FALSE | 2 | 0: 233, 1: 83 |
| FP010 | 0 | 1 | FALSE | 2 | 0: 255, 1: 61 |
| FP011 | 0 | 1 | FALSE | 2 | 0: 261, 1: 55 |
| FP012 | 0 | 1 | FALSE | 2 | 0: 263, 1: 53 |
| FP013 | 0 | 1 | FALSE | 2 | 0: 264, 1: 52 |
| FP014 | 0 | 1 | FALSE | 2 | 0: 266, 1: 50 |
| FP015 | 0 | 1 | FALSE | 2 | 1: 262, 0: 54 |
| FP016 | 0 | 1 | FALSE | 2 | 0: 271, 1: 45 |
| FP017 | 0 | 1 | FALSE | 2 | 0: 269, 1: 47 |
| FP018 | 0 | 1 | FALSE | 2 | 0: 289, 1: 27 |
| FP019 | 0 | 1 | FALSE | 2 | 0: 280, 1: 36 |
| FP020 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP021 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP022 | 0 | 1 | FALSE | 2 | 0: 279, 1: 37 |
| FP023 | 0 | 1 | FALSE | 2 | 0: 289, 1: 27 |
| FP024 | 0 | 1 | FALSE | 2 | 0: 285, 1: 31 |
| FP025 | 0 | 1 | FALSE | 2 | 0: 291, 1: 25 |
| FP026 | 0 | 1 | FALSE | 2 | 0: 279, 1: 37 |
| FP027 | 0 | 1 | FALSE | 2 | 0: 291, 1: 25 |
| FP028 | 0 | 1 | FALSE | 2 | 0: 298, 1: 18 |
| FP029 | 0 | 1 | FALSE | 2 | 0: 300, 1: 16 |
| FP030 | 0 | 1 | FALSE | 2 | 0: 290, 1: 26 |
| FP031 | 0 | 1 | FALSE | 2 | 0: 285, 1: 31 |
| FP032 | 0 | 1 | FALSE | 2 | 0: 275, 1: 41 |
| FP033 | 0 | 1 | FALSE | 2 | 0: 278, 1: 38 |
| FP034 | 0 | 1 | FALSE | 2 | 0: 295, 1: 21 |
| FP035 | 0 | 1 | FALSE | 2 | 0: 285, 1: 31 |
| FP036 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP037 | 0 | 1 | FALSE | 2 | 0: 286, 1: 30 |
| FP038 | 0 | 1 | FALSE | 2 | 0: 306, 1: 10 |
| FP039 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP040 | 0 | 1 | FALSE | 2 | 0: 298, 1: 18 |
| FP041 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP042 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP043 | 0 | 1 | FALSE | 2 | 0: 302, 1: 14 |
| FP044 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP045 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP046 | 0 | 1 | FALSE | 2 | 0: 213, 1: 103 |
| FP047 | 0 | 1 | FALSE | 2 | 0: 222, 1: 94 |
| FP048 | 0 | 1 | FALSE | 2 | 0: 280, 1: 36 |
| FP049 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP050 | 0 | 1 | FALSE | 2 | 0: 280, 1: 36 |
| FP051 | 0 | 1 | FALSE | 2 | 0: 298, 1: 18 |
| FP052 | 0 | 1 | FALSE | 2 | 0: 283, 1: 33 |
| FP053 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP054 | 0 | 1 | FALSE | 2 | 0: 285, 1: 31 |
| FP055 | 0 | 1 | FALSE | 2 | 0: 287, 1: 29 |
| FP056 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP057 | 0 | 1 | FALSE | 2 | 0: 277, 1: 39 |
| FP058 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP059 | 0 | 1 | FALSE | 2 | 0: 302, 1: 14 |
| FP060 | 0 | 1 | FALSE | 2 | 0: 173, 1: 143 |
| FP061 | 0 | 1 | FALSE | 2 | 0: 192, 1: 124 |
| FP062 | 0 | 1 | FALSE | 2 | 0: 181, 1: 135 |
| FP063 | 0 | 1 | FALSE | 2 | 0: 203, 1: 113 |
| FP064 | 0 | 1 | FALSE | 2 | 0: 193, 1: 123 |
| FP065 | 0 | 1 | FALSE | 2 | 1: 189, 0: 127 |
| FP066 | 0 | 1 | FALSE | 2 | 1: 195, 0: 121 |
| FP067 | 0 | 1 | FALSE | 2 | 0: 213, 1: 103 |
| FP068 | 0 | 1 | FALSE | 2 | 0: 224, 1: 92 |
| FP069 | 0 | 1 | FALSE | 2 | 0: 198, 1: 118 |
| FP070 | 0 | 1 | FALSE | 2 | 0: 211, 1: 105 |
| FP071 | 0 | 1 | FALSE | 2 | 0: 207, 1: 109 |
| FP072 | 0 | 1 | FALSE | 2 | 1: 204, 0: 112 |
| FP073 | 0 | 1 | FALSE | 2 | 0: 224, 1: 92 |
| FP074 | 0 | 1 | FALSE | 2 | 0: 213, 1: 103 |
| FP075 | 0 | 1 | FALSE | 2 | 0: 235, 1: 81 |
| FP076 | 0 | 1 | FALSE | 2 | 0: 216, 1: 100 |
| FP077 | 0 | 1 | FALSE | 2 | 0: 219, 1: 97 |
| FP078 | 0 | 1 | FALSE | 2 | 0: 218, 1: 98 |
| FP079 | 0 | 1 | FALSE | 2 | 1: 230, 0: 86 |
| FP080 | 0 | 1 | FALSE | 2 | 0: 233, 1: 83 |
| FP081 | 0 | 1 | FALSE | 2 | 0: 225, 1: 91 |
| FP082 | 0 | 1 | FALSE | 2 | 1: 235, 0: 81 |
| FP083 | 0 | 1 | FALSE | 2 | 0: 236, 1: 80 |
| FP084 | 0 | 1 | FALSE | 2 | 0: 245, 1: 71 |
| FP085 | 0 | 1 | FALSE | 2 | 0: 231, 1: 85 |
| FP086 | 0 | 1 | FALSE | 2 | 0: 230, 1: 86 |
| FP087 | 0 | 1 | FALSE | 2 | 1: 241, 0: 75 |
| FP088 | 0 | 1 | FALSE | 2 | 0: 239, 1: 77 |
| FP089 | 0 | 1 | FALSE | 2 | 0: 236, 1: 80 |
| FP090 | 0 | 1 | FALSE | 2 | 0: 244, 1: 72 |
| FP091 | 0 | 1 | FALSE | 2 | 0: 243, 1: 73 |
| FP092 | 0 | 1 | FALSE | 2 | 0: 247, 1: 69 |
| FP093 | 0 | 1 | FALSE | 2 | 0: 248, 1: 68 |
| FP094 | 0 | 1 | FALSE | 2 | 0: 237, 1: 79 |
| FP095 | 0 | 1 | FALSE | 2 | 0: 251, 1: 65 |
| FP096 | 0 | 1 | FALSE | 2 | 0: 257, 1: 59 |
| FP097 | 0 | 1 | FALSE | 2 | 0: 250, 1: 66 |
| FP098 | 0 | 1 | FALSE | 2 | 0: 252, 1: 64 |
| FP099 | 0 | 1 | FALSE | 2 | 0: 249, 1: 67 |
| FP100 | 0 | 1 | FALSE | 2 | 0: 259, 1: 57 |
| FP101 | 0 | 1 | FALSE | 2 | 0: 260, 1: 56 |
| FP102 | 0 | 1 | FALSE | 2 | 0: 270, 1: 46 |
| FP103 | 0 | 1 | FALSE | 2 | 0: 247, 1: 69 |
| FP104 | 0 | 1 | FALSE | 2 | 0: 258, 1: 58 |
| FP105 | 0 | 1 | FALSE | 2 | 0: 248, 1: 68 |
| FP106 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP107 | 0 | 1 | FALSE | 2 | 0: 254, 1: 62 |
| FP108 | 0 | 1 | FALSE | 2 | 0: 259, 1: 57 |
| FP109 | 0 | 1 | FALSE | 2 | 0: 261, 1: 55 |
| FP110 | 0 | 1 | FALSE | 2 | 0: 264, 1: 52 |
| FP111 | 0 | 1 | FALSE | 2 | 0: 259, 1: 57 |
| FP112 | 0 | 1 | FALSE | 2 | 0: 260, 1: 56 |
| FP113 | 0 | 1 | FALSE | 2 | 0: 264, 1: 52 |
| FP114 | 0 | 1 | FALSE | 2 | 0: 260, 1: 56 |
| FP115 | 0 | 1 | FALSE | 2 | 0: 266, 1: 50 |
| FP116 | 0 | 1 | FALSE | 2 | 0: 269, 1: 47 |
| FP117 | 0 | 1 | FALSE | 2 | 0: 262, 1: 54 |
| FP118 | 0 | 1 | FALSE | 2 | 0: 279, 1: 37 |
| FP119 | 0 | 1 | FALSE | 2 | 0: 263, 1: 53 |
| FP120 | 0 | 1 | FALSE | 2 | 0: 267, 1: 49 |
| FP121 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP122 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP123 | 0 | 1 | FALSE | 2 | 0: 270, 1: 46 |
| FP124 | 0 | 1 | FALSE | 2 | 0: 274, 1: 42 |
| FP125 | 0 | 1 | FALSE | 2 | 0: 278, 1: 38 |
| FP126 | 0 | 1 | FALSE | 2 | 0: 280, 1: 36 |
| FP127 | 0 | 1 | FALSE | 2 | 0: 269, 1: 47 |
| FP128 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP129 | 0 | 1 | FALSE | 2 | 0: 272, 1: 44 |
| FP130 | 0 | 1 | FALSE | 2 | 0: 290, 1: 26 |
| FP131 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP132 | 0 | 1 | FALSE | 2 | 0: 276, 1: 40 |
| FP133 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP134 | 0 | 1 | FALSE | 2 | 0: 289, 1: 27 |
| FP135 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP136 | 0 | 1 | FALSE | 2 | 0: 284, 1: 32 |
| FP137 | 0 | 1 | FALSE | 2 | 0: 288, 1: 28 |
| FP138 | 0 | 1 | FALSE | 2 | 0: 290, 1: 26 |
| FP139 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP140 | 0 | 1 | FALSE | 2 | 0: 288, 1: 28 |
| FP141 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP142 | 0 | 1 | FALSE | 2 | 0: 286, 1: 30 |
| FP143 | 0 | 1 | FALSE | 2 | 0: 299, 1: 17 |
| FP144 | 0 | 1 | FALSE | 2 | 0: 287, 1: 29 |
| FP145 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP146 | 0 | 1 | FALSE | 2 | 0: 287, 1: 29 |
| FP147 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP148 | 0 | 1 | FALSE | 2 | 0: 291, 1: 25 |
| FP149 | 0 | 1 | FALSE | 2 | 0: 290, 1: 26 |
| FP150 | 0 | 1 | FALSE | 2 | 0: 295, 1: 21 |
| FP151 | 0 | 1 | FALSE | 2 | 0: 306, 1: 10 |
| FP152 | 0 | 1 | FALSE | 2 | 0: 299, 1: 17 |
| FP153 | 0 | 1 | FALSE | 2 | 0: 305, 1: 11 |
| FP155 | 0 | 1 | FALSE | 2 | 0: 295, 1: 21 |
| FP156 | 0 | 1 | FALSE | 2 | 0: 301, 1: 15 |
| FP157 | 0 | 1 | FALSE | 2 | 0: 298, 1: 18 |
| FP158 | 0 | 1 | FALSE | 2 | 0: 291, 1: 25 |
| FP159 | 0 | 1 | FALSE | 2 | 0: 305, 1: 11 |
| FP160 | 0 | 1 | FALSE | 2 | 0: 305, 1: 11 |
| FP161 | 0 | 1 | FALSE | 2 | 0: 305, 1: 11 |
| FP162 | 0 | 1 | FALSE | 2 | 1: 168, 0: 148 |
| FP163 | 0 | 1 | FALSE | 2 | 0: 173, 1: 143 |
| FP164 | 0 | 1 | FALSE | 2 | 1: 207, 0: 109 |
| FP165 | 0 | 1 | FALSE | 2 | 0: 215, 1: 101 |
| FP166 | 0 | 1 | FALSE | 2 | 0: 209, 1: 107 |
| FP167 | 0 | 1 | FALSE | 2 | 0: 221, 1: 95 |
| FP168 | 0 | 1 | FALSE | 2 | 1: 226, 0: 90 |
| FP169 | 0 | 1 | FALSE | 2 | 0: 257, 1: 59 |
| FP170 | 0 | 1 | FALSE | 2 | 0: 267, 1: 49 |
| FP171 | 0 | 1 | FALSE | 2 | 0: 275, 1: 41 |
| FP172 | 0 | 1 | FALSE | 2 | 0: 269, 1: 47 |
| FP173 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP174 | 0 | 1 | FALSE | 2 | 0: 267, 1: 49 |
| FP175 | 0 | 1 | FALSE | 2 | 0: 274, 1: 42 |
| FP176 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP177 | 0 | 1 | FALSE | 2 | 0: 284, 1: 32 |
| FP178 | 0 | 1 | FALSE | 2 | 0: 282, 1: 34 |
| FP179 | 0 | 1 | FALSE | 2 | 0: 272, 1: 44 |
| FP180 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP181 | 0 | 1 | FALSE | 2 | 0: 283, 1: 33 |
| FP182 | 0 | 1 | FALSE | 2 | 0: 292, 1: 24 |
| FP183 | 0 | 1 | FALSE | 2 | 0: 274, 1: 42 |
| FP184 | 0 | 1 | FALSE | 2 | 0: 286, 1: 30 |
| FP185 | 0 | 1 | FALSE | 2 | 0: 285, 1: 31 |
| FP186 | 0 | 1 | FALSE | 2 | 0: 297, 1: 19 |
| FP187 | 0 | 1 | FALSE | 2 | 0: 295, 1: 21 |
| FP188 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP189 | 0 | 1 | FALSE | 2 | 0: 303, 1: 13 |
| FP190 | 0 | 1 | FALSE | 2 | 0: 299, 1: 17 |
| FP191 | 0 | 1 | FALSE | 2 | 0: 298, 1: 18 |
| FP192 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP193 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP194 | 0 | 1 | FALSE | 2 | 0: 295, 1: 21 |
| FP195 | 0 | 1 | FALSE | 2 | 0: 300, 1: 16 |
| FP196 | 0 | 1 | FALSE | 2 | 0: 294, 1: 22 |
| FP197 | 0 | 1 | FALSE | 2 | 0: 296, 1: 20 |
| FP198 | 0 | 1 | FALSE | 2 | 0: 302, 1: 14 |
| FP201 | 0 | 1 | FALSE | 2 | 0: 303, 1: 13 |
| FP202 | 0 | 1 | FALSE | 2 | 0: 232, 1: 84 |
| FP203 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
| FP204 | 0 | 1 | FALSE | 2 | 0: 286, 1: 30 |
| FP205 | 0 | 1 | FALSE | 2 | 0: 291, 1: 25 |
| FP206 | 0 | 1 | FALSE | 2 | 0: 300, 1: 16 |
| FP207 | 0 | 1 | FALSE | 2 | 0: 302, 1: 14 |
| FP208 | 0 | 1 | FALSE | 2 | 0: 273, 1: 43 |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| MolWeight | 0 | 1 | 0 | 1 | -2.46 | -0.78 | -0.06 | 0.81 | 2.18 | ▁▇▇▇▃ |
| NumBonds | 0 | 1 | 0 | 1 | -2.92 | -0.67 | 0.03 | 0.57 | 2.55 | ▁▂▇▃▂ |
| NumMultBonds | 0 | 1 | 0 | 1 | -1.24 | -1.04 | -0.06 | 0.72 | 4.06 | ▇▇▅▁▁ |
| NumRotBonds | 0 | 1 | 0 | 1 | -0.82 | -0.82 | -0.40 | 0.44 | 5.94 | ▇▁▁▁▁ |
| NumDblBonds | 0 | 1 | 0 | 1 | -0.76 | -0.76 | 0.09 | 0.09 | 4.35 | ▇▁▁▁▁ |
| NumCarbon | 0 | 1 | 0 | 1 | -2.71 | -0.70 | -0.21 | 0.56 | 2.23 | ▁▂▇▅▂ |
| NumNitrogen | 0 | 1 | 0 | 1 | -0.63 | -0.63 | -0.63 | 0.26 | 4.71 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 0 | 1 | -0.92 | -0.92 | -0.26 | 0.40 | 5.02 | ▇▃▁▁▁ |
| NumSulfer | 0 | 1 | 0 | 1 | -0.28 | -0.28 | -0.28 | -0.28 | 8.06 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0 | 1 | -0.40 | -0.40 | -0.40 | -0.40 | 6.02 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0 | 1 | -0.48 | -0.48 | -0.48 | 0.20 | 5.57 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 0 | 1 | -1.14 | -0.32 | -0.32 | 0.49 | 3.74 | ▇▃▁▁▁ |
| HydrophilicFactor | 0 | 1 | 0 | 1 | -0.90 | -0.68 | -0.30 | 0.32 | 5.19 | ▇▂▁▁▁ |
| SurfaceArea1 | 0 | 1 | 0 | 1 | -1.04 | -0.75 | -0.21 | 0.53 | 5.37 | ▇▃▁▁▁ |
| SurfaceArea2 | 0 | 1 | 0 | 1 | -1.05 | -0.77 | -0.26 | 0.52 | 5.00 | ▇▃▁▁▁ |
###################################
# Verifying the data dimensions
# for the test set
###################################
dim(PMA_PreModelling_Test)## [1] 316 2211.4 Data Exploration
[A] Numeric variables which demonstrated differential relationships with the Log_Solubility_Class response variable include:
[A.1] MolWeight variable (numeric)
[A.2] NumCarbon variable (numeric)
[A.3] NumChlorine variable (numeric)
[A.4] NumHalogen variable (numeric)
[A.5] NumMultBonds variable (numeric)
[B] Factor variables which demonstrated relatively better differentiation of the Log_Solubility_Class response variable between its 1 and 0 structure levels include:
[B.1] FP207 variable (factor)
[B.2] FP190 variable (factor)
[B.3] FP197 variable (factor)
[B.4] FP196 variable (factor)
[B.5] FP193 variable (factor)
[B.6] FP184 variable (factor)
[B.7] FP172 variable (factor)
[B.8] FP149 variable (factor)
[B.9] FP112 variable (factor)
[B.10] FP107 variable (factor)
[B.11] FP089 variable (factor)
[B.12] FP079 variable (factor)
[B.13] FP076 variable (factor)
[B.14] FP072 variable (factor)
[B.15] FP071 variable (factor)
[B.16] FP070 variable (factor)
[B.17] FP065 variable (factor)
[B.18] FP059 variable (factor)
[B.19] FP054 variable (factor)
[B.20] FP056 variable (factor)
[B.21] FP053 variable (factor)
[B.22] FP049 variable (factor)
[B.23] FP044 variable (factor)
[B.24] FP041 variable (factor)
[B.25] FP039 variable (factor)
[B.26] FP014 variable (factor)
[B.27] FP013 variable (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
EDA <- PMA_PreModelling_Train
##################################
# Listing all predictors
##################################
EDA.Predictors <- EDA[,!names(EDA) %in% c("Log_Solubility_Class")]
##################################
# Listing all numeric predictors
##################################
EDA.Predictors.Numeric <- EDA.Predictors[,sapply(EDA.Predictors, is.numeric)]
ncol(EDA.Predictors.Numeric)## [1] 15names(EDA.Predictors.Numeric)## [1] "MolWeight" "NumBonds" "NumMultBonds"
## [4] "NumRotBonds" "NumDblBonds" "NumCarbon"
## [7] "NumNitrogen" "NumOxygen" "NumSulfer"
## [10] "NumChlorine" "NumHalogen" "NumRings"
## [13] "HydrophilicFactor" "SurfaceArea1" "SurfaceArea2"##################################
# Listing all factor predictors
##################################
EDA.Predictors.Factor <- EDA.Predictors[,sapply(EDA.Predictors, is.factor)]
ncol(EDA.Predictors.Factor)## [1] 205names(EDA.Predictors.Factor)## [1] "FP001" "FP002" "FP003" "FP004" "FP005" "FP006" "FP007" "FP008" "FP009"
## [10] "FP010" "FP011" "FP012" "FP013" "FP014" "FP015" "FP016" "FP017" "FP018"
## [19] "FP019" "FP020" "FP021" "FP022" "FP023" "FP024" "FP025" "FP026" "FP027"
## [28] "FP028" "FP029" "FP030" "FP031" "FP032" "FP033" "FP034" "FP035" "FP036"
## [37] "FP037" "FP038" "FP039" "FP040" "FP041" "FP042" "FP043" "FP044" "FP045"
## [46] "FP046" "FP047" "FP048" "FP049" "FP050" "FP051" "FP052" "FP053" "FP054"
## [55] "FP055" "FP056" "FP057" "FP058" "FP059" "FP060" "FP061" "FP062" "FP063"
## [64] "FP064" "FP065" "FP066" "FP067" "FP068" "FP069" "FP070" "FP071" "FP072"
## [73] "FP073" "FP074" "FP075" "FP076" "FP077" "FP078" "FP079" "FP080" "FP081"
## [82] "FP082" "FP083" "FP084" "FP085" "FP086" "FP087" "FP088" "FP089" "FP090"
## [91] "FP091" "FP092" "FP093" "FP094" "FP095" "FP096" "FP097" "FP098" "FP099"
## [100] "FP100" "FP101" "FP102" "FP103" "FP104" "FP105" "FP106" "FP107" "FP108"
## [109] "FP109" "FP110" "FP111" "FP112" "FP113" "FP114" "FP115" "FP116" "FP117"
## [118] "FP118" "FP119" "FP120" "FP121" "FP122" "FP123" "FP124" "FP125" "FP126"
## [127] "FP127" "FP128" "FP129" "FP130" "FP131" "FP132" "FP133" "FP134" "FP135"
## [136] "FP136" "FP137" "FP138" "FP139" "FP140" "FP141" "FP142" "FP143" "FP144"
## [145] "FP145" "FP146" "FP147" "FP148" "FP149" "FP150" "FP151" "FP152" "FP153"
## [154] "FP155" "FP156" "FP157" "FP158" "FP159" "FP160" "FP161" "FP162" "FP163"
## [163] "FP164" "FP165" "FP166" "FP167" "FP168" "FP169" "FP170" "FP171" "FP172"
## [172] "FP173" "FP174" "FP175" "FP176" "FP177" "FP178" "FP179" "FP180" "FP181"
## [181] "FP182" "FP183" "FP184" "FP185" "FP186" "FP187" "FP188" "FP189" "FP190"
## [190] "FP191" "FP192" "FP193" "FP194" "FP195" "FP196" "FP197" "FP198" "FP201"
## [199] "FP202" "FP203" "FP204" "FP205" "FP206" "FP207" "FP208"##################################
# Formulating the box plots
##################################
featurePlot(x = EDA.Predictors.Numeric,
y = EDA$Log_Solubility_Class,
plot = "box",
scales = list(x = list(relation="free", rot = 90),
y = list(relation="free")),
adjust = 1.5,
pch = "|")
##################################
# Restructuring the dataset for
# for barchart analysis
##################################
Log_Solubility_Class <- DPA$Log_Solubility_Class
EDA.Bar.Source <- as.data.frame(cbind(Log_Solubility_Class,
EDA.Predictors.Factor))
ncol(EDA.Bar.Source)## [1] 206##################################
# Creating a function to formulate
# the proportions table
##################################
EDA.PropTable.Function <- function(FactorVar) {
EDA.Bar.Source.FactorVar <- EDA.Bar.Source[,c("Log_Solubility_Class",
FactorVar)]
EDA.Bar.Source.FactorVar.Prop <- as.data.frame(prop.table(table(EDA.Bar.Source.FactorVar), 2))
names(EDA.Bar.Source.FactorVar.Prop)[2] <- "Structure"
EDA.Bar.Source.FactorVar.Prop$Variable <- rep(FactorVar,nrow(EDA.Bar.Source.FactorVar.Prop))
return(EDA.Bar.Source.FactorVar.Prop)
}
EDA.Bar.Source.FactorVar.Prop.Group5 <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[162]),
EDA.PropTable.Function(names(EDA.Bar.Source)[163]),
EDA.PropTable.Function(names(EDA.Bar.Source)[164]),
EDA.PropTable.Function(names(EDA.Bar.Source)[165]),
EDA.PropTable.Function(names(EDA.Bar.Source)[166]),
EDA.PropTable.Function(names(EDA.Bar.Source)[167]),
EDA.PropTable.Function(names(EDA.Bar.Source)[168]),
EDA.PropTable.Function(names(EDA.Bar.Source)[169]),
EDA.PropTable.Function(names(EDA.Bar.Source)[170]),
EDA.PropTable.Function(names(EDA.Bar.Source)[171]),
EDA.PropTable.Function(names(EDA.Bar.Source)[172]),
EDA.PropTable.Function(names(EDA.Bar.Source)[173]),
EDA.PropTable.Function(names(EDA.Bar.Source)[174]),
EDA.PropTable.Function(names(EDA.Bar.Source)[175]),
EDA.PropTable.Function(names(EDA.Bar.Source)[176]),
EDA.PropTable.Function(names(EDA.Bar.Source)[177]),
EDA.PropTable.Function(names(EDA.Bar.Source)[178]),
EDA.PropTable.Function(names(EDA.Bar.Source)[179]),
EDA.PropTable.Function(names(EDA.Bar.Source)[180]),
EDA.PropTable.Function(names(EDA.Bar.Source)[181]),
EDA.PropTable.Function(names(EDA.Bar.Source)[182]),
EDA.PropTable.Function(names(EDA.Bar.Source)[183]),
EDA.PropTable.Function(names(EDA.Bar.Source)[184]),
EDA.PropTable.Function(names(EDA.Bar.Source)[185]),
EDA.PropTable.Function(names(EDA.Bar.Source)[186]),
EDA.PropTable.Function(names(EDA.Bar.Source)[187]),
EDA.PropTable.Function(names(EDA.Bar.Source)[188]),
EDA.PropTable.Function(names(EDA.Bar.Source)[189]),
EDA.PropTable.Function(names(EDA.Bar.Source)[190]),
EDA.PropTable.Function(names(EDA.Bar.Source)[191]),
EDA.PropTable.Function(names(EDA.Bar.Source)[192]),
EDA.PropTable.Function(names(EDA.Bar.Source)[193]),
EDA.PropTable.Function(names(EDA.Bar.Source)[194]),
EDA.PropTable.Function(names(EDA.Bar.Source)[195]),
EDA.PropTable.Function(names(EDA.Bar.Source)[196]),
EDA.PropTable.Function(names(EDA.Bar.Source)[197]),
EDA.PropTable.Function(names(EDA.Bar.Source)[198]),
EDA.PropTable.Function(names(EDA.Bar.Source)[199]),
EDA.PropTable.Function(names(EDA.Bar.Source)[200]),
EDA.PropTable.Function(names(EDA.Bar.Source)[201]),
EDA.PropTable.Function(names(EDA.Bar.Source)[202]),
EDA.PropTable.Function(names(EDA.Bar.Source)[203]),
EDA.PropTable.Function(names(EDA.Bar.Source)[204]),
EDA.PropTable.Function(names(EDA.Bar.Source)[205]),
EDA.PropTable.Function(names(EDA.Bar.Source)[206]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group5[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group5[,2] | EDA.Bar.Source.FactorVar.Prop.Group5[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group5,
groups = EDA.Bar.Source.FactorVar.Prop.Group5[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj=1, space="top", columns=2),
layout=(c(9,5))))
EDA.Bar.Source.FactorVar.Prop.Group4 <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[122]),
EDA.PropTable.Function(names(EDA.Bar.Source)[123]),
EDA.PropTable.Function(names(EDA.Bar.Source)[124]),
EDA.PropTable.Function(names(EDA.Bar.Source)[125]),
EDA.PropTable.Function(names(EDA.Bar.Source)[126]),
EDA.PropTable.Function(names(EDA.Bar.Source)[127]),
EDA.PropTable.Function(names(EDA.Bar.Source)[128]),
EDA.PropTable.Function(names(EDA.Bar.Source)[129]),
EDA.PropTable.Function(names(EDA.Bar.Source)[130]),
EDA.PropTable.Function(names(EDA.Bar.Source)[131]),
EDA.PropTable.Function(names(EDA.Bar.Source)[132]),
EDA.PropTable.Function(names(EDA.Bar.Source)[133]),
EDA.PropTable.Function(names(EDA.Bar.Source)[134]),
EDA.PropTable.Function(names(EDA.Bar.Source)[135]),
EDA.PropTable.Function(names(EDA.Bar.Source)[136]),
EDA.PropTable.Function(names(EDA.Bar.Source)[137]),
EDA.PropTable.Function(names(EDA.Bar.Source)[138]),
EDA.PropTable.Function(names(EDA.Bar.Source)[139]),
EDA.PropTable.Function(names(EDA.Bar.Source)[140]),
EDA.PropTable.Function(names(EDA.Bar.Source)[141]),
EDA.PropTable.Function(names(EDA.Bar.Source)[142]),
EDA.PropTable.Function(names(EDA.Bar.Source)[143]),
EDA.PropTable.Function(names(EDA.Bar.Source)[144]),
EDA.PropTable.Function(names(EDA.Bar.Source)[145]),
EDA.PropTable.Function(names(EDA.Bar.Source)[146]),
EDA.PropTable.Function(names(EDA.Bar.Source)[147]),
EDA.PropTable.Function(names(EDA.Bar.Source)[148]),
EDA.PropTable.Function(names(EDA.Bar.Source)[149]),
EDA.PropTable.Function(names(EDA.Bar.Source)[150]),
EDA.PropTable.Function(names(EDA.Bar.Source)[151]),
EDA.PropTable.Function(names(EDA.Bar.Source)[152]),
EDA.PropTable.Function(names(EDA.Bar.Source)[153]),
EDA.PropTable.Function(names(EDA.Bar.Source)[154]),
EDA.PropTable.Function(names(EDA.Bar.Source)[155]),
EDA.PropTable.Function(names(EDA.Bar.Source)[156]),
EDA.PropTable.Function(names(EDA.Bar.Source)[157]),
EDA.PropTable.Function(names(EDA.Bar.Source)[158]),
EDA.PropTable.Function(names(EDA.Bar.Source)[159]),
EDA.PropTable.Function(names(EDA.Bar.Source)[160]),
EDA.PropTable.Function(names(EDA.Bar.Source)[161]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group4[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group4[,2] | EDA.Bar.Source.FactorVar.Prop.Group4[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group4,
groups = EDA.Bar.Source.FactorVar.Prop.Group4[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj=1, space="top", columns=2),
layout=(c(9,5))))
EDA.Bar.Source.FactorVar.Prop.Group3 <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[82]),
EDA.PropTable.Function(names(EDA.Bar.Source)[83]),
EDA.PropTable.Function(names(EDA.Bar.Source)[84]),
EDA.PropTable.Function(names(EDA.Bar.Source)[85]),
EDA.PropTable.Function(names(EDA.Bar.Source)[86]),
EDA.PropTable.Function(names(EDA.Bar.Source)[87]),
EDA.PropTable.Function(names(EDA.Bar.Source)[88]),
EDA.PropTable.Function(names(EDA.Bar.Source)[89]),
EDA.PropTable.Function(names(EDA.Bar.Source)[90]),
EDA.PropTable.Function(names(EDA.Bar.Source)[91]),
EDA.PropTable.Function(names(EDA.Bar.Source)[92]),
EDA.PropTable.Function(names(EDA.Bar.Source)[93]),
EDA.PropTable.Function(names(EDA.Bar.Source)[94]),
EDA.PropTable.Function(names(EDA.Bar.Source)[95]),
EDA.PropTable.Function(names(EDA.Bar.Source)[96]),
EDA.PropTable.Function(names(EDA.Bar.Source)[97]),
EDA.PropTable.Function(names(EDA.Bar.Source)[98]),
EDA.PropTable.Function(names(EDA.Bar.Source)[99]),
EDA.PropTable.Function(names(EDA.Bar.Source)[100]),
EDA.PropTable.Function(names(EDA.Bar.Source)[101]),
EDA.PropTable.Function(names(EDA.Bar.Source)[102]),
EDA.PropTable.Function(names(EDA.Bar.Source)[103]),
EDA.PropTable.Function(names(EDA.Bar.Source)[104]),
EDA.PropTable.Function(names(EDA.Bar.Source)[105]),
EDA.PropTable.Function(names(EDA.Bar.Source)[106]),
EDA.PropTable.Function(names(EDA.Bar.Source)[107]),
EDA.PropTable.Function(names(EDA.Bar.Source)[108]),
EDA.PropTable.Function(names(EDA.Bar.Source)[109]),
EDA.PropTable.Function(names(EDA.Bar.Source)[110]),
EDA.PropTable.Function(names(EDA.Bar.Source)[111]),
EDA.PropTable.Function(names(EDA.Bar.Source)[112]),
EDA.PropTable.Function(names(EDA.Bar.Source)[113]),
EDA.PropTable.Function(names(EDA.Bar.Source)[114]),
EDA.PropTable.Function(names(EDA.Bar.Source)[115]),
EDA.PropTable.Function(names(EDA.Bar.Source)[116]),
EDA.PropTable.Function(names(EDA.Bar.Source)[117]),
EDA.PropTable.Function(names(EDA.Bar.Source)[118]),
EDA.PropTable.Function(names(EDA.Bar.Source)[119]),
EDA.PropTable.Function(names(EDA.Bar.Source)[120]),
EDA.PropTable.Function(names(EDA.Bar.Source)[121]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group3[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group3[,2] | EDA.Bar.Source.FactorVar.Prop.Group3[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group3,
groups = EDA.Bar.Source.FactorVar.Prop.Group3[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj=1, space="top", columns=2),
layout=(c(9,5))))
EDA.Bar.Source.FactorVar.Prop.Group2 <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[42]),
EDA.PropTable.Function(names(EDA.Bar.Source)[43]),
EDA.PropTable.Function(names(EDA.Bar.Source)[44]),
EDA.PropTable.Function(names(EDA.Bar.Source)[45]),
EDA.PropTable.Function(names(EDA.Bar.Source)[46]),
EDA.PropTable.Function(names(EDA.Bar.Source)[47]),
EDA.PropTable.Function(names(EDA.Bar.Source)[48]),
EDA.PropTable.Function(names(EDA.Bar.Source)[49]),
EDA.PropTable.Function(names(EDA.Bar.Source)[50]),
EDA.PropTable.Function(names(EDA.Bar.Source)[51]),
EDA.PropTable.Function(names(EDA.Bar.Source)[52]),
EDA.PropTable.Function(names(EDA.Bar.Source)[53]),
EDA.PropTable.Function(names(EDA.Bar.Source)[54]),
EDA.PropTable.Function(names(EDA.Bar.Source)[55]),
EDA.PropTable.Function(names(EDA.Bar.Source)[56]),
EDA.PropTable.Function(names(EDA.Bar.Source)[57]),
EDA.PropTable.Function(names(EDA.Bar.Source)[58]),
EDA.PropTable.Function(names(EDA.Bar.Source)[59]),
EDA.PropTable.Function(names(EDA.Bar.Source)[60]),
EDA.PropTable.Function(names(EDA.Bar.Source)[61]),
EDA.PropTable.Function(names(EDA.Bar.Source)[62]),
EDA.PropTable.Function(names(EDA.Bar.Source)[63]),
EDA.PropTable.Function(names(EDA.Bar.Source)[64]),
EDA.PropTable.Function(names(EDA.Bar.Source)[65]),
EDA.PropTable.Function(names(EDA.Bar.Source)[66]),
EDA.PropTable.Function(names(EDA.Bar.Source)[67]),
EDA.PropTable.Function(names(EDA.Bar.Source)[68]),
EDA.PropTable.Function(names(EDA.Bar.Source)[69]),
EDA.PropTable.Function(names(EDA.Bar.Source)[70]),
EDA.PropTable.Function(names(EDA.Bar.Source)[71]),
EDA.PropTable.Function(names(EDA.Bar.Source)[72]),
EDA.PropTable.Function(names(EDA.Bar.Source)[73]),
EDA.PropTable.Function(names(EDA.Bar.Source)[74]),
EDA.PropTable.Function(names(EDA.Bar.Source)[75]),
EDA.PropTable.Function(names(EDA.Bar.Source)[76]),
EDA.PropTable.Function(names(EDA.Bar.Source)[77]),
EDA.PropTable.Function(names(EDA.Bar.Source)[78]),
EDA.PropTable.Function(names(EDA.Bar.Source)[79]),
EDA.PropTable.Function(names(EDA.Bar.Source)[80]),
EDA.PropTable.Function(names(EDA.Bar.Source)[81]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group2[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group2[,2] | EDA.Bar.Source.FactorVar.Prop.Group2[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group2,
groups = EDA.Bar.Source.FactorVar.Prop.Group2[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj=1, space="top", columns=2),
layout=(c(9,5))))
EDA.Bar.Source.FactorVar.Prop.Group1 <- rbind(EDA.PropTable.Function(names(EDA.Bar.Source)[2]),
EDA.PropTable.Function(names(EDA.Bar.Source)[3]),
EDA.PropTable.Function(names(EDA.Bar.Source)[4]),
EDA.PropTable.Function(names(EDA.Bar.Source)[5]),
EDA.PropTable.Function(names(EDA.Bar.Source)[6]),
EDA.PropTable.Function(names(EDA.Bar.Source)[7]),
EDA.PropTable.Function(names(EDA.Bar.Source)[8]),
EDA.PropTable.Function(names(EDA.Bar.Source)[9]),
EDA.PropTable.Function(names(EDA.Bar.Source)[10]),
EDA.PropTable.Function(names(EDA.Bar.Source)[11]),
EDA.PropTable.Function(names(EDA.Bar.Source)[12]),
EDA.PropTable.Function(names(EDA.Bar.Source)[13]),
EDA.PropTable.Function(names(EDA.Bar.Source)[14]),
EDA.PropTable.Function(names(EDA.Bar.Source)[15]),
EDA.PropTable.Function(names(EDA.Bar.Source)[16]),
EDA.PropTable.Function(names(EDA.Bar.Source)[17]),
EDA.PropTable.Function(names(EDA.Bar.Source)[18]),
EDA.PropTable.Function(names(EDA.Bar.Source)[19]),
EDA.PropTable.Function(names(EDA.Bar.Source)[20]),
EDA.PropTable.Function(names(EDA.Bar.Source)[21]),
EDA.PropTable.Function(names(EDA.Bar.Source)[22]),
EDA.PropTable.Function(names(EDA.Bar.Source)[23]),
EDA.PropTable.Function(names(EDA.Bar.Source)[24]),
EDA.PropTable.Function(names(EDA.Bar.Source)[25]),
EDA.PropTable.Function(names(EDA.Bar.Source)[26]),
EDA.PropTable.Function(names(EDA.Bar.Source)[27]),
EDA.PropTable.Function(names(EDA.Bar.Source)[28]),
EDA.PropTable.Function(names(EDA.Bar.Source)[29]),
EDA.PropTable.Function(names(EDA.Bar.Source)[30]),
EDA.PropTable.Function(names(EDA.Bar.Source)[31]),
EDA.PropTable.Function(names(EDA.Bar.Source)[32]),
EDA.PropTable.Function(names(EDA.Bar.Source)[33]),
EDA.PropTable.Function(names(EDA.Bar.Source)[34]),
EDA.PropTable.Function(names(EDA.Bar.Source)[35]),
EDA.PropTable.Function(names(EDA.Bar.Source)[36]),
EDA.PropTable.Function(names(EDA.Bar.Source)[37]),
EDA.PropTable.Function(names(EDA.Bar.Source)[38]),
EDA.PropTable.Function(names(EDA.Bar.Source)[39]),
EDA.PropTable.Function(names(EDA.Bar.Source)[40]),
EDA.PropTable.Function(names(EDA.Bar.Source)[41]))
(EDA.Barchart.FactorVar <- barchart(EDA.Bar.Source.FactorVar.Prop.Group1[,3] ~
EDA.Bar.Source.FactorVar.Prop.Group1[,2] | EDA.Bar.Source.FactorVar.Prop.Group1[,4],
data=EDA.Bar.Source.FactorVar.Prop.Group1,
groups = EDA.Bar.Source.FactorVar.Prop.Group1[,1],
stack=TRUE,
ylab = "Proportion",
xlab = "Structure",
auto.key = list(adj=1, space="top", columns=2),
layout=(c(9,5))))
1.5 Model-Independent Feature Importance Metrics
1.5.1 Area Under The Receiver Operating Characteristics Curve (AUROC)
Area Under The Receiver Operating Characteristics Curve involves a plot that demonstrates the performance of a classification prediction model to discriminate between two levels of a categorical response when compared to a gold standard, using the true positive rate (or Sensitivity) and the false positive rate (or 1-Specificity) for a range of threshold settings. The area under the ROC curve measures its discrimination power which is the ability of the model to distinguish between two classes or groups. The presence of high AUROC indicates the univariate discrimination of the categorical response by the numeric predictors.
[A] The area under the receiver operating characteristics curve metric was obtained using the caret package. High raw values of the metric indicate that the numeric predictors were associated with the factor response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.84419
[B.2] NumCarbon = 0.84088
[B.3] NumBonds = 0.78932
[B.4] NumRings = 0.74385
[B.5] NumMultBonds = 0.72301
Code Chunk | Output
##################################
# Filtering in the numeric predictors
# with the factor response variable
##################################
PMA_PreModelling_Train_Numeric <- PMA_PreModelling_Train[,!grepl("FP", names(PMA_PreModelling_Train))]
dim(PMA_PreModelling_Train_Numeric)## [1] 951 16str(PMA_PreModelling_Train_Numeric)## 'data.frame': 951 obs. of 16 variables:
## $ Log_Solubility_Class: Factor w/ 2 levels "Low","High": 1 1 1 1 1 1 1 1 1 1 ...
## $ MolWeight : num 0.304 1.475 0.284 -0.579 0.508 ...
## $ NumBonds : num 0.498 1.698 0.697 0.207 0.566 ...
## $ NumMultBonds : num 1.9049 1.3248 0.1647 -0.8021 -0.0287 ...
## $ NumRotBonds : num -0.935 0.726 0.726 -0.52 1.141 ...
## $ NumDblBonds : num -0.83134 -0.83134 -0.00521 0.82092 -0.83134 ...
## $ NumCarbon : num 0.858 1.804 0.701 0.182 -0.012 ...
## $ NumNitrogen : num 1.001 1.844 -0.685 -0.685 3.53 ...
## $ NumOxygen : num -0.91 -0.332 0.246 -0.91 -0.91 ...
## $ NumSulfer : num -0.336 1.712 -0.336 -0.336 -0.336 ...
## $ NumChlorine : num -0.397 -0.397 -0.397 -0.397 0.317 ...
## $ NumHalogen : num -0.474 -0.474 -0.474 -0.474 0.205 ...
## $ NumRings : num 1.231 2.001 -0.309 -0.309 -0.309 ...
## $ HydrophilicFactor : num -0.742 -0.31 -0.275 -0.834 -0.043 ...
## $ SurfaceArea1 : num -0.3026 0.4458 0.0238 -1.0332 0.4954 ...
## $ SurfaceArea2 : num -0.379 1.054 -0.077 -1.055 0.36 ...summary(PMA_PreModelling_Train_Numeric)## Log_Solubility_Class MolWeight NumBonds NumMultBonds
## Low :427 Min. :-2.835229 Min. :-2.9239 Min. :-1.18881
## High:524 1st Qu.:-0.798923 1st Qu.:-0.6096 1st Qu.:-0.99545
## Median :-0.008626 Median :-0.0356 Median :-0.02867
## Mean : 0.000000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.800109 3rd Qu.: 0.5994 3rd Qu.: 0.74476
## Max. : 2.722778 Max. : 3.2279 Max. : 3.64511
## NumRotBonds NumDblBonds NumCarbon NumNitrogen
## Min. :-0.9347 Min. :-0.831342 Min. :-2.64433 Min. :-0.6852
## 1st Qu.:-0.9347 1st Qu.:-0.831342 1st Qu.:-0.68596 1st Qu.:-0.6852
## Median :-0.1043 Median :-0.005212 Median :-0.01199 Median :-0.6852
## Mean : 0.0000 Mean : 0.000000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.5184 3rd Qu.: 0.820917 3rd Qu.: 0.53700 3rd Qu.: 0.1578
## Max. : 5.7083 Max. : 4.951564 Max. : 3.05604 Max. : 4.3730
## NumOxygen NumSulfer NumChlorine NumHalogen
## Min. :-0.9103 Min. :-0.336 Min. :-0.3972 Min. :-0.4741
## 1st Qu.:-0.9103 1st Qu.:-0.336 1st Qu.:-0.3972 1st Qu.:-0.4741
## Median :-0.3320 Median :-0.336 Median :-0.3972 Median :-0.4741
## Mean : 0.0000 Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2463 3rd Qu.:-0.336 3rd Qu.:-0.3972 3rd Qu.: 0.2049
## Max. : 6.6077 Max. : 7.858 Max. : 6.7442 Max. : 6.3162
## NumRings HydrophilicFactor SurfaceArea1 SurfaceArea2
## Min. :-1.0792 Min. :-0.8565 Min. :-1.0332 Min. :-1.0554
## 1st Qu.:-1.0792 1st Qu.:-0.6593 1st Qu.:-0.7716 1st Qu.:-0.7765
## Median :-0.3093 Median :-0.2606 Median :-0.2085 Median :-0.1866
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.4607 3rd Qu.: 0.2963 3rd Qu.: 0.4767 3rd Qu.: 0.5358
## Max. : 4.3103 Max. :11.9924 Max. : 8.3733 Max. : 7.6515##################################
# Obtaining the ROC AUC
##################################
AUROC <- filterVarImp(x = PMA_PreModelling_Train_Numeric[, 2:ncol(PMA_PreModelling_Train_Numeric)],
y = PMA_PreModelling_Train_Numeric$Log_Solubility_Class)
##################################
# Formulating the summary table
##################################
AUROC_Summary <- AUROC
AUROC_Summary$Predictor <- rownames(AUROC)
names(AUROC_Summary)[1] <- "AUROC"
AUROC_Summary$Metric <- rep("AUROC",nrow(AUROC))
AUROC_Summary## AUROC High Predictor Metric
## MolWeight 0.8441930 0.8441930 MolWeight AUROC
## NumBonds 0.7893255 0.7893255 NumBonds AUROC
## NumMultBonds 0.7230098 0.7230098 NumMultBonds AUROC
## NumRotBonds 0.5847695 0.5847695 NumRotBonds AUROC
## NumDblBonds 0.5142035 0.5142035 NumDblBonds AUROC
## NumCarbon 0.8408857 0.8408857 NumCarbon AUROC
## NumNitrogen 0.5285634 0.5285634 NumNitrogen AUROC
## NumOxygen 0.5536988 0.5536988 NumOxygen AUROC
## NumSulfer 0.5317902 0.5317902 NumSulfer AUROC
## NumChlorine 0.6104904 0.6104904 NumChlorine AUROC
## NumHalogen 0.6381755 0.6381755 NumHalogen AUROC
## NumRings 0.7438569 0.7438569 NumRings AUROC
## HydrophilicFactor 0.6699367 0.6699367 HydrophilicFactor AUROC
## SurfaceArea1 0.5726889 0.5726889 SurfaceArea1 AUROC
## SurfaceArea2 0.5479915 0.5479915 SurfaceArea2 AUROC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ AUROC | Metric,
AUROC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.5.2 Absolute T-Test Statistic (ATS)
T-Test Statistic, as derived from implementing a T-Test, measures the magnitude of the difference between the two group means being compared and is used to estimate the likelihood that this difference exists purely by chance. The test of hypothesis involved applying T-Test to evaluate for any significant differences in the means of the numeric predictors between the two classes of the categorical response. The presence of high T-Test statistic indicates the univariate discrimination of the categorical response by the numeric predictors.
[A] The absolute T-test statistic was obtained using the stats package. High absolute values of the metric indicate that the numeric predictors were associated with the factor response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 22.06757
[B.2] NumCarbon = 21.00363
[B.3] NumBonds = 16.49077
[B.4] NumRings = 14.39568
[B.5] NumMultBonds = 13.09205
Code Chunk | Output
##################################
# Obtaining the t-test statistics
##################################
ATS <- apply(PMA_PreModelling_Train_Numeric[, 2:ncol(PMA_PreModelling_Train_Numeric)],
2,
function(x, y){
tStats <- t.test(x ~ y)[c("statistic", "p.value", "estimate")]
unlist(tStats)},
y=PMA_PreModelling_Train_Numeric$Log_Solubility_Class)
##################################
# Formulating the summary table
##################################
ATS_Summary <- as.data.frame(t(ATS))
names(ATS_Summary) <- c("t.Statistic", "t.Test_P.Value", "Mean0", "Mean1")
ATS_Summary$Predictor <- names(PMA_PreModelling_Train_Numeric[,-1])
ATS_Summary$Metric <- rep("ATS", nrow(ATS_Summary))
ATS_Summary$ATS <- abs(ATS_Summary$t.Statistic)
ATS_Summary## t.Statistic t.Test_P.Value Mean0 Mean1
## MolWeight 22.0675750 8.568100e-87 0.64470512 -0.52536085
## NumBonds 16.4907720 2.071672e-53 0.52608866 -0.42870202
## NumMultBonds 13.0920514 2.095787e-35 0.44544021 -0.36298277
## NumRotBonds 5.8690062 6.747824e-09 0.21555838 -0.17565539
## NumDblBonds 1.9672950 4.949190e-02 0.07217700 -0.05881599
## NumCarbon 21.0036355 8.156177e-80 0.62632485 -0.51038304
## NumNitrogen -0.8247139 4.097543e-01 -0.02976857 0.02425798
## NumOxygen -0.9312510 3.519946e-01 -0.03406840 0.02776184
## NumSulfer 3.2021823 1.427379e-03 0.11971764 -0.09755617
## NumChlorine 8.3362925 7.315159e-16 0.31187920 -0.25414584
## NumHalogen 9.1800512 9.409615e-19 0.33850129 -0.27583979
## NumRings 14.3956856 1.979115e-41 0.48410734 -0.39449205
## HydrophilicFactor -7.3321483 5.053677e-13 -0.24599149 0.20045490
## SurfaceArea1 -2.5597702 1.063897e-02 -0.09225436 0.07517674
## SurfaceArea2 -1.1459666 2.521288e-01 -0.04168274 0.03396666
## Predictor Metric ATS
## MolWeight MolWeight ATS 22.0675750
## NumBonds NumBonds ATS 16.4907720
## NumMultBonds NumMultBonds ATS 13.0920514
## NumRotBonds NumRotBonds ATS 5.8690062
## NumDblBonds NumDblBonds ATS 1.9672950
## NumCarbon NumCarbon ATS 21.0036355
## NumNitrogen NumNitrogen ATS 0.8247139
## NumOxygen NumOxygen ATS 0.9312510
## NumSulfer NumSulfer ATS 3.2021823
## NumChlorine NumChlorine ATS 8.3362925
## NumHalogen NumHalogen ATS 9.1800512
## NumRings NumRings ATS 14.3956856
## HydrophilicFactor HydrophilicFactor ATS 7.3321483
## SurfaceArea1 SurfaceArea1 ATS 2.5597702
## SurfaceArea2 SurfaceArea2 ATS 1.1459666##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ ATS | Metric,
ATS_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.5.3 Maximal Information Coefficient (MIC)
Maximal Information Coefficient is an information theory-based measure of two-variable dependence through the computation of the mutual information normalized by the minimum joint entropy. It evaluates the strength of linear or non-linear association using binning as a means to apply mutual information between continuous random variables and selecting the maximum over many possible grids. The presence of high coefficient values indicate the univariate association between the numeric predictors and the categorical response.
[A] The maximal information coefficient was obtained using the minerva package. High raw values of the metric indicate that the numeric predictors were associated with the factor response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.46368
[B.2] HydrophilicFactor = 0.32734
[B.3] NumCarbon = 0.31544
[B.4] NumBonds = 0.28498
[B.5] SurfaceArea2 = 0.31618
Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
MIC <- mine(x = PMA_PreModelling_Train_Numeric[, 2:ncol(PMA_PreModelling_Train_Numeric)],
y = ifelse(PMA_PreModelling_Train_Numeric$Log_Solubility_Class=="High",1,0))$MIC
##################################
# Formulating the summary table
##################################
MIC_Summary <- data.frame(Predictor = names(PMA_PreModelling_Train_Numeric)[2:ncol(PMA_PreModelling_Train_Numeric)],
MIC = MIC[,1],
Metric = rep("MIC", length(MIC)))
MIC_Summary## Predictor MIC Metric
## 1 MolWeight 0.46368934 MIC
## 2 NumBonds 0.28498465 MIC
## 3 NumMultBonds 0.19005119 MIC
## 4 NumRotBonds 0.03861351 MIC
## 5 NumDblBonds 0.01133118 MIC
## 6 NumCarbon 0.31544845 MIC
## 7 NumNitrogen 0.01995159 MIC
## 8 NumOxygen 0.07491145 MIC
## 9 NumSulfer 0.01254889 MIC
## 10 NumChlorine 0.07010055 MIC
## 11 NumHalogen 0.08459159 MIC
## 12 NumRings 0.16421351 MIC
## 13 HydrophilicFactor 0.32734124 MIC
## 14 SurfaceArea1 0.19959108 MIC
## 15 SurfaceArea2 0.22267432 MIC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ MIC | Metric,
MIC_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.5.4 Relief Values (RV)
Relief Values are heuristic measures which estimate the quality of variables according to how well their values compare to instances that are near to each other, but are efficient in detecting contextual information even with strong dependencies between attributes. Given a randomly selected instance, its two nearest neighbors are selected - one from the same class (called nearest hit) and the other from a different class (called nearest miss). The quality for an attribute is estimated depending on the difference in probabilities obtained using the hits and misses. The presence of high relief values indicate the univariate association between the numeric predictors and the categorical response.
[A] The relief values statistic was obtained using the CORElearn package. High raw values of the metric indicate that the numeric predictors were associated with the factor response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.30039
[B.2] NumCarbon = 0.28938
[B.3] NumRings = 0.20600
[B.4] NumBonds = 0.20575
[B.5] NumNitrogen = 0.10800
Code Chunk | Output
summary(PMA_PreModelling_Train_Numeric)## Log_Solubility_Class MolWeight NumBonds NumMultBonds
## Low :427 Min. :-2.835229 Min. :-2.9239 Min. :-1.18881
## High:524 1st Qu.:-0.798923 1st Qu.:-0.6096 1st Qu.:-0.99545
## Median :-0.008626 Median :-0.0356 Median :-0.02867
## Mean : 0.000000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: 0.800109 3rd Qu.: 0.5994 3rd Qu.: 0.74476
## Max. : 2.722778 Max. : 3.2279 Max. : 3.64511
## NumRotBonds NumDblBonds NumCarbon NumNitrogen
## Min. :-0.9347 Min. :-0.831342 Min. :-2.64433 Min. :-0.6852
## 1st Qu.:-0.9347 1st Qu.:-0.831342 1st Qu.:-0.68596 1st Qu.:-0.6852
## Median :-0.1043 Median :-0.005212 Median :-0.01199 Median :-0.6852
## Mean : 0.0000 Mean : 0.000000 Mean : 0.00000 Mean : 0.0000
## 3rd Qu.: 0.5184 3rd Qu.: 0.820917 3rd Qu.: 0.53700 3rd Qu.: 0.1578
## Max. : 5.7083 Max. : 4.951564 Max. : 3.05604 Max. : 4.3730
## NumOxygen NumSulfer NumChlorine NumHalogen
## Min. :-0.9103 Min. :-0.336 Min. :-0.3972 Min. :-0.4741
## 1st Qu.:-0.9103 1st Qu.:-0.336 1st Qu.:-0.3972 1st Qu.:-0.4741
## Median :-0.3320 Median :-0.336 Median :-0.3972 Median :-0.4741
## Mean : 0.0000 Mean : 0.000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.2463 3rd Qu.:-0.336 3rd Qu.:-0.3972 3rd Qu.: 0.2049
## Max. : 6.6077 Max. : 7.858 Max. : 6.7442 Max. : 6.3162
## NumRings HydrophilicFactor SurfaceArea1 SurfaceArea2
## Min. :-1.0792 Min. :-0.8565 Min. :-1.0332 Min. :-1.0554
## 1st Qu.:-1.0792 1st Qu.:-0.6593 1st Qu.:-0.7716 1st Qu.:-0.7765
## Median :-0.3093 Median :-0.2606 Median :-0.2085 Median :-0.1866
## Mean : 0.0000 Mean : 0.0000 Mean : 0.0000 Mean : 0.0000
## 3rd Qu.: 0.4607 3rd Qu.: 0.2963 3rd Qu.: 0.4767 3rd Qu.: 0.5358
## Max. : 4.3103 Max. :11.9924 Max. : 8.3733 Max. : 7.6515##################################
# Obtaining the relief values
##################################
set.seed(12345678)
RV <- attrEval(Log_Solubility_Class ~ .,
data = PMA_PreModelling_Train_Numeric,
estimator = "ReliefFequalK",
ReliefIterations = 50)
##################################
# Formulating the summary table
##################################
RV_Summary <- data.frame(Predictor = names(RV),
RV = RV,
Metric = rep("RV", length(RV)))
RV_Summary## Predictor RV Metric
## MolWeight MolWeight 0.30039075 RV
## NumBonds NumBonds 0.20575373 RV
## NumMultBonds NumMultBonds 0.08333333 RV
## NumRotBonds NumRotBonds 0.08825000 RV
## NumDblBonds NumDblBonds 0.00200000 RV
## NumCarbon NumCarbon 0.28938624 RV
## NumNitrogen NumNitrogen 0.10800000 RV
## NumOxygen NumOxygen 0.07476923 RV
## NumSulfer NumSulfer 0.01600000 RV
## NumChlorine NumChlorine 0.01400000 RV
## NumHalogen NumHalogen 0.08400000 RV
## NumRings NumRings 0.20600000 RV
## HydrophilicFactor HydrophilicFactor 0.04048816 RV
## SurfaceArea1 SurfaceArea1 0.05630307 RV
## SurfaceArea2 SurfaceArea2 0.08079488 RV##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ RV | Metric,
RV_Summary,
origin = 0,
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.5.5 Volcano Plot - Fisher’s Exact Test (VP_F)
Fisher’s Exact Test tests contingency tables for homogeneity of proportion and determines if there are nonrandom associations between two categorical variables. To compute the P-value of the test, the tables must be ordered by some criterion that measures dependence, and those tables that represent equal or greater deviation from independence than the observed table are the ones whose probabilities are added together. The presence of low Fisher’s exact test p-values indicate the univariate association between the categorical predictors and the categorical response.
[A] The volcano plot - Fisher’s Exact Test p-value was obtained using the stats package. High raw values of the negative logarithm of the metric, and the absolute log odds ratio indicate that the factor predictors were associated with the factor response.
[B] The factor predictors which demonstrated the best feature importance in terms of the negative logarithm of the Fisher’s Exact Test p-value are as follows:
[B.1] FP076 = 44.20850
[B.2] FP089 = 31.66852
[B.3] FP112 = 27.56836
[B.4] FP172 = 23.86142
[B.5] FP065 = 23.62888
[C] The factor predictors which demonstrated the best feature importance in terms of the absolute log odds ratio are as follows:
[C.1] FP044 = 2.911268
[C.2] FP193 = 2.50206
[C.3] FP172 = 2.09503
[C.4] FP076 = 2.08379
[C.5] FP184 = 1.98819
Code Chunk | Output
##################################
# Filtering in the factor predictors
# with the numeric response variable
##################################
PMA_PreModelling_Train_Factor <- PMA_PreModelling_Train[,grepl("FP", names(PMA_PreModelling_Train))]
PMA_PreModelling_Train_Factor$Log_Solubility_Class <- PMA_PreModelling_Train$Log_Solubility
dim(PMA_PreModelling_Train_Factor)## [1] 951 206str(PMA_PreModelling_Train_Factor)## 'data.frame': 951 obs. of 206 variables:
## $ FP001 : Factor w/ 2 levels "0","1": 1 1 2 1 1 2 1 2 2 2 ...
## $ FP002 : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 2 1 1 2 ...
## $ FP003 : Factor w/ 2 levels "0","1": 1 1 2 2 2 2 1 2 2 2 ...
## $ FP004 : Factor w/ 2 levels "0","1": 1 2 2 1 2 2 2 2 2 2 ...
## $ FP005 : Factor w/ 2 levels "0","1": 2 2 2 1 2 1 2 1 1 2 ...
## $ FP006 : Factor w/ 2 levels "0","1": 1 2 1 1 2 1 1 1 2 2 ...
## $ FP007 : Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 2 2 2 ...
## $ FP008 : Factor w/ 2 levels "0","1": 2 2 2 1 1 1 2 1 1 1 ...
## $ FP009 : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 2 1 2 1 ...
## $ FP010 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ FP011 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 1 ...
## $ FP012 : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 2 1 1 ...
## $ FP013 : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 2 1 1 1 ...
## $ FP014 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ FP015 : Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ...
## $ FP016 : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 1 2 1 1 ...
## $ FP017 : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 1 2 2 ...
## $ FP018 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ FP019 : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 2 1 1 1 ...
## $ FP020 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP021 : Factor w/ 2 levels "0","1": 1 1 1 1 1 2 1 1 2 1 ...
## $ FP022 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ FP023 : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 2 1 ...
## $ FP024 : Factor w/ 2 levels "0","1": 2 1 1 1 2 1 1 1 1 1 ...
## $ FP025 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ FP026 : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 2 1 1 1 ...
## $ FP027 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ FP028 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 2 ...
## $ FP029 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP030 : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 1 ...
## $ FP031 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ FP032 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP033 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP034 : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 2 ...
## $ FP035 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ FP036 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP037 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ FP038 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ FP039 : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ FP040 : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ FP041 : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 2 1 ...
## $ FP042 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP043 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ FP044 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP045 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 1 1 1 ...
## $ FP046 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 2 1 1 2 ...
## $ FP047 : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 2 1 1 1 ...
## $ FP048 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ FP049 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ FP050 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 2 ...
## $ FP051 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 1 1 ...
## $ FP052 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ FP053 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ FP054 : Factor w/ 2 levels "0","1": 1 1 1 2 1 1 1 1 2 2 ...
## $ FP055 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 1 ...
## $ FP056 : Factor w/ 2 levels "0","1": 2 1 1 1 1 1 1 1 1 1 ...
## $ FP057 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 1 1 ...
## $ FP058 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 1 2 ...
## $ FP059 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 2 1 1 ...
## $ FP060 : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 2 2 1 ...
## $ FP061 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 2 2 1 ...
## $ FP062 : Factor w/ 2 levels "0","1": 1 1 2 1 1 2 1 2 2 2 ...
## $ FP063 : Factor w/ 2 levels "0","1": 2 2 1 1 2 2 2 1 1 2 ...
## $ FP064 : Factor w/ 2 levels "0","1": 1 2 2 1 2 2 1 2 1 1 ...
## $ FP065 : Factor w/ 2 levels "0","1": 2 2 1 1 2 1 2 1 2 2 ...
## $ FP066 : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 2 2 2 2 ...
## $ FP067 : Factor w/ 2 levels "0","1": 2 2 1 1 2 2 2 1 1 2 ...
## $ FP068 : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 2 1 1 2 ...
## $ FP069 : Factor w/ 2 levels "0","1": 2 1 2 2 2 2 1 2 2 1 ...
## $ FP070 : Factor w/ 2 levels "0","1": 2 2 1 2 1 1 2 1 2 1 ...
## $ FP071 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 2 1 2 2 ...
## $ FP072 : Factor w/ 2 levels "0","1": 1 2 2 1 1 2 1 2 2 2 ...
## $ FP073 : Factor w/ 2 levels "0","1": 1 2 2 1 1 1 1 1 2 1 ...
## $ FP074 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 1 ...
## $ FP075 : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 2 1 1 2 ...
## $ FP076 : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 2 1 2 2 ...
## $ FP077 : Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 2 2 2 ...
## $ FP078 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 1 2 1 ...
## $ FP079 : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 2 1 2 2 ...
## $ FP080 : Factor w/ 2 levels "0","1": 1 2 1 1 2 2 2 2 1 1 ...
## $ FP081 : Factor w/ 2 levels "0","1": 1 1 2 2 1 1 1 2 2 2 ...
## $ FP082 : Factor w/ 2 levels "0","1": 2 2 2 1 2 2 2 1 2 2 ...
## $ FP083 : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 1 1 2 ...
## $ FP084 : Factor w/ 2 levels "0","1": 2 2 1 1 2 1 2 1 1 1 ...
## $ FP085 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 2 1 1 1 ...
## $ FP086 : Factor w/ 2 levels "0","1": 1 1 1 2 2 1 1 2 2 2 ...
## $ FP087 : Factor w/ 2 levels "0","1": 2 2 2 2 2 1 2 1 2 2 ...
## $ FP088 : Factor w/ 2 levels "0","1": 1 2 1 1 1 1 1 2 2 1 ...
## $ FP089 : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 2 1 1 1 ...
## $ FP090 : Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 2 2 2 ...
## $ FP091 : Factor w/ 2 levels "0","1": 2 2 1 1 2 1 2 1 1 2 ...
## $ FP092 : Factor w/ 2 levels "0","1": 1 1 1 1 2 2 2 1 2 1 ...
## $ FP093 : Factor w/ 2 levels "0","1": 1 2 1 2 1 1 1 2 2 2 ...
## $ FP094 : Factor w/ 2 levels "0","1": 1 1 1 1 2 1 1 2 1 1 ...
## $ FP095 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 2 ...
## $ FP096 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## $ FP097 : Factor w/ 2 levels "0","1": 2 2 1 1 1 1 2 1 2 1 ...
## $ FP098 : Factor w/ 2 levels "0","1": 1 1 2 1 1 1 1 2 1 1 ...
## $ FP099 : Factor w/ 2 levels "0","1": 1 1 1 1 1 1 1 1 2 1 ...
## [list output truncated]summary(PMA_PreModelling_Train_Factor)## FP001 FP002 FP003 FP004 FP005 FP006 FP007 FP008 FP009
## 0:482 0:438 0:536 0:395 0:400 0:570 0:605 0:641 0:685
## 1:469 1:513 1:415 1:556 1:551 1:381 1:346 1:310 1:266
## FP010 FP011 FP012 FP013 FP014 FP015 FP016 FP017 FP018
## 0:781 0:747 0:783 0:793 0:798 0:133 0:812 0:814 0:826
## 1:170 1:204 1:168 1:158 1:153 1:818 1:139 1:137 1:125
## FP019 FP020 FP021 FP022 FP023 FP024 FP025 FP026 FP027
## 0:835 0:837 0:836 0:852 0:834 0:844 0:841 0:871 0:858
## 1:116 1:114 1:115 1: 99 1:117 1:107 1:110 1: 80 1: 93
## FP028 FP029 FP030 FP031 FP032 FP033 FP034 FP035 FP036
## 0:850 0:854 0:862 0:866 0:881 0:885 0:875 0:882 0:879
## 1:101 1: 97 1: 89 1: 85 1: 70 1: 66 1: 76 1: 69 1: 72
## FP037 FP038 FP039 FP040 FP041 FP042 FP043 FP044 FP045
## 0:884 0:869 0:880 0:886 0:891 0:897 0:888 0:894 0:898
## 1: 67 1: 82 1: 71 1: 65 1: 60 1: 54 1: 63 1: 57 1: 53
## FP046 FP047 FP048 FP049 FP050 FP051 FP052 FP053 FP054
## 0:651 0:698 0:833 0:835 0:844 0:847 0:864 0:862 0:879
## 1:300 1:253 1:118 1:116 1:107 1:104 1: 87 1: 89 1: 72
## FP055 FP056 FP057 FP058 FP059 FP060 FP061 FP062 FP063
## 0:900 0:889 0:837 0:843 0:899 0:493 0:526 0:535 0:546
## 1: 51 1: 62 1:114 1:108 1: 52 1:458 1:425 1:416 1:405
## FP064 FP065 FP066 FP067 FP068 FP069 FP070 FP071 FP072
## 0:555 0:387 0:371 0:590 0:607 0:607 0:613 0:640 0:325
## 1:396 1:564 1:580 1:361 1:344 1:344 1:338 1:311 1:626
## FP073 FP074 FP075 FP076 FP077 FP078 FP079 FP080 FP081
## 0:656 0:642 0:629 0:639 0:646 0:662 0:295 0:663 0:686
## 1:295 1:309 1:322 1:312 1:305 1:289 1:656 1:288 1:265
## FP082 FP083 FP084 FP085 FP086 FP087 FP088 FP089 FP090
## 0:272 0:691 0:679 0:708 0:695 0:260 0:701 0:716 0:714
## 1:679 1:260 1:272 1:243 1:256 1:691 1:250 1:235 1:237
## FP091 FP092 FP093 FP094 FP095 FP096 FP097 FP098 FP099
## 0:737 0:719 0:719 0:731 0:742 0:744 0:727 0:725 0:735
## 1:214 1:232 1:232 1:220 1:209 1:207 1:224 1:226 1:216
## FP100 FP101 FP102 FP103 FP104 FP105 FP106 FP107 FP108
## 0:731 0:726 0:759 0:743 0:739 0:746 0:769 0:750 0:756
## 1:220 1:225 1:192 1:208 1:212 1:205 1:182 1:201 1:195
## FP109 FP110 FP111 FP112 FP113 FP114 FP115 FP116 FP117
## 0:783 0:755 0:764 0:766 0:765 0:803 0:781 0:768 0:781
## 1:168 1:196 1:187 1:185 1:186 1:148 1:170 1:183 1:170
## FP118 FP119 FP120 FP121 FP122 FP123 FP124 FP125 FP126
## 0:768 0:796 0:793 0:818 0:795 0:792 0:797 0:803 0:810
## 1:183 1:155 1:158 1:133 1:156 1:159 1:154 1:148 1:141
## FP127 FP128 FP129 FP130 FP131 FP132 FP133 FP134 FP135
## 0:818 0:810 0:819 0:851 0:831 0:832 0:831 0:830 0:831
## 1:133 1:141 1:132 1:100 1:120 1:119 1:120 1:121 1:120
## FP136 FP137 FP138 FP139 FP140 FP141 FP142 FP143 FP144
## 0:836 0:841 0:845 0:873 0:845 0:840 0:847 0:874 0:852
## 1:115 1:110 1:106 1: 78 1:106 1:111 1:104 1: 77 1: 99
## FP145 FP146 FP147 FP148 FP149 FP150 FP151 FP152 FP153
## 0:852 0:853 0:851 0:868 0:865 0:876 0:898 0:873 0:877
## 1: 99 1: 98 1:100 1: 83 1: 86 1: 75 1: 53 1: 78 1: 74
## FP155 FP156 FP157 FP158 FP159 FP160 FP161 FP162 FP163
## 0:885 0:884 0:892 0:900 0:884 0:886 0:888 0:480 0:498
## 1: 66 1: 67 1: 59 1: 51 1: 67 1: 65 1: 63 1:471 1:453
## FP164 FP165 FP166 FP167 FP168 FP169 FP170 FP171 FP172
## 0:354 0:619 0:636 0:639 0:318 0:774 0:776 0:790 0:807
## 1:597 1:332 1:315 1:312 1:633 1:177 1:175 1:161 1:144
## FP173 FP174 FP175 FP176 FP177 FP178 FP179 FP180 FP181
## 0:816 0:827 0:823 0:835 0:836 0:836 0:858 0:849 0:862
## 1:135 1:124 1:128 1:116 1:115 1:115 1: 93 1:102 1: 89
## FP182 FP183 FP184 FP185 FP186 FP187 FP188 FP189 FP190
## 0:857 0:879 0:871 0:870 0:878 0:882 0:886 0:878 0:882
## 1: 94 1: 72 1: 80 1: 81 1: 73 1: 69 1: 65 1: 73 1: 69
## FP191 FP192 FP193 FP194 FP195 FP196 FP197 FP198 FP201
## 0:884 0:893 0:892 0:895 0:893 0:897 0:901 0:897 0:901
## 1: 67 1: 58 1: 59 1: 56 1: 58 1: 54 1: 50 1: 54 1: 50
## FP202 FP203 FP204 FP205 FP206 FP207 FP208 Log_Solubility_Class
## 0:706 0:842 0:857 0:877 0:894 0:897 0:844 Low :427
## 1:245 1:109 1: 94 1: 74 1: 57 1: 54 1:107 High:524##################################
# Obtaining the Fisher exact test statistics
##################################
VP_F <- function(x, y){
tab <- table(x, y)
fet <- fisher.test(tab)
out <- c(FET_OR = fet$estimate,
FET_P.Value = fet$p.value)}
##################################
# Formulating the summary table
##################################
VP_F_Summary <- lapply(PMA_PreModelling_Train_Factor,
VP_F,
y = PMA_PreModelling_Train_Factor$Log_Solubility_Class)
VP_F_Summary <- as.data.frame(do.call("rbind", VP_F_Summary))
VP_F_Summary$Predictor <- names(PMA_PreModelling_Train_Factor)
VP_F_Summary <- VP_F_Summary[-nrow(VP_F_Summary),]
VP_F_Summary$Metric <- rep("VP_F", nrow(VP_F_Summary))
VP_F_Summary$NegativeLog10_FET_P.Value <- -log10(VP_F_Summary$FET_P.Value)
VP_F_Summary$LogOddsRatio <- log(VP_F_Summary[,1])
VP_F_Summary$AbsoluteLogOddsRatio <- abs(VP_F_Summary$LogOddsRatio)
VP_F_Summary$Group <- ifelse(rownames(VP_F_Summary)=="FP076","FP076",
ifelse(rownames(VP_F_Summary)=="FP089","FP089",
ifelse(rownames(VP_F_Summary)=="FP112","FP112",
ifelse(rownames(VP_F_Summary)=="FP044","FP044",
ifelse(rownames(VP_F_Summary)=="FP193","FP193",
"Others")))))
VP_F_Summary## FET_OR.odds ratio FET_P.Value Predictor Metric NegativeLog10_FET_P.Value
## FP001 1.19708080 1.715372e-01 FP001 VP_F 7.656418e-01
## FP002 0.35039786 5.181602e-15 FP002 VP_F 1.428554e+01
## FP003 0.97166586 8.438406e-01 FP003 VP_F 7.373956e-02
## FP004 1.26919078 7.435063e-02 FP004 VP_F 1.128715e+00
## FP005 0.35435518 2.273432e-14 FP005 VP_F 1.364332e+01
## FP006 1.84346020 5.972899e-06 FP006 VP_F 5.223815e+00
## FP007 0.88537336 3.789438e-01 FP007 VP_F 4.214252e-01
## FP008 0.57340983 7.203997e-05 FP008 VP_F 4.142426e+00
## FP009 0.26982672 1.890323e-18 FP009 VP_F 1.772346e+01
## FP010 1.57515069 1.057248e-02 FP010 VP_F 1.975823e+00
## FP011 1.90083496 9.542912e-05 FP011 VP_F 4.020319e+00
## FP012 1.10579162 6.082058e-01 FP012 VP_F 2.159494e-01
## FP013 0.14883782 2.727491e-23 FP013 VP_F 2.256424e+01
## FP014 0.15750978 9.678623e-22 FP014 VP_F 2.101419e+01
## FP015 2.86928927 5.561892e-08 FP015 VP_F 7.254777e+00
## FP016 0.85604079 4.075094e-01 FP016 VP_F 3.898624e-01
## FP017 0.49376123 1.883936e-04 FP017 VP_F 3.724934e+00
## FP018 0.47679807 1.545802e-04 FP018 VP_F 3.810846e+00
## FP019 1.04400654 8.428105e-01 FP019 VP_F 7.427005e-02
## FP020 1.28698533 2.293994e-01 FP020 VP_F 6.394076e-01
## FP021 0.74658209 1.614886e-01 FP021 VP_F 7.918582e-01
## FP022 1.02076928 1.000000e+00 FP022 VP_F 4.821637e-17
## FP023 0.54497814 2.790955e-03 FP023 VP_F 2.554247e+00
## FP024 1.09109789 6.815917e-01 FP024 VP_F 1.664757e-01
## FP025 1.30887702 2.213225e-01 FP025 VP_F 6.549744e-01
## FP026 0.64245234 6.095878e-02 FP026 VP_F 1.214964e+00
## FP027 2.01654412 2.893976e-03 FP027 VP_F 2.538505e+00
## FP028 1.21715865 3.977531e-01 FP028 VP_F 4.003864e-01
## FP029 0.85300530 5.182871e-01 FP029 VP_F 2.854296e-01
## FP030 1.42811535 1.453412e-01 FP030 VP_F 8.376114e-01
## FP031 0.81964988 4.241544e-01 FP031 VP_F 3.724760e-01
## FP032 1.72977657 4.512846e-02 FP032 VP_F 1.345550e+00
## FP033 1.56955042 9.624740e-02 FP033 VP_F 1.016611e+00
## FP034 0.71399401 1.858759e-01 FP034 VP_F 7.307769e-01
## FP035 0.28405176 2.035732e-06 FP035 VP_F 5.691279e+00
## FP036 2.60842408 4.826361e-04 FP036 VP_F 3.316380e+00
## FP037 0.60007108 5.538991e-02 FP037 VP_F 1.256569e+00
## FP038 1.37688518 2.016885e-01 FP038 VP_F 6.953188e-01
## FP039 0.27168922 7.256956e-07 FP039 VP_F 6.139246e+00
## FP040 1.91054797 1.975706e-02 FP040 VP_F 1.704278e+00
## FP041 0.29980664 2.191840e-05 FP041 VP_F 4.659191e+00
## FP042 1.29903467 3.997709e-01 FP042 VP_F 3.981888e-01
## FP043 0.63257683 8.858424e-02 FP043 VP_F 1.052644e+00
## FP044 0.05440672 3.102624e-15 FP044 VP_F 1.450827e+01
## FP045 0.39880063 1.656122e-03 FP045 VP_F 2.780908e+00
## FP046 0.47005989 8.986407e-08 FP046 VP_F 7.046414e+00
## FP047 0.65690301 5.010695e-03 FP047 VP_F 2.300102e+00
## FP048 0.82285525 3.247352e-01 FP048 VP_F 4.884706e-01
## FP049 0.19582389 2.675582e-14 FP049 VP_F 1.357258e+01
## FP050 0.60302823 1.734588e-02 FP050 VP_F 1.760804e+00
## FP051 0.51334022 1.655115e-03 FP051 VP_F 2.781172e+00
## FP052 1.23362693 3.684283e-01 FP052 VP_F 4.336471e-01
## FP053 0.23582634 1.209324e-09 FP053 VP_F 8.917457e+00
## FP054 0.30817566 5.987552e-06 FP054 VP_F 5.222751e+00
## FP055 1.67259146 1.108072e-01 FP055 VP_F 9.554319e-01
## FP056 0.26122657 2.079039e-06 FP056 VP_F 5.682137e+00
## FP057 0.79249223 2.698146e-01 FP057 VP_F 5.689346e-01
## FP058 0.93853294 7.590565e-01 FP058 VP_F 1.197259e-01
## FP059 0.28137500 3.906572e-05 FP059 VP_F 4.408204e+00
## FP060 1.63240884 1.999323e-04 FP060 VP_F 3.699117e+00
## FP061 1.04977625 7.431330e-01 FP061 VP_F 1.289334e-01
## FP062 1.29158337 5.673496e-02 FP062 VP_F 1.246149e+00
## FP063 1.36519387 2.100723e-02 FP063 VP_F 1.677631e+00
## FP064 1.18728146 2.091106e-01 FP064 VP_F 6.796239e-01
## FP065 0.24259847 2.350296e-24 FP065 VP_F 2.362888e+01
## FP066 1.00753443 1.000000e+00 FP066 VP_F 4.821637e-17
## FP067 1.15740510 2.832438e-01 FP067 VP_F 5.478396e-01
## FP068 1.12634107 4.156288e-01 FP068 VP_F 3.812944e-01
## FP069 0.85481954 2.496501e-01 FP069 VP_F 6.026683e-01
## FP070 0.27947568 5.654858e-20 FP070 VP_F 1.924758e+01
## FP071 0.32504356 2.346502e-15 FP071 VP_F 1.462958e+01
## FP072 2.69986223 1.052267e-12 FP072 VP_F 1.197787e+01
## FP073 1.74346468 1.063125e-04 FP073 VP_F 3.973416e+00
## FP074 1.20851743 1.862849e-01 FP074 VP_F 7.298225e-01
## FP075 1.32082732 4.603196e-02 FP075 VP_F 1.336941e+00
## FP076 0.12445706 6.187262e-45 FP076 VP_F 4.420850e+01
## FP077 0.76099672 5.088938e-02 FP077 VP_F 1.293373e+00
## FP078 0.91855970 5.709918e-01 FP078 VP_F 2.433701e-01
## FP079 0.22398333 6.263011e-23 FP079 VP_F 2.220322e+01
## FP080 1.25723356 1.186374e-01 FP080 VP_F 9.257783e-01
## FP081 0.72911277 2.948404e-02 FP081 VP_F 1.530413e+00
## FP082 0.22175679 7.799605e-22 FP082 VP_F 2.110793e+01
## FP083 1.79705328 1.022517e-04 FP083 VP_F 3.990329e+00
## FP084 1.13637183 3.874658e-01 FP084 VP_F 4.117666e-01
## FP085 0.27122453 2.014219e-17 FP085 VP_F 1.669589e+01
## FP086 0.64974151 3.320546e-03 FP086 VP_F 2.478791e+00
## FP087 0.25801089 7.989715e-18 FP087 VP_F 1.709747e+01
## FP088 1.57116925 3.024704e-03 FP088 VP_F 2.519317e+00
## FP089 0.14721223 2.145272e-32 FP089 VP_F 3.166852e+01
## FP090 0.64237027 3.317666e-03 FP090 VP_F 2.479167e+00
## FP091 0.97800429 9.377988e-01 FP091 VP_F 2.789033e-02
## FP092 0.20899251 6.412816e-23 FP092 VP_F 2.219295e+01
## FP093 0.49172261 3.399920e-06 FP093 VP_F 5.468531e+00
## FP094 0.99476660 1.000000e+00 FP094 VP_F 0.000000e+00
## FP095 1.21598118 2.378080e-01 FP095 VP_F 6.237736e-01
## FP096 0.79866626 1.559207e-01 FP096 VP_F 8.070962e-01
## FP097 0.27373537 2.585714e-16 FP097 VP_F 1.558742e+01
## FP098 1.28105523 1.254892e-01 FP098 VP_F 9.013938e-01
## FP099 0.47185118 1.845962e-06 FP099 VP_F 5.733777e+00
## FP100 1.36192602 5.326631e-02 FP100 VP_F 1.273547e+00
## FP101 1.15291250 3.587501e-01 FP101 VP_F 4.452080e-01
## FP102 0.54942707 2.496460e-04 FP102 VP_F 3.602675e+00
## FP103 0.58576485 6.976654e-04 FP103 VP_F 3.156353e+00
## FP104 0.63194616 3.717872e-03 FP104 VP_F 2.429706e+00
## FP105 0.48316770 5.646872e-06 FP105 VP_F 5.248192e+00
## FP106 0.65902108 1.285535e-02 FP106 VP_F 1.890916e+00
## FP107 0.27844164 7.142993e-15 FP107 VP_F 1.414612e+01
## FP108 1.01457497 9.358550e-01 FP108 VP_F 2.879143e-02
## FP109 0.61805388 4.837573e-03 FP109 VP_F 2.315372e+00
## FP110 1.37134941 5.366059e-02 FP110 VP_F 1.270345e+00
## FP111 0.89745661 5.128947e-01 FP111 VP_F 2.899718e-01
## FP112 0.13793978 2.701713e-28 FP112 VP_F 2.756836e+01
## FP113 1.44943098 2.675822e-02 FP113 VP_F 1.572543e+00
## FP114 0.73592698 8.832607e-02 FP114 VP_F 1.053911e+00
## FP115 0.87408678 4.445275e-01 FP115 VP_F 3.521014e-01
## FP116 1.15250268 4.091495e-01 FP116 VP_F 3.881180e-01
## FP117 0.60131089 2.891885e-03 FP117 VP_F 2.538819e+00
## FP118 1.28812070 1.371138e-01 FP118 VP_F 8.629190e-01
## FP119 1.11895502 5.380064e-01 FP119 VP_F 2.692126e-01
## FP120 1.32008395 1.363755e-01 FP120 VP_F 8.652636e-01
## FP121 0.54424360 1.353781e-03 FP121 VP_F 2.868452e+00
## FP122 0.97085516 9.298813e-01 FP122 VP_F 3.157251e-02
## FP123 0.47156532 2.445612e-05 FP123 VP_F 4.611612e+00
## FP124 1.17599458 3.773446e-01 FP124 VP_F 4.232618e-01
## FP125 1.15573797 4.720280e-01 FP125 VP_F 3.260322e-01
## FP126 1.01045303 1.000000e+00 FP126 VP_F 0.000000e+00
## FP127 1.41857268 7.424460e-02 FP127 VP_F 1.129335e+00
## FP128 1.37469908 9.866654e-02 FP128 VP_F 1.005830e+00
## FP129 0.51443239 4.682229e-04 FP129 VP_F 3.329547e+00
## FP130 0.66502981 5.625422e-02 FP130 VP_F 1.249845e+00
## FP131 0.95783416 8.447698e-01 FP131 VP_F 7.326163e-02
## FP132 2.04266955 5.495649e-04 FP132 VP_F 3.259981e+00
## FP133 1.11789560 6.238370e-01 FP133 VP_F 2.049288e-01
## FP134 0.57195802 4.523155e-03 FP134 VP_F 2.344559e+00
## FP135 0.95783416 8.447698e-01 FP135 VP_F 7.326163e-02
## FP136 1.06770699 7.650764e-01 FP136 VP_F 1.162952e-01
## FP137 0.59399949 1.088153e-02 FP137 VP_F 1.963310e+00
## FP138 0.64186857 3.794478e-02 FP138 VP_F 1.420848e+00
## FP139 0.80019382 3.452346e-01 FP139 VP_F 4.618857e-01
## FP140 1.39373024 1.212801e-01 FP140 VP_F 9.162104e-01
## FP141 0.49290239 5.407860e-04 FP141 VP_F 3.266975e+00
## FP142 0.82977493 4.037494e-01 FP142 VP_F 3.938882e-01
## FP143 0.73530968 2.319326e-01 FP143 VP_F 6.346383e-01
## FP144 1.63603800 3.234915e-02 FP144 VP_F 1.490137e+00
## FP145 1.34827701 2.000793e-01 FP145 VP_F 6.987978e-01
## FP146 0.27276366 6.880778e-09 FP146 VP_F 8.162362e+00
## FP147 1.14075786 5.955152e-01 FP147 VP_F 2.251071e-01
## FP148 1.25912604 3.564450e-01 FP148 VP_F 4.480075e-01
## FP149 0.17427309 2.945871e-12 FP149 VP_F 1.153079e+01
## FP150 1.10335419 7.180062e-01 FP150 VP_F 1.438718e-01
## FP151 1.15739238 6.709985e-01 FP151 VP_F 1.732785e-01
## FP152 0.67652104 1.220168e-01 FP152 VP_F 9.135804e-01
## FP153 0.59654511 3.829194e-02 FP153 VP_F 1.416893e+00
## FP155 0.23782568 2.509629e-07 FP155 VP_F 6.600391e+00
## FP156 0.56197587 2.973839e-02 FP156 VP_F 1.526682e+00
## FP157 0.89614943 6.876569e-01 FP157 VP_F 1.626282e-01
## FP158 1.83744533 5.907418e-02 FP158 VP_F 1.228602e+00
## FP159 0.60007108 5.538991e-02 FP159 VP_F 1.256569e+00
## FP160 0.94725316 8.973947e-01 FP160 VP_F 4.701649e-02
## FP161 4.68576694 6.710555e-07 FP161 VP_F 6.173242e+00
## FP162 0.39641239 2.689179e-12 FP162 VP_F 1.157038e+01
## FP163 1.30035228 5.018728e-02 FP163 VP_F 1.299406e+00
## FP164 0.29316745 3.185855e-18 FP164 VP_F 1.749677e+01
## FP165 1.14157018 3.392740e-01 FP165 VP_F 4.694494e-01
## FP166 0.52535686 4.568833e-06 FP166 VP_F 5.340195e+00
## FP167 1.16920320 2.674396e-01 FP167 VP_F 5.727742e-01
## FP168 0.24673455 1.869134e-21 FP168 VP_F 2.072836e+01
## FP169 0.19581712 3.513565e-20 FP169 VP_F 1.945425e+01
## FP170 0.57865361 1.348906e-03 FP170 VP_F 2.870018e+00
## FP171 1.60655269 8.996114e-03 FP171 VP_F 2.045945e+00
## FP172 0.12306538 1.375876e-24 FP172 VP_F 2.386142e+01
## FP173 0.45698434 3.551485e-05 FP173 VP_F 4.449590e+00
## FP174 0.65612868 3.299563e-02 FP174 VP_F 1.481544e+00
## FP175 0.94595425 7.753215e-01 FP175 VP_F 1.105182e-01
## FP176 1.00334103 1.000000e+00 FP176 VP_F 0.000000e+00
## FP177 0.87473364 5.488107e-01 FP177 VP_F 2.605775e-01
## FP178 0.49966530 6.352505e-04 FP178 VP_F 3.197055e+00
## FP179 0.67434769 7.911965e-02 FP179 VP_F 1.101716e+00
## FP180 1.42373128 1.140569e-01 FP180 VP_F 9.428783e-01
## FP181 0.26768983 2.106565e-08 FP181 VP_F 7.676425e+00
## FP182 1.22466105 3.835099e-01 FP182 VP_F 4.162234e-01
## FP183 1.58365219 8.415258e-02 FP183 VP_F 1.074933e+00
## FP184 0.13694274 1.118373e-13 FP184 VP_F 1.295141e+01
## FP185 0.37563701 5.699476e-05 FP185 VP_F 4.244165e+00
## FP186 1.18281337 5.414405e-01 FP186 VP_F 2.664493e-01
## FP187 0.93790027 8.029155e-01 FP187 VP_F 9.533016e-02
## FP188 1.77119205 3.859076e-02 FP188 VP_F 1.413517e+00
## FP189 0.57675161 2.735176e-02 FP189 VP_F 1.563015e+00
## FP190 0.18597681 1.732961e-09 FP190 VP_F 8.761211e+00
## FP191 1.14571288 6.130662e-01 FP191 VP_F 2.124926e-01
## FP192 0.51452638 1.988386e-02 FP192 VP_F 1.701499e+00
## FP193 0.08191610 1.128789e-13 FP193 VP_F 1.294739e+01
## FP194 1.18034939 5.822638e-01 FP194 VP_F 2.348802e-01
## FP195 3.72159008 3.063427e-05 FP195 VP_F 4.513792e+00
## FP196 0.14858927 3.201577e-09 FP196 VP_F 8.494636e+00
## FP197 0.16479415 4.266746e-08 FP197 VP_F 7.369903e+00
## FP198 2.00709087 2.367488e-02 FP198 VP_F 1.625712e+00
## FP201 1.78135621 7.872569e-02 FP201 VP_F 1.103884e+00
## FP202 0.52577576 2.020707e-05 FP202 VP_F 4.694497e+00
## FP203 0.95670909 8.384706e-01 FP203 VP_F 7.651215e-02
## FP204 0.54424384 6.162864e-03 FP204 VP_F 2.210217e+00
## FP205 0.31642129 5.109654e-06 FP205 VP_F 5.291608e+00
## FP206 0.45381997 5.634832e-03 FP206 VP_F 2.249119e+00
## FP207 0.14858927 3.201577e-09 FP207 VP_F 8.494636e+00
## FP208 0.92038091 7.570924e-01 FP208 VP_F 1.208511e-01
## LogOddsRatio AbsoluteLogOddsRatio Group
## FP001 0.179885927 0.179885927 Others
## FP002 -1.048686041 1.048686041 Others
## FP003 -0.028743299 0.028743299 Others
## FP004 0.238379513 0.238379513 Others
## FP005 -1.037455546 1.037455546 Others
## FP006 0.611644351 0.611644351 Others
## FP007 -0.121745848 0.121745848 Others
## FP008 -0.556154591 0.556154591 Others
## FP009 -1.309975319 1.309975319 Others
## FP010 0.454350945 0.454350945 Others
## FP011 0.642293242 0.642293242 Others
## FP012 0.100561473 0.100561473 Others
## FP013 -1.904898041 1.904898041 Others
## FP014 -1.848267748 1.848267748 Others
## FP015 1.054064357 1.054064357 Others
## FP016 -0.155437247 0.155437247 Others
## FP017 -0.705703211 0.705703211 Others
## FP018 -0.740662219 0.740662219 Others
## FP019 0.043065754 0.043065754 Others
## FP020 0.252302533 0.252302533 Others
## FP021 -0.292249695 0.292249695 Others
## FP022 0.020556543 0.020556543 Others
## FP023 -0.607009590 0.607009590 Others
## FP024 0.087184424 0.087184424 Others
## FP025 0.269169534 0.269169534 Others
## FP026 -0.442462641 0.442462641 Others
## FP027 0.701385217 0.701385217 Others
## FP028 0.196519164 0.196519164 Others
## FP029 -0.158989520 0.158989520 Others
## FP030 0.356355635 0.356355635 Others
## FP031 -0.198878004 0.198878004 Others
## FP032 0.547992253 0.547992253 Others
## FP033 0.450789220 0.450789220 Others
## FP034 -0.336880708 0.336880708 Others
## FP035 -1.258598799 1.258598799 Others
## FP036 0.958746238 0.958746238 Others
## FP037 -0.510707161 0.510707161 Others
## FP038 0.319823833 0.319823833 Others
## FP039 -1.303096431 1.303096431 Others
## FP040 0.647390098 0.647390098 Others
## FP041 -1.204617559 1.204617559 Others
## FP042 0.261621424 0.261621424 Others
## FP043 -0.457953588 0.457953588 Others
## FP044 -2.911267576 2.911267576 FP044
## FP045 -0.919293663 0.919293663 Others
## FP046 -0.754895161 0.754895161 Others
## FP047 -0.420218893 0.420218893 Others
## FP048 -0.194974974 0.194974974 Others
## FP049 -1.630539536 1.630539536 Others
## FP050 -0.505791274 0.505791274 Others
## FP051 -0.666816450 0.666816450 Others
## FP052 0.209958553 0.209958553 Others
## FP053 -1.444659604 1.444659604 Others
## FP054 -1.177085338 1.177085338 Others
## FP055 0.514374198 0.514374198 Others
## FP056 -1.342367160 1.342367160 Others
## FP057 -0.232572576 0.232572576 Others
## FP058 -0.063437329 0.063437329 Others
## FP059 -1.268066990 1.268066990 Others
## FP060 0.490056742 0.490056742 Others
## FP061 0.048577050 0.048577050 Others
## FP062 0.255868883 0.255868883 Others
## FP063 0.311296446 0.311296446 Others
## FP064 0.171666208 0.171666208 Others
## FP065 -1.416347592 1.416347592 Others
## FP066 0.007506185 0.007506185 Others
## FP067 0.146180513 0.146180513 Others
## FP068 0.118974389 0.118974389 Others
## FP069 -0.156864895 0.156864895 Others
## FP070 -1.274839999 1.274839999 Others
## FP071 -1.123796088 1.123796088 Others
## FP072 0.993200747 0.993200747 Others
## FP073 0.555874326 0.555874326 Others
## FP074 0.189394345 0.189394345 Others
## FP075 0.278258294 0.278258294 Others
## FP076 -2.083794540 2.083794540 FP076
## FP077 -0.273126235 0.273126235 Others
## FP078 -0.084948384 0.084948384 Others
## FP079 -1.496183628 1.496183628 Others
## FP080 0.228913722 0.228913722 Others
## FP081 -0.315926873 0.315926873 Others
## FP082 -1.506174057 1.506174057 Others
## FP083 0.586148259 0.586148259 Others
## FP084 0.127840582 0.127840582 Others
## FP085 -1.304808259 1.304808259 Others
## FP086 -0.431180676 0.431180676 Others
## FP087 -1.354753489 1.354753489 Others
## FP088 0.451820085 0.451820085 Others
## FP089 -1.915879958 1.915879958 FP089
## FP090 -0.442590395 0.442590395 Others
## FP091 -0.022241223 0.022241223 Others
## FP092 -1.565456851 1.565456851 Others
## FP093 -0.709840517 0.709840517 Others
## FP094 -0.005247142 0.005247142 Others
## FP095 0.195551307 0.195551307 Others
## FP096 -0.224812113 0.224812113 Others
## FP097 -1.295593440 1.295593440 Others
## FP098 0.247684138 0.247684138 Others
## FP099 -0.751091647 0.751091647 Others
## FP100 0.308899886 0.308899886 Others
## FP101 0.142291351 0.142291351 Others
## FP102 -0.598879231 0.598879231 Others
## FP103 -0.534836847 0.534836847 Others
## FP104 -0.458951075 0.458951075 Others
## FP105 -0.727391471 0.727391471 Others
## FP106 -0.416999764 0.416999764 Others
## FP107 -1.278546809 1.278546809 Others
## FP108 0.014469780 0.014469780 Others
## FP109 -0.481179637 0.481179637 Others
## FP110 0.315795222 0.315795222 Others
## FP111 -0.108190502 0.108190502 Others
## FP112 -1.980938083 1.980938083 FP112
## FP113 0.371171050 0.371171050 Others
## FP114 -0.306624372 0.306624372 Others
## FP115 -0.134575613 0.134575613 Others
## FP116 0.141935823 0.141935823 Others
## FP117 -0.508643191 0.508643191 Others
## FP118 0.253184331 0.253184331 Others
## FP119 0.112395234 0.112395234 Others
## FP120 0.277695331 0.277695331 Others
## FP121 -0.608358336 0.608358336 Others
## FP122 -0.029577988 0.029577988 Others
## FP123 -0.751697650 0.751697650 Others
## FP124 0.162114244 0.162114244 Others
## FP125 0.144739079 0.144739079 Others
## FP126 0.010398773 0.010398773 Others
## FP127 0.349651214 0.349651214 Others
## FP128 0.318234855 0.318234855 Others
## FP129 -0.664691145 0.664691145 Others
## FP130 -0.407923418 0.407923418 Others
## FP131 -0.043080623 0.043080623 Others
## FP132 0.714257556 0.714257556 Others
## FP133 0.111447989 0.111447989 Others
## FP134 -0.558689675 0.558689675 Others
## FP135 -0.043080623 0.043080623 Others
## FP136 0.065513353 0.065513353 Others
## FP137 -0.520876823 0.520876823 Others
## FP138 -0.443371715 0.443371715 Others
## FP139 -0.222901302 0.222901302 Others
## FP140 0.331983776 0.331983776 Others
## FP141 -0.707444112 0.707444112 Others
## FP142 -0.186600779 0.186600779 Others
## FP143 -0.307463530 0.307463530 Others
## FP144 0.492277463 0.492277463 Others
## FP145 0.298827485 0.298827485 Others
## FP146 -1.299149556 1.299149556 Others
## FP147 0.131692828 0.131692828 Others
## FP148 0.230417865 0.230417865 Others
## FP149 -1.747131733 1.747131733 Others
## FP150 0.098354800 0.098354800 Others
## FP151 0.146169523 0.146169523 Others
## FP152 -0.390791731 0.390791731 Others
## FP153 -0.516600423 0.516600423 Others
## FP155 -1.436217309 1.436217309 Others
## FP156 -0.576296365 0.576296365 Others
## FP157 -0.109648111 0.109648111 Others
## FP158 0.608376197 0.608376197 Others
## FP159 -0.510707161 0.510707161 Others
## FP160 -0.054188894 0.054188894 Others
## FP161 1.544529604 1.544529604 Others
## FP162 -0.925300229 0.925300229 Others
## FP163 0.262635216 0.262635216 Others
## FP164 -1.227011339 1.227011339 Others
## FP165 0.132404668 0.132404668 Others
## FP166 -0.643677518 0.643677518 Others
## FP167 0.156322489 0.156322489 Others
## FP168 -1.399442231 1.399442231 Others
## FP169 -1.630574123 1.630574123 Others
## FP170 -0.547051236 0.547051236 Others
## FP171 0.474090698 0.474090698 Others
## FP172 -2.095039482 2.095039482 Others
## FP173 -0.783106166 0.783106166 Others
## FP174 -0.421398357 0.421398357 Others
## FP175 -0.055561071 0.055561071 Others
## FP176 0.003335459 0.003335459 Others
## FP177 -0.133835847 0.133835847 Others
## FP178 -0.693816809 0.693816809 Others
## FP179 -0.394009441 0.394009441 Others
## FP180 0.353281085 0.353281085 Others
## FP181 -1.317926317 1.317926317 Others
## FP182 0.202664115 0.202664115 Others
## FP183 0.459733693 0.459733693 Others
## FP184 -1.988192371 1.988192371 Others
## FP185 -0.979131989 0.979131989 Others
## FP186 0.167895811 0.167895811 Others
## FP187 -0.064111657 0.064111657 Others
## FP188 0.571652797 0.571652797 Others
## FP189 -0.550343597 0.550343597 Others
## FP190 -1.682133264 1.682133264 Others
## FP191 0.136027042 0.136027042 Others
## FP192 -0.664508453 0.664508453 Others
## FP193 -2.502059785 2.502059785 FP193
## FP194 0.165810490 0.165810490 Others
## FP195 1.314151018 1.314151018 Others
## FP196 -1.906569334 1.906569334 Others
## FP197 -1.803058135 1.803058135 Others
## FP198 0.696686345 0.696686345 Others
## FP201 0.577374992 0.577374992 Others
## FP202 -0.642880476 0.642880476 Others
## FP203 -0.044255912 0.044255912 Others
## FP204 -0.608357895 0.608357895 Others
## FP205 -1.150680746 1.150680746 Others
## FP206 -0.790054693 0.790054693 Others
## FP207 -1.906569334 1.906569334 Others
## FP208 -0.082967663 0.082967663 Others##################################
# Selecting the best-performing
# predictors based from metrics
##################################
VP_F_Summary_Top15_FETPValue <- VP_F_Summary[order(VP_F_Summary$NegativeLog10_FET_P.Value,decreasing=TRUE),]
(VP_F_Summary_Top15_FETPValue <- VP_F_Summary_Top15_FETPValue[1:15,])## FET_OR.odds ratio FET_P.Value Predictor Metric NegativeLog10_FET_P.Value
## FP076 0.1244571 6.187262e-45 FP076 VP_F 44.20850
## FP089 0.1472122 2.145272e-32 FP089 VP_F 31.66852
## FP112 0.1379398 2.701713e-28 FP112 VP_F 27.56836
## FP172 0.1230654 1.375876e-24 FP172 VP_F 23.86142
## FP065 0.2425985 2.350296e-24 FP065 VP_F 23.62888
## FP013 0.1488378 2.727491e-23 FP013 VP_F 22.56424
## FP079 0.2239833 6.263011e-23 FP079 VP_F 22.20322
## FP092 0.2089925 6.412816e-23 FP092 VP_F 22.19295
## FP082 0.2217568 7.799605e-22 FP082 VP_F 21.10793
## FP014 0.1575098 9.678623e-22 FP014 VP_F 21.01419
## FP168 0.2467345 1.869134e-21 FP168 VP_F 20.72836
## FP169 0.1958171 3.513565e-20 FP169 VP_F 19.45425
## FP070 0.2794757 5.654858e-20 FP070 VP_F 19.24758
## FP009 0.2698267 1.890323e-18 FP009 VP_F 17.72346
## FP164 0.2931674 3.185855e-18 FP164 VP_F 17.49677
## LogOddsRatio AbsoluteLogOddsRatio Group
## FP076 -2.083795 2.083795 FP076
## FP089 -1.915880 1.915880 FP089
## FP112 -1.980938 1.980938 FP112
## FP172 -2.095039 2.095039 Others
## FP065 -1.416348 1.416348 Others
## FP013 -1.904898 1.904898 Others
## FP079 -1.496184 1.496184 Others
## FP092 -1.565457 1.565457 Others
## FP082 -1.506174 1.506174 Others
## FP014 -1.848268 1.848268 Others
## FP168 -1.399442 1.399442 Others
## FP169 -1.630574 1.630574 Others
## FP070 -1.274840 1.274840 Others
## FP009 -1.309975 1.309975 Others
## FP164 -1.227011 1.227011 OthersVP_F_Summary_Top15_AbsoluteLogOddsRatio <- VP_F_Summary[order(VP_F_Summary$AbsoluteLogOddsRatio,decreasing=TRUE),]
(VP_F_Summary_Top15_AbsoluteLogOddsRatio <- VP_F_Summary_Top15_AbsoluteLogOddsRatio[1:15,])## FET_OR.odds ratio FET_P.Value Predictor Metric NegativeLog10_FET_P.Value
## FP044 0.05440672 3.102624e-15 FP044 VP_F 14.508271
## FP193 0.08191610 1.128789e-13 FP193 VP_F 12.947387
## FP172 0.12306538 1.375876e-24 FP172 VP_F 23.861421
## FP076 0.12445706 6.187262e-45 FP076 VP_F 44.208501
## FP184 0.13694274 1.118373e-13 FP184 VP_F 12.951413
## FP112 0.13793978 2.701713e-28 FP112 VP_F 27.568361
## FP089 0.14721223 2.145272e-32 FP089 VP_F 31.668518
## FP196 0.14858927 3.201577e-09 FP196 VP_F 8.494636
## FP207 0.14858927 3.201577e-09 FP207 VP_F 8.494636
## FP013 0.14883782 2.727491e-23 FP013 VP_F 22.564237
## FP014 0.15750978 9.678623e-22 FP014 VP_F 21.014186
## FP197 0.16479415 4.266746e-08 FP197 VP_F 7.369903
## FP149 0.17427309 2.945871e-12 FP149 VP_F 11.530786
## FP190 0.18597681 1.732961e-09 FP190 VP_F 8.761211
## FP169 0.19581712 3.513565e-20 FP169 VP_F 19.454252
## LogOddsRatio AbsoluteLogOddsRatio Group
## FP044 -2.911268 2.911268 FP044
## FP193 -2.502060 2.502060 FP193
## FP172 -2.095039 2.095039 Others
## FP076 -2.083795 2.083795 FP076
## FP184 -1.988192 1.988192 Others
## FP112 -1.980938 1.980938 FP112
## FP089 -1.915880 1.915880 FP089
## FP196 -1.906569 1.906569 Others
## FP207 -1.906569 1.906569 Others
## FP013 -1.904898 1.904898 Others
## FP014 -1.848268 1.848268 Others
## FP197 -1.803058 1.803058 Others
## FP149 -1.747132 1.747132 Others
## FP190 -1.682133 1.682133 Others
## FP169 -1.630574 1.630574 Others##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ NegativeLog10_FET_P.Value | Metric,
VP_F_Summary_Top15_FETPValue,
origin = 0,
xlab = "-Log10(Fisher Exact Test P-Value)",
type = c("p","h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
dotplot(Predictor ~ AbsoluteLogOddsRatio | Metric,
VP_F_Summary_Top15_AbsoluteLogOddsRatio,
origin = 0,
xlab = "ABsolute Log Odds Ratio",
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.5.6 Volcano Plot - Gain Ratio (VP_G)
Gain Ratio, based off of the information gain which determines the best features that render maximum information about a class, attempts to lessen its bias from predictors with many levels by introducing a normalizing term called the intrinsic information defined as the entropy of sub-dataset proportions. The presence of high gain ratio values indicate the univariate association between the categorical predictors and the categorical response.
[A] The volcano plot - gain ratio was obtained using the COREtrain package. High raw values of the metric, and the absolute log odds ratio indicate that the factor predictors were associated with the factor response.
[B] The factor predictors which demonstrated the best feature importance in terms of the gain ratio are as follows:
[B.1] FP076 = 0.16518
[B.2] FP044 = 0.14858
[B.3] FP089 = 0.13291
[B.4] FP172 = 0.13177
[B.5] FP112 = 0.13074
[C] The factor predictors which demonstrated the best feature importance in terms of the absolute log odds ratio are as follows:
[C.1] FP044 = 2.911268
[C.2] FP193 = 2.50206
[C.3] FP172 = 2.09503
[C.4] FP076 = 2.08379
[C.5] FP184 = 1.98819
Code Chunk | Output
##################################
# Obtaining the gain ratio
##################################
VP_G <- function(x, y){
tab <- table(x, y)
fet <- fisher.test(tab)
out <- c(FET_OR = fet$estimate,
Gain = attrEval(y ~ x, estimator="GainRatio"))}
##################################
# Formulating the summary table
##################################
VP_G_Summary <- lapply(PMA_PreModelling_Train_Factor,
VP_G,
y = PMA_PreModelling_Train_Factor$Log_Solubility_Class)
VP_G_Summary <- as.data.frame(do.call("rbind", VP_G_Summary))
VP_G_Summary$Gain <- VP_G_Summary$Gain.x
VP_G_Summary$Gain.x <- NULL
VP_G_Summary$Predictor <- names(PMA_PreModelling_Train_Factor)
VP_G_Summary <- VP_G_Summary[-nrow(VP_G_Summary),]
VP_G_Summary$Metric <- rep("VP_G", nrow(VP_G_Summary))
VP_G_Summary$LogOddsRatio <- log(VP_G_Summary[,1])
VP_G_Summary$AbsoluteLogOddsRatio <- abs(VP_G_Summary$LogOddsRatio)
VP_G_Summary$Group <- ifelse(rownames(VP_G_Summary)=="FP076","FP076",
ifelse(rownames(VP_G_Summary)=="FP089","FP089",
ifelse(rownames(VP_G_Summary)=="FP172","FP172",
ifelse(rownames(VP_G_Summary)=="FP044","FP044",
ifelse(rownames(VP_G_Summary)=="FP193","FP193",
"Others")))))
VP_G_Summary## FET_OR.odds ratio Gain Predictor Metric LogOddsRatio
## FP001 1.19708080 1.444996e-03 FP001 VP_G 0.179885927
## FP002 0.35039786 4.708223e-02 FP002 VP_G -1.048686041
## FP003 0.97166586 3.674512e-05 FP003 VP_G -0.028743299
## FP004 1.26919078 2.522331e-03 FP004 VP_G 0.238379513
## FP005 0.35435518 4.551018e-02 FP005 VP_G -1.037455546
## FP006 1.84346020 1.618232e-02 FP006 VP_G 0.611644351
## FP007 0.88537336 6.497937e-04 FP007 VP_G -0.121745848
## FP008 0.57340983 1.334323e-02 FP008 VP_G -0.556154591
## FP009 0.26982672 6.919817e-02 FP009 VP_G -1.309975319
## FP010 1.57515069 7.731544e-03 FP010 VP_G 0.454350945
## FP011 1.90083496 1.575628e-02 FP011 VP_G 0.642293242
## FP012 1.10579162 3.890917e-04 FP012 VP_G 0.100561473
## FP013 0.14883782 1.170524e-01 FP013 VP_G -1.904898041
## FP014 0.15750978 1.104560e-01 FP014 VP_G -1.848267748
## FP015 2.86928927 3.948765e-02 FP015 VP_G 1.054064357
## FP016 0.85604079 9.035850e-04 FP016 VP_G -0.155437247
## FP017 0.49376123 1.837501e-02 FP017 VP_G -0.705703211
## FP018 0.47679807 1.979839e-02 FP018 VP_G -0.740662219
## FP019 1.04400654 6.620556e-05 FP019 VP_G 0.043065754
## FP020 1.28698533 2.228941e-03 FP020 VP_G 0.252302533
## FP021 0.74658209 3.076260e-03 FP021 VP_G -0.292249695
## FP022 1.02076928 1.460657e-05 FP022 VP_G 0.020556543
## FP023 0.54497814 1.321735e-02 FP023 VP_G -0.607009590
## FP024 1.09109789 2.662594e-04 FP024 VP_G 0.087184424
## FP025 1.30887702 2.513100e-03 FP025 VP_G 0.269169534
## FP026 0.64245234 6.517365e-03 FP026 VP_G -0.442462641
## FP027 2.01654412 1.547364e-02 FP027 VP_G 0.701385217
## FP028 1.21715865 1.323858e-03 FP028 VP_G 0.196519164
## FP029 0.85300530 8.767322e-04 FP029 VP_G -0.158989520
## FP030 1.42811535 4.164198e-03 FP030 VP_G 0.356355635
## FP031 0.81964988 1.335234e-03 FP031 VP_G -0.198878004
## FP032 1.72977657 9.085596e-03 FP032 VP_G 0.547992253
## FP033 1.56955042 6.160663e-03 FP033 VP_G 0.450789220
## FP034 0.71399401 3.744446e-03 FP034 VP_G -0.336880708
## FP035 0.28405176 4.681628e-02 FP035 VP_G -1.258598799
## FP036 2.60842408 2.580646e-02 FP036 VP_G 0.958746238
## FP037 0.60007108 8.341477e-03 FP037 VP_G -0.510707161
## FP038 1.37688518 3.308563e-03 FP038 VP_G 0.319823833
## FP039 0.27168922 5.011275e-02 FP039 VP_G -1.303096431
## FP040 1.91054797 1.225945e-02 FP040 VP_G 0.647390098
## FP041 0.29980664 4.192846e-02 FP041 VP_G -1.204617559
## FP042 1.29903467 2.037164e-03 FP042 VP_G 0.261621424
## FP043 0.63257683 6.634154e-03 FP043 VP_G -0.457953588
## FP044 0.05440672 1.485800e-01 FP044 VP_G -2.911267576
## FP045 0.39880063 2.481611e-02 FP045 VP_G -0.919293663
## FP046 0.47005989 2.432271e-02 FP046 VP_G -0.754895161
## FP047 0.65690301 7.410695e-03 FP047 VP_G -0.420218893
## FP048 0.82285525 1.375230e-03 FP048 VP_G -0.194974974
## FP049 0.19582389 8.320908e-02 FP049 VP_G -1.630539536
## FP050 0.60302823 9.042809e-03 FP050 VP_G -0.505791274
## FP051 0.51334022 1.549500e-02 FP051 VP_G -0.666816450
## FP052 1.23362693 1.461205e-03 FP052 VP_G 0.209958553
## FP053 0.23582634 6.331973e-02 FP053 VP_G -1.444659604
## FP054 0.30817566 4.194124e-02 FP054 VP_G -1.177085338
## FP055 1.67259146 7.509525e-03 FP055 VP_G 0.514374198
## FP056 0.26122657 5.112509e-02 FP056 VP_G -1.342367160
## FP057 0.79249223 1.943775e-03 FP057 VP_G -0.232572576
## FP058 0.93853294 1.423960e-04 FP058 VP_G -0.063437329
## FP059 0.28137500 4.445829e-02 FP059 VP_G -1.268066990
## FP060 1.63240884 1.063821e-02 FP060 VP_G 0.490056742
## FP061 1.04977625 1.049998e-04 FP061 VP_G 0.048577050
## FP062 1.29158337 2.901668e-03 FP062 VP_G 0.255868883
## FP063 1.36519387 4.278415e-03 FP063 VP_G 0.311296446
## FP064 1.18728146 1.303357e-03 FP064 VP_G 0.171666208
## FP065 0.24259847 8.119361e-02 FP065 VP_G -1.416347592
## FP066 1.00753443 2.484327e-06 FP066 VP_G 0.007506185
## FP067 1.15740510 9.366937e-04 FP067 VP_G 0.146180513
## FP068 1.12634107 6.173353e-04 FP068 VP_G 0.118974389
## FP069 0.85481954 1.078357e-03 FP069 VP_G -0.156864895
## FP070 0.27947568 6.832822e-02 FP070 VP_G -1.274839999
## FP071 0.32504356 5.309215e-02 FP071 VP_G -1.123796088
## FP072 2.69986223 4.205633e-02 FP072 VP_G 0.993200747
## FP073 1.74346468 1.286319e-02 FP073 VP_G 0.555874326
## FP074 1.20851743 1.537511e-03 FP074 VP_G 0.189394345
## FP075 1.32082732 3.326825e-03 FP075 VP_G 0.278258294
## FP076 0.12445706 1.651819e-01 FP076 VP_G -2.083794540
## FP077 0.76099672 3.222236e-03 FP077 VP_G -0.273126235
## FP078 0.91855970 3.087306e-04 FP078 VP_G -0.084948384
## FP079 0.22398333 8.315958e-02 FP079 VP_G -1.496183628
## FP080 1.25723356 2.217248e-03 FP080 VP_G 0.228913722
## FP081 0.72911277 4.222848e-03 FP081 VP_G -0.315926873
## FP082 0.22175679 8.202021e-02 FP082 VP_G -1.506174057
## FP083 1.79705328 1.391607e-02 FP083 VP_G 0.586148259
## FP084 1.13637183 6.880795e-04 FP084 VP_G 0.127840582
## FP085 0.27122453 6.744040e-02 FP085 VP_G -1.304808259
## FP086 0.64974151 7.816398e-03 FP086 VP_G -0.431180676
## FP087 0.25801089 6.720425e-02 FP087 VP_G -1.354753489
## FP088 1.57116925 8.298839e-03 FP088 VP_G 0.451820085
## FP089 0.14721223 1.329106e-01 FP089 VP_G -1.915879958
## FP090 0.64237027 8.128373e-03 FP090 VP_G -0.442590395
## FP091 0.97800429 2.005882e-05 FP091 VP_G -0.022241223
## FP092 0.20899251 9.317801e-02 FP092 VP_G -1.565456851
## FP093 0.49172261 2.066734e-02 FP093 VP_G -0.709840517
## FP094 0.99476660 1.122062e-06 FP094 VP_G -0.005247142
## FP095 1.21598118 1.528442e-03 FP095 VP_G 0.195551307
## FP096 0.79866626 2.047708e-03 FP096 VP_G -0.224812113
## FP097 0.27373537 6.540942e-02 FP097 VP_G -1.295593440
## FP098 1.28105523 2.481631e-03 FP098 VP_G 0.247684138
## FP099 0.47185118 2.279089e-02 FP099 VP_G -0.751091647
## FP100 1.36192602 3.823449e-03 FP100 VP_G 0.308899886
## FP101 1.15291250 8.233660e-04 FP101 VP_G 0.142291351
## FP102 0.54942707 1.425853e-02 FP102 VP_G -0.598879231
## FP103 0.58576485 1.157244e-02 FP103 VP_G -0.534836847
## FP104 0.63194616 8.565860e-03 FP104 VP_G -0.458951075
## FP105 0.48316770 2.118709e-02 FP105 VP_G -0.727391471
## FP106 0.65902108 6.873749e-03 FP106 VP_G -0.416999764
## FP107 0.27844164 6.227834e-02 FP107 VP_G -1.278546809
## FP108 1.01457497 8.332361e-06 FP108 VP_G 0.014469780
## FP109 0.61805388 8.997866e-03 FP109 VP_G -0.481179637
## FP110 1.37134941 3.901093e-03 FP110 VP_G 0.315795222
## FP111 0.89745661 4.641219e-04 FP111 VP_G -0.108190502
## FP112 0.13793978 1.307413e-01 FP112 VP_G -1.980938083
## FP113 1.44943098 5.305027e-03 FP113 VP_G 0.371171050
## FP114 0.73592698 3.568467e-03 FP114 VP_G -0.306624372
## FP115 0.87408678 7.052371e-04 FP115 VP_G -0.134575613
## FP116 1.15250268 7.866812e-04 FP116 VP_G 0.141935823
## FP117 0.60131089 1.007118e-02 FP117 VP_G -0.508643191
## FP118 1.28812070 2.484224e-03 FP118 VP_G 0.253184331
## FP119 1.11895502 4.775937e-04 FP119 VP_G 0.112395234
## FP120 1.32008395 2.891538e-03 FP120 VP_G 0.277695331
## FP121 0.54424360 1.364140e-02 FP121 VP_G -0.608358336
## FP122 0.97085516 3.333730e-05 FP122 VP_G -0.029577988
## FP123 0.47156532 2.145802e-02 FP123 VP_G -0.751697650
## FP124 1.17599458 9.889777e-04 FP124 VP_G 0.162114244
## FP125 1.15573797 7.827169e-04 FP125 VP_G 0.144739079
## FP126 1.01045303 4.030737e-06 FP126 VP_G 0.010398773
## FP127 1.41857268 4.385366e-03 FP127 VP_G 0.349651214
## FP128 1.37469908 3.691278e-03 FP128 VP_G 0.318234855
## FP129 0.51443239 1.621123e-02 FP129 VP_G -0.664691145
## FP130 0.66502981 5.813460e-03 FP130 VP_G -0.407923418
## FP131 0.95783416 6.698224e-05 FP131 VP_G -0.043080623
## FP132 2.04266955 1.697683e-02 FP132 VP_G 0.714257556
## FP133 1.11789560 4.449154e-04 FP133 VP_G 0.111447989
## FP134 0.57195802 1.130280e-02 FP134 VP_G -0.558689675
## FP135 0.95783416 6.698224e-05 FP135 VP_G -0.043080623
## FP136 1.06770699 1.526877e-04 FP136 VP_G 0.065513353
## FP137 0.59399949 9.641656e-03 FP137 VP_G -0.520876823
## FP138 0.64186857 6.947789e-03 FP138 VP_G -0.443371715
## FP139 0.80019382 1.647760e-03 FP139 VP_G -0.222901302
## FP140 1.39373024 3.767078e-03 FP140 VP_G 0.331983776
## FP141 0.49290239 1.764194e-02 FP141 VP_G -0.707444112
## FP142 0.82977493 1.226393e-03 FP142 VP_G -0.186600779
## FP143 0.73530968 3.128164e-03 FP143 VP_G -0.307463530
## FP144 1.63603800 7.995099e-03 FP144 VP_G 0.492277463
## FP145 1.34827701 3.017428e-03 FP145 VP_G 0.298827485
## FP146 0.27276366 5.385873e-02 FP146 VP_G -1.299149556
## FP147 1.14075786 5.964217e-04 FP147 VP_G 0.131692828
## FP148 1.25912604 1.738537e-03 FP148 VP_G 0.230417865
## FP149 0.17427309 8.590156e-02 FP149 VP_G -1.747131733
## FP150 1.10335419 3.137235e-04 FP150 VP_G 0.098354800
## FP151 1.15739238 6.409558e-04 FP151 VP_G 0.146169523
## FP152 0.67652104 5.062911e-03 FP152 VP_G -0.390791731
## FP153 0.59654511 8.715026e-03 FP153 VP_G -0.516600423
## FP155 0.23782568 5.826784e-02 FP155 VP_G -1.436217309
## FP156 0.56197587 1.058664e-02 FP156 VP_G -0.576296365
## FP157 0.89614943 3.749301e-04 FP157 VP_G -0.109648111
## FP158 1.83744533 1.033477e-02 FP158 VP_G 0.608376197
## FP159 0.60007108 8.341477e-03 FP159 VP_G -0.510707161
## FP160 0.94725316 9.332815e-05 FP160 VP_G -0.054188894
## FP161 4.68576694 5.515771e-02 FP161 VP_G 1.544529604
## FP162 0.39641239 3.726946e-02 FP162 VP_G -0.925300229
## FP163 1.30035228 3.072756e-03 FP163 VP_G 0.262635216
## FP164 0.29316745 6.099461e-02 FP164 VP_G -1.227011339
## FP165 1.14157018 7.607483e-04 FP165 VP_G 0.132404668
## FP166 0.52535686 1.786823e-02 FP166 VP_G -0.643677518
## FP167 1.16920320 1.050288e-03 FP167 VP_G 0.156322489
## FP168 0.24673455 7.549097e-02 FP168 VP_G -1.399442231
## FP169 0.19581712 9.334163e-02 FP169 VP_G -1.630574123
## FP170 0.57865361 1.170409e-02 FP170 VP_G -0.547051236
## FP171 1.60655269 8.298564e-03 FP171 VP_G 0.474090698
## FP172 0.12306538 1.317752e-01 FP172 VP_G -2.095039482
## FP173 0.45698434 2.243736e-02 FP173 VP_G -0.783106166
## FP174 0.65612868 6.490726e-03 FP174 VP_G -0.421398357
## FP175 0.94595425 1.132239e-04 FP175 VP_G -0.055561071
## FP176 1.00334103 3.981905e-07 FP176 VP_G 0.003335459
## FP177 0.87473364 6.435415e-04 FP177 VP_G -0.133835847
## FP178 0.49966530 1.711553e-02 FP178 VP_G -0.693816809
## FP179 0.67434769 5.342073e-03 FP179 VP_G -0.394009441
## FP180 1.42373128 4.219579e-03 FP180 VP_G 0.353281085
## FP181 0.26768983 5.396589e-02 FP181 VP_G -1.317926317
## FP182 1.22466105 1.385412e-03 FP182 VP_G 0.202664115
## FP183 1.58365219 6.522956e-03 FP183 VP_G 0.459733693
## FP184 0.13694274 1.025154e-01 FP184 VP_G -1.988192371
## FP185 0.37563701 3.068609e-02 FP185 VP_G -0.979131989
## FP186 1.18281337 9.032186e-04 FP186 VP_G 0.167895811
## FP187 0.93790027 1.322768e-04 FP187 VP_G -0.064111657
## FP188 1.77119205 9.687088e-03 FP188 VP_G 0.571652797
## FP189 0.57675161 9.846444e-03 FP189 VP_G -0.550343597
## FP190 0.18597681 7.641501e-02 FP190 VP_G -1.682133264
## FP191 1.14571288 5.837548e-04 FP191 VP_G 0.136027042
## FP192 0.51452638 1.356897e-02 FP192 VP_G -0.664508453
## FP193 0.08191610 1.275872e-01 FP193 VP_G -2.502059785
## FP194 1.18034939 8.328458e-04 FP194 VP_G 0.165810490
## FP195 3.72159008 4.193747e-02 FP195 VP_G 1.314151018
## FP196 0.14858927 8.701108e-02 FP196 VP_G -1.906569334
## FP197 0.16479415 7.859039e-02 FP197 VP_G -1.803058135
## FP198 2.00709087 1.349541e-02 FP198 VP_G 0.696686345
## FP201 1.78135621 9.320368e-03 FP201 VP_G 0.577374992
## FP202 0.52577576 1.716021e-02 FP202 VP_G -0.642880476
## FP203 0.95670909 6.926750e-05 FP203 VP_G -0.044255912
## FP204 0.54424384 1.266393e-02 FP204 VP_G -0.608357895
## FP205 0.31642129 4.050846e-02 FP205 VP_G -1.150680746
## FP206 0.45381997 1.889706e-02 FP206 VP_G -0.790054693
## FP207 0.14858927 8.701108e-02 FP207 VP_G -1.906569334
## FP208 0.92038091 2.431613e-04 FP208 VP_G -0.082967663
## AbsoluteLogOddsRatio Group
## FP001 0.179885927 Others
## FP002 1.048686041 Others
## FP003 0.028743299 Others
## FP004 0.238379513 Others
## FP005 1.037455546 Others
## FP006 0.611644351 Others
## FP007 0.121745848 Others
## FP008 0.556154591 Others
## FP009 1.309975319 Others
## FP010 0.454350945 Others
## FP011 0.642293242 Others
## FP012 0.100561473 Others
## FP013 1.904898041 Others
## FP014 1.848267748 Others
## FP015 1.054064357 Others
## FP016 0.155437247 Others
## FP017 0.705703211 Others
## FP018 0.740662219 Others
## FP019 0.043065754 Others
## FP020 0.252302533 Others
## FP021 0.292249695 Others
## FP022 0.020556543 Others
## FP023 0.607009590 Others
## FP024 0.087184424 Others
## FP025 0.269169534 Others
## FP026 0.442462641 Others
## FP027 0.701385217 Others
## FP028 0.196519164 Others
## FP029 0.158989520 Others
## FP030 0.356355635 Others
## FP031 0.198878004 Others
## FP032 0.547992253 Others
## FP033 0.450789220 Others
## FP034 0.336880708 Others
## FP035 1.258598799 Others
## FP036 0.958746238 Others
## FP037 0.510707161 Others
## FP038 0.319823833 Others
## FP039 1.303096431 Others
## FP040 0.647390098 Others
## FP041 1.204617559 Others
## FP042 0.261621424 Others
## FP043 0.457953588 Others
## FP044 2.911267576 FP044
## FP045 0.919293663 Others
## FP046 0.754895161 Others
## FP047 0.420218893 Others
## FP048 0.194974974 Others
## FP049 1.630539536 Others
## FP050 0.505791274 Others
## FP051 0.666816450 Others
## FP052 0.209958553 Others
## FP053 1.444659604 Others
## FP054 1.177085338 Others
## FP055 0.514374198 Others
## FP056 1.342367160 Others
## FP057 0.232572576 Others
## FP058 0.063437329 Others
## FP059 1.268066990 Others
## FP060 0.490056742 Others
## FP061 0.048577050 Others
## FP062 0.255868883 Others
## FP063 0.311296446 Others
## FP064 0.171666208 Others
## FP065 1.416347592 Others
## FP066 0.007506185 Others
## FP067 0.146180513 Others
## FP068 0.118974389 Others
## FP069 0.156864895 Others
## FP070 1.274839999 Others
## FP071 1.123796088 Others
## FP072 0.993200747 Others
## FP073 0.555874326 Others
## FP074 0.189394345 Others
## FP075 0.278258294 Others
## FP076 2.083794540 FP076
## FP077 0.273126235 Others
## FP078 0.084948384 Others
## FP079 1.496183628 Others
## FP080 0.228913722 Others
## FP081 0.315926873 Others
## FP082 1.506174057 Others
## FP083 0.586148259 Others
## FP084 0.127840582 Others
## FP085 1.304808259 Others
## FP086 0.431180676 Others
## FP087 1.354753489 Others
## FP088 0.451820085 Others
## FP089 1.915879958 FP089
## FP090 0.442590395 Others
## FP091 0.022241223 Others
## FP092 1.565456851 Others
## FP093 0.709840517 Others
## FP094 0.005247142 Others
## FP095 0.195551307 Others
## FP096 0.224812113 Others
## FP097 1.295593440 Others
## FP098 0.247684138 Others
## FP099 0.751091647 Others
## FP100 0.308899886 Others
## FP101 0.142291351 Others
## FP102 0.598879231 Others
## FP103 0.534836847 Others
## FP104 0.458951075 Others
## FP105 0.727391471 Others
## FP106 0.416999764 Others
## FP107 1.278546809 Others
## FP108 0.014469780 Others
## FP109 0.481179637 Others
## FP110 0.315795222 Others
## FP111 0.108190502 Others
## FP112 1.980938083 Others
## FP113 0.371171050 Others
## FP114 0.306624372 Others
## FP115 0.134575613 Others
## FP116 0.141935823 Others
## FP117 0.508643191 Others
## FP118 0.253184331 Others
## FP119 0.112395234 Others
## FP120 0.277695331 Others
## FP121 0.608358336 Others
## FP122 0.029577988 Others
## FP123 0.751697650 Others
## FP124 0.162114244 Others
## FP125 0.144739079 Others
## FP126 0.010398773 Others
## FP127 0.349651214 Others
## FP128 0.318234855 Others
## FP129 0.664691145 Others
## FP130 0.407923418 Others
## FP131 0.043080623 Others
## FP132 0.714257556 Others
## FP133 0.111447989 Others
## FP134 0.558689675 Others
## FP135 0.043080623 Others
## FP136 0.065513353 Others
## FP137 0.520876823 Others
## FP138 0.443371715 Others
## FP139 0.222901302 Others
## FP140 0.331983776 Others
## FP141 0.707444112 Others
## FP142 0.186600779 Others
## FP143 0.307463530 Others
## FP144 0.492277463 Others
## FP145 0.298827485 Others
## FP146 1.299149556 Others
## FP147 0.131692828 Others
## FP148 0.230417865 Others
## FP149 1.747131733 Others
## FP150 0.098354800 Others
## FP151 0.146169523 Others
## FP152 0.390791731 Others
## FP153 0.516600423 Others
## FP155 1.436217309 Others
## FP156 0.576296365 Others
## FP157 0.109648111 Others
## FP158 0.608376197 Others
## FP159 0.510707161 Others
## FP160 0.054188894 Others
## FP161 1.544529604 Others
## FP162 0.925300229 Others
## FP163 0.262635216 Others
## FP164 1.227011339 Others
## FP165 0.132404668 Others
## FP166 0.643677518 Others
## FP167 0.156322489 Others
## FP168 1.399442231 Others
## FP169 1.630574123 Others
## FP170 0.547051236 Others
## FP171 0.474090698 Others
## FP172 2.095039482 FP172
## FP173 0.783106166 Others
## FP174 0.421398357 Others
## FP175 0.055561071 Others
## FP176 0.003335459 Others
## FP177 0.133835847 Others
## FP178 0.693816809 Others
## FP179 0.394009441 Others
## FP180 0.353281085 Others
## FP181 1.317926317 Others
## FP182 0.202664115 Others
## FP183 0.459733693 Others
## FP184 1.988192371 Others
## FP185 0.979131989 Others
## FP186 0.167895811 Others
## FP187 0.064111657 Others
## FP188 0.571652797 Others
## FP189 0.550343597 Others
## FP190 1.682133264 Others
## FP191 0.136027042 Others
## FP192 0.664508453 Others
## FP193 2.502059785 FP193
## FP194 0.165810490 Others
## FP195 1.314151018 Others
## FP196 1.906569334 Others
## FP197 1.803058135 Others
## FP198 0.696686345 Others
## FP201 0.577374992 Others
## FP202 0.642880476 Others
## FP203 0.044255912 Others
## FP204 0.608357895 Others
## FP205 1.150680746 Others
## FP206 0.790054693 Others
## FP207 1.906569334 Others
## FP208 0.082967663 Others##################################
# Selecting the best-performing
# predictors based from metrics
##################################
VP_G_Summary_Top15_Gain <- VP_G_Summary[order(VP_G_Summary$Gain,decreasing=TRUE),]
(VP_G_Summary_Top15_Gain <- VP_G_Summary_Top15_Gain[1:15,])## FET_OR.odds ratio Gain Predictor Metric LogOddsRatio
## FP076 0.12445706 0.16518193 FP076 VP_G -2.083795
## FP044 0.05440672 0.14857995 FP044 VP_G -2.911268
## FP089 0.14721223 0.13291055 FP089 VP_G -1.915880
## FP172 0.12306538 0.13177523 FP172 VP_G -2.095039
## FP112 0.13793978 0.13074132 FP112 VP_G -1.980938
## FP193 0.08191610 0.12758720 FP193 VP_G -2.502060
## FP013 0.14883782 0.11705243 FP013 VP_G -1.904898
## FP014 0.15750978 0.11045599 FP014 VP_G -1.848268
## FP184 0.13694274 0.10251539 FP184 VP_G -1.988192
## FP169 0.19581712 0.09334163 FP169 VP_G -1.630574
## FP092 0.20899251 0.09317801 FP092 VP_G -1.565457
## FP196 0.14858927 0.08701108 FP196 VP_G -1.906569
## FP207 0.14858927 0.08701108 FP207 VP_G -1.906569
## FP149 0.17427309 0.08590156 FP149 VP_G -1.747132
## FP049 0.19582389 0.08320908 FP049 VP_G -1.630540
## AbsoluteLogOddsRatio Group
## FP076 2.083795 FP076
## FP044 2.911268 FP044
## FP089 1.915880 FP089
## FP172 2.095039 FP172
## FP112 1.980938 Others
## FP193 2.502060 FP193
## FP013 1.904898 Others
## FP014 1.848268 Others
## FP184 1.988192 Others
## FP169 1.630574 Others
## FP092 1.565457 Others
## FP196 1.906569 Others
## FP207 1.906569 Others
## FP149 1.747132 Others
## FP049 1.630540 OthersVP_G_Summary_Top15_AbsoluteLogOddsRatio <- VP_G_Summary[order(VP_G_Summary$AbsoluteLogOddsRatio,decreasing=TRUE),]
(VP_G_Summary_Top15_AbsoluteLogOddsRatio <- VP_G_Summary_Top15_AbsoluteLogOddsRatio[1:15,])## FET_OR.odds ratio Gain Predictor Metric LogOddsRatio
## FP044 0.05440672 0.14857995 FP044 VP_G -2.911268
## FP193 0.08191610 0.12758720 FP193 VP_G -2.502060
## FP172 0.12306538 0.13177523 FP172 VP_G -2.095039
## FP076 0.12445706 0.16518193 FP076 VP_G -2.083795
## FP184 0.13694274 0.10251539 FP184 VP_G -1.988192
## FP112 0.13793978 0.13074132 FP112 VP_G -1.980938
## FP089 0.14721223 0.13291055 FP089 VP_G -1.915880
## FP196 0.14858927 0.08701108 FP196 VP_G -1.906569
## FP207 0.14858927 0.08701108 FP207 VP_G -1.906569
## FP013 0.14883782 0.11705243 FP013 VP_G -1.904898
## FP014 0.15750978 0.11045599 FP014 VP_G -1.848268
## FP197 0.16479415 0.07859039 FP197 VP_G -1.803058
## FP149 0.17427309 0.08590156 FP149 VP_G -1.747132
## FP190 0.18597681 0.07641501 FP190 VP_G -1.682133
## FP169 0.19581712 0.09334163 FP169 VP_G -1.630574
## AbsoluteLogOddsRatio Group
## FP044 2.911268 FP044
## FP193 2.502060 FP193
## FP172 2.095039 FP172
## FP076 2.083795 FP076
## FP184 1.988192 Others
## FP112 1.980938 Others
## FP089 1.915880 FP089
## FP196 1.906569 Others
## FP207 1.906569 Others
## FP013 1.904898 Others
## FP014 1.848268 Others
## FP197 1.803058 Others
## FP149 1.747132 Others
## FP190 1.682133 Others
## FP169 1.630574 Others##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ Gain | Metric,
VP_G_Summary_Top15_Gain,
origin = 0,
xlab = "Gain Ratio",
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
dotplot(Predictor ~ AbsoluteLogOddsRatio | Metric,
VP_G_Summary_Top15_AbsoluteLogOddsRatio,
origin = 0,
xlab = "Absolute Log Odds Ratio",
type = c("p", "h"),
pch = 16,
cex = 2,
alpha = 0.45,
prepanel = function(x, y) {
list(ylim = levels(reorder(y, x)))
},
panel = function(x, y, ...) {
panel.dotplot(x, reorder(y, x), ...)
})
1.6 Consolidated Findings
[A] The model-independent feature importance for all numeric predictors were evaluated in terms of the following metrics:
[A.1] AUROC: Area Under the Receiver Operating Characteristics Curve (caret package)
[A.2] ATS : Absolute T-Test Statistic (stats package)
[A.3] MIC: Maximal Information Coefficient (minerva package)
[A.4] RV: Relief Values (CORElearn package)
[B] The numeric predictors which demonstrated the most consistent feature importance across the given metrics are as follows:
[B.1] MolWeight
[B.2] NumCarbon
[B.3] NumMultBonds
[B.4] NumBonds
[B.5] NumRings
[C] The model-independent feature importance for all factor predictors were evaluated in terms of the following metrics:
[C.1] VP_F: Volcano Plot - Fisher’s Exact Test (stats package)
[C.2] VP_G: Volcano Plot - Gain Ratio (CORElearn package)
[D] The factor predictors which demonstrated the most consistent feature importance across the given metrics are as follows:
[D.1] FP076
[D.2] FP044
[D.3] FP089
[D.4] FP193
[D.5] FP112
Code Chunk | Output
##################################
# Consolidating all performance metrics
# for the numeric predictors
##################################
NumericPredictor_Metrics <- cbind(AUROC_Summary$AUROC,
ATS_Summary$ATS,
MIC_Summary$MIC,
RV_Summary$RV)
colnames(NumericPredictor_Metrics) <- c("AUROC",
"ATS",
"MIC",
"RV")
rownames(NumericPredictor_Metrics) <- names(PMA_PreModelling_Train_Numeric)[2:ncol(PMA_PreModelling_Train_Numeric)]
NumericPredictor_Metrics <- as.data.frame(NumericPredictor_Metrics)
NumericPredictor_Metrics$Group <- ifelse(rownames(NumericPredictor_Metrics)=="MolWeight","MolWeight",
ifelse(rownames(NumericPredictor_Metrics)=="NumCarbon","NumCarbon",
ifelse(rownames(NumericPredictor_Metrics)=="NumMultBonds","NumMultBonds",
ifelse(rownames(NumericPredictor_Metrics)=="NumBonds","NumBonds",
ifelse(rownames(NumericPredictor_Metrics)=="NumRings","NumRings",
"Others")))))
NumericPredictor_Metrics## AUROC ATS MIC RV Group
## MolWeight 0.8441930 22.0675750 0.46368934 0.30039075 MolWeight
## NumBonds 0.7893255 16.4907720 0.28498465 0.20575373 NumBonds
## NumMultBonds 0.7230098 13.0920514 0.19005119 0.08333333 NumMultBonds
## NumRotBonds 0.5847695 5.8690062 0.03861351 0.08825000 Others
## NumDblBonds 0.5142035 1.9672950 0.01133118 0.00200000 Others
## NumCarbon 0.8408857 21.0036355 0.31544845 0.28938624 NumCarbon
## NumNitrogen 0.5285634 0.8247139 0.01995159 0.10800000 Others
## NumOxygen 0.5536988 0.9312510 0.07491145 0.07476923 Others
## NumSulfer 0.5317902 3.2021823 0.01254889 0.01600000 Others
## NumChlorine 0.6104904 8.3362925 0.07010055 0.01400000 Others
## NumHalogen 0.6381755 9.1800512 0.08459159 0.08400000 Others
## NumRings 0.7438569 14.3956856 0.16421351 0.20600000 NumRings
## HydrophilicFactor 0.6699367 7.3321483 0.32734124 0.04048816 Others
## SurfaceArea1 0.5726889 2.5597702 0.19959108 0.05630307 Others
## SurfaceArea2 0.5479915 1.1459666 0.22267432 0.08079488 Otherssplom(~NumericPredictor_Metrics[,c(1:4)],
groups = NumericPredictor_Metrics$Group,
pch = 16,
cex = 2,
alpha = 0.45,
varnames = c("AUROC", "ATS", "MIC", "RV"),
auto.key = list(points=TRUE, space="top", columns=2),
main = "Feature Importance Comparison for Numeric Predictors",
xlab = "Scatterplot Matrix of Feature Importance Metrics")
##################################
# Consolidating all performance metrics
# for the factor predictors
##################################
FET_P.Value_Plot <- xyplot(NegativeLog10_FET_P.Value ~ LogOddsRatio,
groups = VP_F_Summary$Group,
data = VP_F_Summary,
xlab = "Log Odds Ratio",
ylab = "-Log10(Fisher Exact Test P-Value)",
type = "p",
pch = 16,
cex = 2,
alpha = 0.45,
auto.key = list(points=TRUE, space="top", columns=2),
main = "Feature Importance Comparison for Factor Predictors")
Gain_Plot <- xyplot(Gain ~ LogOddsRatio,
groups = VP_G_Summary$Group,
data = VP_G_Summary,
xlab = "Log Odds Ratio",
ylab = "Gain Ratio",
type = "p",
pch = 16,
cex = 2,
alpha = 0.45,
auto.key = list(points=TRUE, space="top", columns=2),
main = "Feature Importance Comparison for Factor Predictors")
grid.arrange(FET_P.Value_Plot,
Gain_Plot,
ncol = 2) 
3. References
[Book] Applied Predictive Modeling by Max Kuhn and Kjell Johnson
[Book] An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Rob Tibshirani
[Book] Multivariate Data Visualization with R by Deepayan Sarkar
[Book] Machine Learning by Samuel Jackson
[Book] Data Modeling Methods by Jacob Larget
[Book] Introduction to R by Sara Bonnin
[R Package] AppliedPredictiveModeling by Max Kuhn
[R Package] caret by Max Kuhn
[R Package] rpart by Terry Therneau and Beth Atkinson
[R Package] lattice by Deepayan Sarkar
[R Package] dplyr by Hadley Wickham
[R Package] moments by Lukasz Komsta and Frederick
[R Package] skimr by Elin Waring
[R Package] RANN by Sunil Arya, David Mount, Samuel Kemp and Gregory Jefferis
[R Package] corrplot by Taiyun Wei
[R Package] tidyverse by Hadley Wickham
[R Package] lares by Bernardo Lares
[R Package] DMwR2 by Luis Torgo
[R Package] gridExtra by Baptiste Auguie and Anton Antonov
[R Package] rattle by Graham Williams
[R Package] rpart.plot by Stephen Milborrow
[R Package] RColorBrewer by Erich Neuwirth
[R Package] stats by R Core Team
[R Package] pls by Kristian Hovde Liland
[R Package] nnet by Brian Ripley
[R Package] elasticnet by Hui Zou
[R Package] earth by Stephen Milborrow
[R Package] party by Torsten Hothorn
[R Package] kernlab by Alexandros Karatzoglou
[R Package] randomForest by Andy Liaw
[R Package] Cubist by Max Kuhn
[R Package] minerva by Michele Filosi
[R Package] CORElearn by Marko Robnik-Sikonja and Petr Savicky
[Article] The caret Package by Max Kuhn
[Article] A Short Introduction to the caret Package by Max Kuhn
[Article] Caret Package – A Practical Guide to Machine Learning in R by Selva Prabhakaran
[Article] Tuning Machine Learning Models Using the Caret R Package by Jason Brownlee
[Article] Lattice Graphs by STHDA Team
[Article] A Tour of Machine Learning Algorithms by Jason Brownlee
[Article] Maximal Information Coefficient by R Bloggers Team
[Article] Understanding t-Tests: 1-sample, 2-sample, and Paired t-Tests by Minitab Support Team
[Article] How to Interpret a ROC Curve (With Examples) by Statology Team
[Article] Fisher’s Exact Test by Stats Test Team
[Article] Visualization of Volcano Plots in R by Samuel David Gamboa-Tuz
[Article] Using Volcano Plots in R to Visualize Microarray and RNA-seq Results by Stephen Turner
[Publication] Signal Detection Theory and ROC Analysis by J Egan ( Series in Cognition and Perception)
[Publication] Detecting Novel Associations in Large Data Sets by David Reshef, Yakir Reshef, Hilary Finucane, Sharon Grossman, Gilean Mcvean, Peter Turnbaugh, Eric Lander, Michael Mitzenmacher and Pardis Sabeti (Science)
[Publication] Theoretical and Empirical Analysis of ReliefF and RReliefF by Marko Robnik-Šikonja and Igor Kononenko (Machine Learning)
[Publication] First (?) Occurrence of Common Terms in Probability and Statistics-A Second List, with Corrections by H David (The American Statistician)
[Publication] On the Interpretation of x2 from Contingency Tables, and the Calculation of P by Ronald Fisher (Journal of the Royal Statistical Society)
[Publication] Induction of Decision Trees by John Quinlan (Computer Science)
[Course] Applied Data Mining and Statistical Learning by Penn State Eberly College of Science
[Course] Regression Methods by Penn State Eberly College of Science
[Course] Applied Regression Analysis by Penn State Eberly College of Science