Feature Selection : Evaluating Model-Independent Feature Importance for Predictors with Numeric Responses
John Pauline Pineda
December 14, 2022
1. Table of Contents
This project explores different model-independent feature importance metrics for predictors with numeric responses using various helpful packages in R. Metrics applied in the analysis to evaluate feature importance for numeric predictors included the Locally Weighted Scatterplot Smoothing Pseudo-R-Squared, Pearson’s Correlation Coefficient, Spearman’s Rank Correlation Coefficient, Maximal Information Coefficient and Relief Values, while that for factor predictors included the Volcano Plot Using T-Test. 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.
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 variable (numeric)
[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(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))
##################################
# Performing a general exploration of the train set
##################################
dim(Solubility_Train)## [1] 951 229str(Solubility_Train)## 'data.frame': 951 obs. of 229 variables:
## $ solTrainY : num -3.97 -3.98 -3.99 -4 -4.06 -4.08 -4.08 -4.1 -4.1 -4.11 ...
## $ 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 ...
## [list output truncated]summary(Solubility_Train)## solTrainY FP001 FP002 FP003
## Min. :-11.620 Min. :0.0000 Min. :0.0000 Min. :0.0000
## 1st Qu.: -3.955 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : -2.510 Median :0.0000 Median :1.0000 Median :0.0000
## Mean : -2.719 Mean :0.4932 Mean :0.5394 Mean :0.4364
## 3rd Qu.: -1.360 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. : 1.580 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP004 FP005 FP006 FP007
## 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 :0.5846 Mean :0.5794 Mean :0.4006 Mean :0.3638
## 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
## FP008 FP009 FP010 FP011
## 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 :0.326 Mean :0.2797 Mean :0.1788 Mean :0.2145
## 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP012 FP013 FP014 FP015
## 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.:1.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.1767 Mean :0.1661 Mean :0.1609 Mean :0.8601
## 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
## FP016 FP017 FP018 FP019
## 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 :0.1462 Mean :0.1441 Mean :0.1314 Mean :0.122
## 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
## FP020 FP021 FP022 FP023
## 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 :0.1199 Mean :0.1209 Mean :0.1041 Mean :0.123
## 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
## FP024 FP025 FP026 FP027
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.1125 Mean :0.1157 Mean :0.08412 Mean :0.09779
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP028 FP029 FP030 FP031
## Min. :0.0000 Min. :0.000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.000 Median :0.00000 Median :0.00000
## Mean :0.1062 Mean :0.102 Mean :0.09359 Mean :0.08938
## 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.000 Max. :1.00000 Max. :1.00000
## FP032 FP033 FP034 FP035
## 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 :0.07361 Mean :0.0694 Mean :0.07992 Mean :0.07256
## 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
## FP036 FP037 FP038 FP039
## 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 :0.07571 Mean :0.07045 Mean :0.08622 Mean :0.07466
## 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
## FP040 FP041 FP042 FP043
## 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 :0.06835 Mean :0.06309 Mean :0.05678 Mean :0.06625
## 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
## FP044 FP045 FP046 FP047
## Min. :0.00000 Min. :0.00000 Min. :0.0000 Min. :0.000
## 1st Qu.:0.00000 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.000
## Median :0.00000 Median :0.00000 Median :0.0000 Median :0.000
## Mean :0.05994 Mean :0.05573 Mean :0.3155 Mean :0.266
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.000
## FP048 FP049 FP050 FP051
## 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 :0.1241 Mean :0.122 Mean :0.1125 Mean :0.1094
## 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
## FP052 FP053 FP054 FP055
## 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 :0.09148 Mean :0.09359 Mean :0.07571 Mean :0.05363
## 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
## FP056 FP057 FP058 FP059
## 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 :0.06519 Mean :0.1199 Mean :0.1136 Mean :0.05468
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.00000
## FP060 FP061 FP062 FP063
## 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 :0.4816 Mean :0.4469 Mean :0.4374 Mean :0.4259
## 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
## FP064 FP065 FP066 FP067
## 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 :1.0000 Median :0.0000
## Mean :0.4164 Mean :0.5931 Mean :0.6099 Mean :0.3796
## 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
## FP068 FP069 FP070 FP071
## 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 :0.3617 Mean :0.3617 Mean :0.3554 Mean :0.327
## 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
## FP072 FP073 FP074 FP075
## 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 :0.6583 Mean :0.3102 Mean :0.3249 Mean :0.3386
## 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
## FP076 FP077 FP078 FP079
## 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 :0.3281 Mean :0.3207 Mean :0.3039 Mean :0.6898
## 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
## FP080 FP081 FP082 FP083
## 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 :1.000 Median :0.0000
## Mean :0.3028 Mean :0.2787 Mean :0.714 Mean :0.2734
## 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
## FP084 FP085 FP086 FP087
## 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 :1.0000
## Mean :0.286 Mean :0.2555 Mean :0.2692 Mean :0.7266
## 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
## FP088 FP089 FP090 FP091
## 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 :0.2629 Mean :0.2471 Mean :0.2492 Mean :0.225
## 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP092 FP093 FP094 FP095
## 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 :0.000 Median :0.000 Median :0.0000 Median :0.0000
## Mean :0.244 Mean :0.244 Mean :0.2313 Mean :0.2198
## 3rd Qu.:0.000 3rd Qu.:0.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP096 FP097 FP098 FP099
## 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 :0.2177 Mean :0.2355 Mean :0.2376 Mean :0.2271
## 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
## FP100 FP101 FP102 FP103
## 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 :0.2313 Mean :0.2366 Mean :0.2019 Mean :0.2187
## 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
## FP104 FP105 FP106 FP107
## 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 :0.2229 Mean :0.2156 Mean :0.1914 Mean :0.2114
## 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
## FP108 FP109 FP110 FP111
## 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 :0.205 Mean :0.1767 Mean :0.2061 Mean :0.1966
## 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
## FP112 FP113 FP114 FP115
## 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 :0.1945 Mean :0.1956 Mean :0.1556 Mean :0.1788
## 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
## FP116 FP117 FP118 FP119
## 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 :0.1924 Mean :0.1788 Mean :0.1924 Mean :0.163
## 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
## FP120 FP121 FP122 FP123
## 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 :0.1661 Mean :0.1399 Mean :0.164 Mean :0.1672
## 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
## FP124 FP125 FP126 FP127
## 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 :0.1619 Mean :0.1556 Mean :0.1483 Mean :0.1399
## 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
## FP128 FP129 FP130 FP131
## 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 :0.1483 Mean :0.1388 Mean :0.1052 Mean :0.1262
## 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
## FP132 FP133 FP134 FP135
## 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 :0.1251 Mean :0.1262 Mean :0.1272 Mean :0.1262
## 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
## FP136 FP137 FP138 FP139
## 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 :0.1209 Mean :0.1157 Mean :0.1115 Mean :0.08202
## 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
## FP140 FP141 FP142 FP143
## 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 :0.1115 Mean :0.1167 Mean :0.1094 Mean :0.08097
## 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
## FP144 FP145 FP146 FP147
## 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 :0.1041 Mean :0.1041 Mean :0.103 Mean :0.1052
## 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
## FP148 FP149 FP150 FP151
## 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 :0.08728 Mean :0.09043 Mean :0.07886 Mean :0.05573
## 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
## FP152 FP153 FP154 FP155
## 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 :0.08202 Mean :0.07781 Mean :0.03785 Mean :0.0694
## 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
## FP156 FP157 FP158 FP159
## 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 :0.06204 Mean :0.05363 Mean :0.07045
## 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
## FP160 FP161 FP162 FP163
## 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 :0.06835 Mean :0.06625 Mean :0.4953 Mean :0.4763
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP164 FP165 FP166 FP167
## 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 :0.6278 Mean :0.3491 Mean :0.3312 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
## FP168 FP169 FP170 FP171
## 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 :1.0000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :0.6656 Mean :0.1861 Mean :0.184 Mean :0.1693
## 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
## FP172 FP173 FP174 FP175
## 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 :0.1514 Mean :0.142 Mean :0.1304 Mean :0.1346
## 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
## FP176 FP177 FP178 FP179
## 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 :0.1209 Mean :0.1209 Mean :0.09779
## 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
## FP180 FP181 FP182 FP183
## 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 :0.1073 Mean :0.09359 Mean :0.09884 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
## FP184 FP185 FP186 FP187
## 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 :0.08412 Mean :0.08517 Mean :0.07676 Mean :0.07256
## 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
## FP188 FP189 FP190 FP191
## 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 :0.06835 Mean :0.07676 Mean :0.07256 Mean :0.07045
## 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
## FP192 FP193 FP194 FP195
## 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 :0.06099 Mean :0.06204 Mean :0.05889 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
## FP196 FP197 FP198 FP199
## 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 :0.05678 Mean :0.05258 Mean :0.05678 Mean :0.04732
## 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
## FP200 FP201 FP202 FP203
## 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 :0.04942 Mean :0.05258 Mean :0.2576 Mean :0.1146
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP204 FP205 FP206 FP207
## 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 :0.09884 Mean :0.07781 Mean :0.05994 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
## FP208 MolWeight NumAtoms NumNonHAtoms
## Min. :0.0000 Min. : 46.09 Min. : 5.00 Min. : 2.00
## 1st Qu.:0.0000 1st Qu.:122.61 1st Qu.:17.00 1st Qu.: 8.00
## Median :0.0000 Median :179.23 Median :22.00 Median :12.00
## Mean :0.1125 Mean :201.65 Mean :25.51 Mean :13.16
## 3rd Qu.:0.0000 3rd Qu.:264.34 3rd Qu.:31.00 3rd Qu.:17.00
## Max. :1.0000 Max. :665.81 Max. :94.00 Max. :47.00
## NumBonds NumNonHBonds NumMultBonds NumRotBonds
## Min. : 4.00 Min. : 1.00 Min. : 0.000 Min. : 0.000
## 1st Qu.:17.00 1st Qu.: 8.00 1st Qu.: 1.000 1st Qu.: 0.000
## Median :23.00 Median :12.00 Median : 6.000 Median : 2.000
## Mean :25.91 Mean :13.56 Mean : 6.148 Mean : 2.251
## 3rd Qu.:31.50 3rd Qu.:18.00 3rd Qu.:10.000 3rd Qu.: 3.500
## Max. :97.00 Max. :50.00 Max. :25.000 Max. :16.000
## NumDblBonds NumAromaticBonds NumHydrogen NumCarbon
## Min. :0.000 Min. : 0.000 Min. : 0.00 Min. : 1.000
## 1st Qu.:0.000 1st Qu.: 0.000 1st Qu.: 7.00 1st Qu.: 6.000
## Median :1.000 Median : 6.000 Median :11.00 Median : 9.000
## Mean :1.006 Mean : 5.121 Mean :12.35 Mean : 9.893
## 3rd Qu.:2.000 3rd Qu.: 6.000 3rd Qu.:16.00 3rd Qu.:12.000
## Max. :7.000 Max. :25.000 Max. :47.00 Max. :33.000
## NumNitrogen NumOxygen NumSulfer NumChlorine
## Min. :0.0000 Min. : 0.000 Min. :0.000 Min. : 0.0000
## 1st Qu.:0.0000 1st Qu.: 0.000 1st Qu.:0.000 1st Qu.: 0.0000
## Median :0.0000 Median : 1.000 Median :0.000 Median : 0.0000
## Mean :0.8128 Mean : 1.574 Mean :0.164 Mean : 0.5563
## 3rd Qu.:1.0000 3rd Qu.: 2.000 3rd Qu.:0.000 3rd Qu.: 0.0000
## Max. :6.0000 Max. :13.000 Max. :4.000 Max. :10.0000
## NumHalogen NumRings HydrophilicFactor SurfaceArea1
## Min. : 0.0000 Min. :0.000 Min. :-0.98500 Min. : 0.00
## 1st Qu.: 0.0000 1st Qu.:0.000 1st Qu.:-0.76300 1st Qu.: 9.23
## Median : 0.0000 Median :1.000 Median :-0.31400 Median : 29.10
## Mean : 0.6982 Mean :1.402 Mean :-0.02059 Mean : 36.46
## 3rd Qu.: 1.0000 3rd Qu.:2.000 3rd Qu.: 0.31300 3rd Qu.: 53.28
## Max. :10.0000 Max. :7.000 Max. :13.48300 Max. :331.94
## SurfaceArea2
## Min. : 0.00
## 1st Qu.: 10.63
## Median : 33.12
## Mean : 40.23
## 3rd Qu.: 60.66
## Max. :331.94##################################
# Performing a general exploration of the test set
##################################
dim(Solubility_Test)## [1] 316 229str(Solubility_Test)## 'data.frame': 316 obs. of 229 variables:
## $ solTestY : num 0.93 0.85 0.81 0.74 0.61 0.58 0.57 0.56 0.52 0.45 ...
## $ 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 ...
## [list output truncated]summary(Solubility_Test)## solTestY FP001 FP002 FP003
## Min. :-10.410 Min. :0.0000 Min. :0.0000 Min. :0.000
## 1st Qu.: -3.953 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.000
## Median : -2.480 Median :0.0000 Median :1.0000 Median :0.000
## Mean : -2.797 Mean :0.4684 Mean :0.5854 Mean :0.443
## 3rd Qu.: -1.373 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:1.000
## Max. : 1.070 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP004 FP005 FP006 FP007
## 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 :0.5316 Mean :0.6171 Mean :0.3513 Mean :0.3544
## 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
## FP008 FP009 FP010 FP011
## 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 :0.3608 Mean :0.2627 Mean :0.193 Mean :0.1741
## 3rd Qu.:1.0000 3rd Qu.:1.0000 3rd Qu.:0.000 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.000 Max. :1.0000
## FP012 FP013 FP014 FP015
## 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.:1.0000
## Median :0.0000 Median :0.0000 Median :0.0000 Median :1.0000
## Mean :0.1677 Mean :0.1646 Mean :0.1582 Mean :0.8291
## 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
## FP016 FP017 FP018 FP019
## 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 :0.1424 Mean :0.1487 Mean :0.08544 Mean :0.1139
## 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
## FP020 FP021 FP022 FP023
## 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 :0.1076 Mean :0.1076 Mean :0.1171 Mean :0.08544
## 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
## FP024 FP025 FP026 FP027
## 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 :0.0981 Mean :0.07911 Mean :0.1171 Mean :0.07911
## 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
## FP028 FP029 FP030 FP031
## 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 :0.05696 Mean :0.05063 Mean :0.08228 Mean :0.0981
## 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
## FP032 FP033 FP034 FP035
## 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 :0.1297 Mean :0.1203 Mean :0.06646 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
## FP036 FP037 FP038 FP039
## 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 :0.06013 Mean :0.09494 Mean :0.03165 Mean :0.06329
## 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
## FP040 FP041 FP042 FP043
## 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 :0.05696 Mean :0.06013 Mean :0.06013 Mean :0.0443
## 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
## FP044 FP045 FP046 FP047
## 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 :0.06013 Mean :0.06329 Mean :0.3259 Mean :0.2975
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP048 FP049 FP050 FP051
## 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 :0.1139 Mean :0.1076 Mean :0.1139 Mean :0.05696
## 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
## FP052 FP053 FP054 FP055
## 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 :0.06013 Mean :0.0981 Mean :0.09177
## 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
## FP056 FP057 FP058 FP059
## 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 :0.1234 Mean :0.1361 Mean :0.0443
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.0000 Max. :1.0000 Max. :1.0000
## FP060 FP061 FP062 FP063
## 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 :0.4525 Mean :0.3924 Mean :0.4272 Mean :0.3576
## 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
## FP064 FP065 FP066 FP067
## 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 :1.0000 Median :0.0000
## Mean :0.3892 Mean :0.5981 Mean :0.6171 Mean :0.3259
## 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
## FP068 FP069 FP070 FP071
## 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 :0.3734 Mean :0.3323 Mean :0.3449
## 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
## FP072 FP073 FP074 FP075
## 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 :0.6456 Mean :0.2911 Mean :0.3259 Mean :0.2563
## 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
## FP076 FP077 FP078 FP079
## 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 :1.0000
## Mean :0.3165 Mean :0.307 Mean :0.3101 Mean :0.7278
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP080 FP081 FP082 FP083
## 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 :1.0000 Median :0.0000
## Mean :0.2627 Mean :0.288 Mean :0.7437 Mean :0.2532
## 3rd Qu.:1.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP084 FP085 FP086 FP087
## 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.:1.0000
## Median :0.0000 Median :0.000 Median :0.0000 Median :1.0000
## Mean :0.2247 Mean :0.269 Mean :0.2722 Mean :0.7627
## 3rd Qu.:0.0000 3rd Qu.:1.000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.0000 Max. :1.000 Max. :1.0000 Max. :1.0000
## FP088 FP089 FP090 FP091
## 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 :0.2437 Mean :0.2532 Mean :0.2278 Mean :0.231
## 3rd Qu.:0.0000 3rd Qu.:1.0000 3rd Qu.:0.0000 3rd Qu.:0.000
## Max. :1.0000 Max. :1.0000 Max. :1.0000 Max. :1.000
## FP092 FP093 FP094 FP095
## Min. :0.0000 Min. :0.0000 Min. :0.00 Min. :0.0000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00 1st Qu.:0.0000
## Median :0.0000 Median :0.0000 Median :0.00 Median :0.0000
## Mean :0.2184 Mean :0.2152 Mean :0.25 Mean :0.2057
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.25 3rd Qu.:0.0000
## Max. :1.0000 Max. :1.0000 Max. :1.00 Max. :1.0000
## FP096 FP097 FP098 FP099
## 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 :0.1867 Mean :0.2089 Mean :0.2025 Mean :0.212
## 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
## FP100 FP101 FP102 FP103
## 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 :0.1804 Mean :0.1772 Mean :0.1456 Mean :0.2184
## 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
## FP104 FP105 FP106 FP107
## 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 :0.1835 Mean :0.2152 Mean :0.1361 Mean :0.1962
## 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
## FP108 FP109 FP110 FP111
## 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 :0.1804 Mean :0.1741 Mean :0.1646 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
## FP112 FP113 FP114 FP115
## 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 :0.1646 Mean :0.1772 Mean :0.1582
## 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
## FP116 FP117 FP118 FP119
## 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 :0.1487 Mean :0.1709 Mean :0.1171 Mean :0.1677
## 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
## FP120 FP121 FP122 FP123
## 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 :0.1551 Mean :0.1076 Mean :0.1361 Mean :0.1456
## 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
## FP124 FP125 FP126 FP127
## 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 :0.1329 Mean :0.1203 Mean :0.1139 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
## FP128 FP129 FP130 FP131
## 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 :0.1392 Mean :0.08228 Mean :0.1076
## 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
## FP132 FP133 FP134 FP135
## Min. :0.0000 Min. :0.0000 Min. :0.00000 Min. :0.00000
## 1st Qu.:0.0000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.00000
## Median :0.0000 Median :0.0000 Median :0.00000 Median :0.00000
## Mean :0.1266 Mean :0.1361 Mean :0.08544 Mean :0.06329
## 3rd Qu.:0.0000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.00000
## Max. :1.0000 Max. :1.0000 Max. :1.00000 Max. :1.00000
## FP136 FP137 FP138 FP139
## 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 :0.1013 Mean :0.08861 Mean :0.08228 Mean :0.06329
## 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
## FP140 FP141 FP142 FP143
## 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 :0.08861 Mean :0.06962 Mean :0.09494 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
## FP144 FP145 FP146 FP147
## 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 :0.09177 Mean :0.06329 Mean :0.09177 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
## FP148 FP149 FP150 FP151
## 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 :0.07911 Mean :0.08228 Mean :0.06646 Mean :0.03165
## 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
## FP152 FP153 FP154 FP155
## 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 :0.0538 Mean :0.03481 Mean :0.03165 Mean :0.06646
## 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
## FP156 FP157 FP158 FP159
## 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 :0.04747 Mean :0.05696 Mean :0.07911 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
## FP160 FP161 FP162 FP163
## 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 :1.0000 Median :0.0000
## Mean :0.03481 Mean :0.03481 Mean :0.5316 Mean :0.4525
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:1.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP164 FP165 FP166 FP167
## 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 :0.6551 Mean :0.3196 Mean :0.3386 Mean :0.3006
## 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
## FP168 FP169 FP170 FP171
## 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 :0.7152 Mean :0.1867 Mean :0.1551 Mean :0.1297
## 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
## FP172 FP173 FP174 FP175
## 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 :0.1487 Mean :0.1361 Mean :0.1551 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
## FP176 FP177 FP178 FP179
## 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 :0.1013 Mean :0.1076 Mean :0.1392
## 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
## FP180 FP181 FP182 FP183
## Min. :0.00000 Min. :0.0000 Min. :0.00000 Min. :0.0000
## 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.:0.00000 1st Qu.:0.0000
## Median :0.00000 Median :0.0000 Median :0.00000 Median :0.0000
## Mean :0.06962 Mean :0.1044 Mean :0.07595 Mean :0.1329
## 3rd Qu.:0.00000 3rd Qu.:0.0000 3rd Qu.:0.00000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.0000 Max. :1.00000 Max. :1.0000
## FP184 FP185 FP186 FP187
## 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 :0.09494 Mean :0.0981 Mean :0.06013 Mean :0.06646
## 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
## FP188 FP189 FP190 FP191
## 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 :0.04114 Mean :0.0538 Mean :0.05696
## 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
## FP192 FP193 FP194 FP195
## 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 :0.06962 Mean :0.06646 Mean :0.05063
## 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
## FP196 FP197 FP198 FP199
## 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 :0.06329 Mean :0.0443 Mean :0.07278
## 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
## FP200 FP201 FP202 FP203
## 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 :0.06329 Mean :0.04114 Mean :0.2658 Mean :0.1361
## 3rd Qu.:0.00000 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:0.0000
## Max. :1.00000 Max. :1.00000 Max. :1.0000 Max. :1.0000
## FP204 FP205 FP206 FP207
## 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 :0.09494 Mean :0.07911 Mean :0.05063 Mean :0.0443
## 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
## FP208 MolWeight NumAtoms NumNonHAtoms NumBonds
## Min. :0.0000 Min. : 56.07 Min. : 5.0 Min. : 3.00 Min. : 4
## 1st Qu.:0.0000 1st Qu.:121.91 1st Qu.:17.0 1st Qu.: 8.00 1st Qu.:16
## Median :0.0000 Median :170.11 Median :22.0 Median :11.00 Median :23
## Mean :0.1361 Mean :194.12 Mean :24.6 Mean :12.71 Mean :25
## 3rd Qu.:0.0000 3rd Qu.:253.82 3rd Qu.:29.0 3rd Qu.:16.00 3rd Qu.:30
## Max. :1.0000 Max. :478.92 Max. :68.0 Max. :33.00 Max. :71
## NumNonHBonds NumMultBonds NumRotBonds NumDblBonds
## Min. : 2.0 Min. : 0.000 Min. : 0.000 Min. :0.0000
## 1st Qu.: 8.0 1st Qu.: 1.000 1st Qu.: 0.000 1st Qu.:0.0000
## Median :12.0 Median : 6.000 Median : 1.000 Median :1.0000
## Mean :13.1 Mean : 6.313 Mean : 1.949 Mean :0.8892
## 3rd Qu.:17.0 3rd Qu.:10.000 3rd Qu.: 3.000 3rd Qu.:1.0000
## Max. :36.0 Max. :27.000 Max. :16.000 Max. :6.0000
## NumAromaticBonds NumHydrogen NumCarbon NumNitrogen
## Min. : 0.000 Min. : 0.0 Min. : 1.000 Min. :0.0000
## 1st Qu.: 0.000 1st Qu.: 7.0 1st Qu.: 6.000 1st Qu.:0.0000
## Median : 6.000 Median :11.0 Median : 8.000 Median :0.0000
## Mean : 5.399 Mean :11.9 Mean : 9.785 Mean :0.7089
## 3rd Qu.:10.000 3rd Qu.:15.0 3rd Qu.:12.000 3rd Qu.:1.0000
## Max. :27.000 Max. :40.0 Max. :24.000 Max. :6.0000
## NumOxygen NumSulfer NumChlorine NumHalogen
## Min. :0.000 Min. :0.0000 Min. :0.000 Min. :0.0000
## 1st Qu.:0.000 1st Qu.:0.0000 1st Qu.:0.000 1st Qu.:0.0000
## Median :1.000 Median :0.0000 Median :0.000 Median :0.0000
## Mean :1.389 Mean :0.1013 Mean :0.557 Mean :0.7089
## 3rd Qu.:2.000 3rd Qu.:0.0000 3rd Qu.:0.000 3rd Qu.:1.0000
## Max. :9.000 Max. :3.0000 Max. :9.000 Max. :9.0000
## NumRings HydrophilicFactor SurfaceArea1 SurfaceArea2
## Min. :0.000 Min. :-0.9860 Min. : 0.00 Min. : 0.00
## 1st Qu.:1.000 1st Qu.:-0.7670 1st Qu.: 9.23 1st Qu.: 9.23
## Median :1.000 Median :-0.3970 Median : 26.30 Median : 26.30
## Mean :1.399 Mean :-0.1022 Mean : 32.76 Mean : 35.04
## 3rd Qu.:2.000 3rd Qu.: 0.2140 3rd Qu.: 49.55 3rd Qu.: 52.32
## Max. :6.000 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 solTrainY numeric
## 2 2 FP001 integer
## 3 3 FP002 integer
## 4 4 FP003 integer
## 5 5 FP004 integer
## 6 6 FP005 integer
## 7 7 FP006 integer
## 8 8 FP007 integer
## 9 9 FP008 integer
## 10 10 FP009 integer
## 11 11 FP010 integer
## 12 12 FP011 integer
## 13 13 FP012 integer
## 14 14 FP013 integer
## 15 15 FP014 integer
## 16 16 FP015 integer
## 17 17 FP016 integer
## 18 18 FP017 integer
## 19 19 FP018 integer
## 20 20 FP019 integer
## 21 21 FP020 integer
## 22 22 FP021 integer
## 23 23 FP022 integer
## 24 24 FP023 integer
## 25 25 FP024 integer
## 26 26 FP025 integer
## 27 27 FP026 integer
## 28 28 FP027 integer
## 29 29 FP028 integer
## 30 30 FP029 integer
## 31 31 FP030 integer
## 32 32 FP031 integer
## 33 33 FP032 integer
## 34 34 FP033 integer
## 35 35 FP034 integer
## 36 36 FP035 integer
## 37 37 FP036 integer
## 38 38 FP037 integer
## 39 39 FP038 integer
## 40 40 FP039 integer
## 41 41 FP040 integer
## 42 42 FP041 integer
## 43 43 FP042 integer
## 44 44 FP043 integer
## 45 45 FP044 integer
## 46 46 FP045 integer
## 47 47 FP046 integer
## 48 48 FP047 integer
## 49 49 FP048 integer
## 50 50 FP049 integer
## 51 51 FP050 integer
## 52 52 FP051 integer
## 53 53 FP052 integer
## 54 54 FP053 integer
## 55 55 FP054 integer
## 56 56 FP055 integer
## 57 57 FP056 integer
## 58 58 FP057 integer
## 59 59 FP058 integer
## 60 60 FP059 integer
## 61 61 FP060 integer
## 62 62 FP061 integer
## 63 63 FP062 integer
## 64 64 FP063 integer
## 65 65 FP064 integer
## 66 66 FP065 integer
## 67 67 FP066 integer
## 68 68 FP067 integer
## 69 69 FP068 integer
## 70 70 FP069 integer
## 71 71 FP070 integer
## 72 72 FP071 integer
## 73 73 FP072 integer
## 74 74 FP073 integer
## 75 75 FP074 integer
## 76 76 FP075 integer
## 77 77 FP076 integer
## 78 78 FP077 integer
## 79 79 FP078 integer
## 80 80 FP079 integer
## 81 81 FP080 integer
## 82 82 FP081 integer
## 83 83 FP082 integer
## 84 84 FP083 integer
## 85 85 FP084 integer
## 86 86 FP085 integer
## 87 87 FP086 integer
## 88 88 FP087 integer
## 89 89 FP088 integer
## 90 90 FP089 integer
## 91 91 FP090 integer
## 92 92 FP091 integer
## 93 93 FP092 integer
## 94 94 FP093 integer
## 95 95 FP094 integer
## 96 96 FP095 integer
## 97 97 FP096 integer
## 98 98 FP097 integer
## 99 99 FP098 integer
## 100 100 FP099 integer
## 101 101 FP100 integer
## 102 102 FP101 integer
## 103 103 FP102 integer
## 104 104 FP103 integer
## 105 105 FP104 integer
## 106 106 FP105 integer
## 107 107 FP106 integer
## 108 108 FP107 integer
## 109 109 FP108 integer
## 110 110 FP109 integer
## 111 111 FP110 integer
## 112 112 FP111 integer
## 113 113 FP112 integer
## 114 114 FP113 integer
## 115 115 FP114 integer
## 116 116 FP115 integer
## 117 117 FP116 integer
## 118 118 FP117 integer
## 119 119 FP118 integer
## 120 120 FP119 integer
## 121 121 FP120 integer
## 122 122 FP121 integer
## 123 123 FP122 integer
## 124 124 FP123 integer
## 125 125 FP124 integer
## 126 126 FP125 integer
## 127 127 FP126 integer
## 128 128 FP127 integer
## 129 129 FP128 integer
## 130 130 FP129 integer
## 131 131 FP130 integer
## 132 132 FP131 integer
## 133 133 FP132 integer
## 134 134 FP133 integer
## 135 135 FP134 integer
## 136 136 FP135 integer
## 137 137 FP136 integer
## 138 138 FP137 integer
## 139 139 FP138 integer
## 140 140 FP139 integer
## 141 141 FP140 integer
## 142 142 FP141 integer
## 143 143 FP142 integer
## 144 144 FP143 integer
## 145 145 FP144 integer
## 146 146 FP145 integer
## 147 147 FP146 integer
## 148 148 FP147 integer
## 149 149 FP148 integer
## 150 150 FP149 integer
## 151 151 FP150 integer
## 152 152 FP151 integer
## 153 153 FP152 integer
## 154 154 FP153 integer
## 155 155 FP154 integer
## 156 156 FP155 integer
## 157 157 FP156 integer
## 158 158 FP157 integer
## 159 159 FP158 integer
## 160 160 FP159 integer
## 161 161 FP160 integer
## 162 162 FP161 integer
## 163 163 FP162 integer
## 164 164 FP163 integer
## 165 165 FP164 integer
## 166 166 FP165 integer
## 167 167 FP166 integer
## 168 168 FP167 integer
## 169 169 FP168 integer
## 170 170 FP169 integer
## 171 171 FP170 integer
## 172 172 FP171 integer
## 173 173 FP172 integer
## 174 174 FP173 integer
## 175 175 FP174 integer
## 176 176 FP175 integer
## 177 177 FP176 integer
## 178 178 FP177 integer
## 179 179 FP178 integer
## 180 180 FP179 integer
## 181 181 FP180 integer
## 182 182 FP181 integer
## 183 183 FP182 integer
## 184 184 FP183 integer
## 185 185 FP184 integer
## 186 186 FP185 integer
## 187 187 FP186 integer
## 188 188 FP187 integer
## 189 189 FP188 integer
## 190 190 FP189 integer
## 191 191 FP190 integer
## 192 192 FP191 integer
## 193 193 FP192 integer
## 194 194 FP193 integer
## 195 195 FP194 integer
## 196 196 FP195 integer
## 197 197 FP196 integer
## 198 198 FP197 integer
## 199 199 FP198 integer
## 200 200 FP199 integer
## 201 201 FP200 integer
## 202 202 FP201 integer
## 203 203 FP202 integer
## 204 204 FP203 integer
## 205 205 FP204 integer
## 206 206 FP205 integer
## 207 207 FP206 integer
## 208 208 FP207 integer
## 209 209 FP208 integer
## 210 210 MolWeight numeric
## 211 211 NumAtoms integer
## 212 212 NumNonHAtoms integer
## 213 213 NumBonds integer
## 214 214 NumNonHBonds integer
## 215 215 NumMultBonds integer
## 216 216 NumRotBonds integer
## 217 217 NumDblBonds integer
## 218 218 NumAromaticBonds integer
## 219 219 NumHydrogen integer
## 220 220 NumCarbon integer
## 221 221 NumNitrogen integer
## 222 222 NumOxygen integer
## 223 223 NumSulfer integer
## 224 224 NumChlorine integer
## 225 225 NumHalogen integer
## 226 226 NumRings integer
## 227 227 HydrophilicFactor numeric
## 228 228 SurfaceArea1 numeric
## 229 229 SurfaceArea2 numeric1.2 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 solTrainY numeric 951 0 1.000
## 2 2 FP001 integer 951 0 1.000
## 3 3 FP002 integer 951 0 1.000
## 4 4 FP003 integer 951 0 1.000
## 5 5 FP004 integer 951 0 1.000
## 6 6 FP005 integer 951 0 1.000
## 7 7 FP006 integer 951 0 1.000
## 8 8 FP007 integer 951 0 1.000
## 9 9 FP008 integer 951 0 1.000
## 10 10 FP009 integer 951 0 1.000
## 11 11 FP010 integer 951 0 1.000
## 12 12 FP011 integer 951 0 1.000
## 13 13 FP012 integer 951 0 1.000
## 14 14 FP013 integer 951 0 1.000
## 15 15 FP014 integer 951 0 1.000
## 16 16 FP015 integer 951 0 1.000
## 17 17 FP016 integer 951 0 1.000
## 18 18 FP017 integer 951 0 1.000
## 19 19 FP018 integer 951 0 1.000
## 20 20 FP019 integer 951 0 1.000
## 21 21 FP020 integer 951 0 1.000
## 22 22 FP021 integer 951 0 1.000
## 23 23 FP022 integer 951 0 1.000
## 24 24 FP023 integer 951 0 1.000
## 25 25 FP024 integer 951 0 1.000
## 26 26 FP025 integer 951 0 1.000
## 27 27 FP026 integer 951 0 1.000
## 28 28 FP027 integer 951 0 1.000
## 29 29 FP028 integer 951 0 1.000
## 30 30 FP029 integer 951 0 1.000
## 31 31 FP030 integer 951 0 1.000
## 32 32 FP031 integer 951 0 1.000
## 33 33 FP032 integer 951 0 1.000
## 34 34 FP033 integer 951 0 1.000
## 35 35 FP034 integer 951 0 1.000
## 36 36 FP035 integer 951 0 1.000
## 37 37 FP036 integer 951 0 1.000
## 38 38 FP037 integer 951 0 1.000
## 39 39 FP038 integer 951 0 1.000
## 40 40 FP039 integer 951 0 1.000
## 41 41 FP040 integer 951 0 1.000
## 42 42 FP041 integer 951 0 1.000
## 43 43 FP042 integer 951 0 1.000
## 44 44 FP043 integer 951 0 1.000
## 45 45 FP044 integer 951 0 1.000
## 46 46 FP045 integer 951 0 1.000
## 47 47 FP046 integer 951 0 1.000
## 48 48 FP047 integer 951 0 1.000
## 49 49 FP048 integer 951 0 1.000
## 50 50 FP049 integer 951 0 1.000
## 51 51 FP050 integer 951 0 1.000
## 52 52 FP051 integer 951 0 1.000
## 53 53 FP052 integer 951 0 1.000
## 54 54 FP053 integer 951 0 1.000
## 55 55 FP054 integer 951 0 1.000
## 56 56 FP055 integer 951 0 1.000
## 57 57 FP056 integer 951 0 1.000
## 58 58 FP057 integer 951 0 1.000
## 59 59 FP058 integer 951 0 1.000
## 60 60 FP059 integer 951 0 1.000
## 61 61 FP060 integer 951 0 1.000
## 62 62 FP061 integer 951 0 1.000
## 63 63 FP062 integer 951 0 1.000
## 64 64 FP063 integer 951 0 1.000
## 65 65 FP064 integer 951 0 1.000
## 66 66 FP065 integer 951 0 1.000
## 67 67 FP066 integer 951 0 1.000
## 68 68 FP067 integer 951 0 1.000
## 69 69 FP068 integer 951 0 1.000
## 70 70 FP069 integer 951 0 1.000
## 71 71 FP070 integer 951 0 1.000
## 72 72 FP071 integer 951 0 1.000
## 73 73 FP072 integer 951 0 1.000
## 74 74 FP073 integer 951 0 1.000
## 75 75 FP074 integer 951 0 1.000
## 76 76 FP075 integer 951 0 1.000
## 77 77 FP076 integer 951 0 1.000
## 78 78 FP077 integer 951 0 1.000
## 79 79 FP078 integer 951 0 1.000
## 80 80 FP079 integer 951 0 1.000
## 81 81 FP080 integer 951 0 1.000
## 82 82 FP081 integer 951 0 1.000
## 83 83 FP082 integer 951 0 1.000
## 84 84 FP083 integer 951 0 1.000
## 85 85 FP084 integer 951 0 1.000
## 86 86 FP085 integer 951 0 1.000
## 87 87 FP086 integer 951 0 1.000
## 88 88 FP087 integer 951 0 1.000
## 89 89 FP088 integer 951 0 1.000
## 90 90 FP089 integer 951 0 1.000
## 91 91 FP090 integer 951 0 1.000
## 92 92 FP091 integer 951 0 1.000
## 93 93 FP092 integer 951 0 1.000
## 94 94 FP093 integer 951 0 1.000
## 95 95 FP094 integer 951 0 1.000
## 96 96 FP095 integer 951 0 1.000
## 97 97 FP096 integer 951 0 1.000
## 98 98 FP097 integer 951 0 1.000
## 99 99 FP098 integer 951 0 1.000
## 100 100 FP099 integer 951 0 1.000
## 101 101 FP100 integer 951 0 1.000
## 102 102 FP101 integer 951 0 1.000
## 103 103 FP102 integer 951 0 1.000
## 104 104 FP103 integer 951 0 1.000
## 105 105 FP104 integer 951 0 1.000
## 106 106 FP105 integer 951 0 1.000
## 107 107 FP106 integer 951 0 1.000
## 108 108 FP107 integer 951 0 1.000
## 109 109 FP108 integer 951 0 1.000
## 110 110 FP109 integer 951 0 1.000
## 111 111 FP110 integer 951 0 1.000
## 112 112 FP111 integer 951 0 1.000
## 113 113 FP112 integer 951 0 1.000
## 114 114 FP113 integer 951 0 1.000
## 115 115 FP114 integer 951 0 1.000
## 116 116 FP115 integer 951 0 1.000
## 117 117 FP116 integer 951 0 1.000
## 118 118 FP117 integer 951 0 1.000
## 119 119 FP118 integer 951 0 1.000
## 120 120 FP119 integer 951 0 1.000
## 121 121 FP120 integer 951 0 1.000
## 122 122 FP121 integer 951 0 1.000
## 123 123 FP122 integer 951 0 1.000
## 124 124 FP123 integer 951 0 1.000
## 125 125 FP124 integer 951 0 1.000
## 126 126 FP125 integer 951 0 1.000
## 127 127 FP126 integer 951 0 1.000
## 128 128 FP127 integer 951 0 1.000
## 129 129 FP128 integer 951 0 1.000
## 130 130 FP129 integer 951 0 1.000
## 131 131 FP130 integer 951 0 1.000
## 132 132 FP131 integer 951 0 1.000
## 133 133 FP132 integer 951 0 1.000
## 134 134 FP133 integer 951 0 1.000
## 135 135 FP134 integer 951 0 1.000
## 136 136 FP135 integer 951 0 1.000
## 137 137 FP136 integer 951 0 1.000
## 138 138 FP137 integer 951 0 1.000
## 139 139 FP138 integer 951 0 1.000
## 140 140 FP139 integer 951 0 1.000
## 141 141 FP140 integer 951 0 1.000
## 142 142 FP141 integer 951 0 1.000
## 143 143 FP142 integer 951 0 1.000
## 144 144 FP143 integer 951 0 1.000
## 145 145 FP144 integer 951 0 1.000
## 146 146 FP145 integer 951 0 1.000
## 147 147 FP146 integer 951 0 1.000
## 148 148 FP147 integer 951 0 1.000
## 149 149 FP148 integer 951 0 1.000
## 150 150 FP149 integer 951 0 1.000
## 151 151 FP150 integer 951 0 1.000
## 152 152 FP151 integer 951 0 1.000
## 153 153 FP152 integer 951 0 1.000
## 154 154 FP153 integer 951 0 1.000
## 155 155 FP154 integer 951 0 1.000
## 156 156 FP155 integer 951 0 1.000
## 157 157 FP156 integer 951 0 1.000
## 158 158 FP157 integer 951 0 1.000
## 159 159 FP158 integer 951 0 1.000
## 160 160 FP159 integer 951 0 1.000
## 161 161 FP160 integer 951 0 1.000
## 162 162 FP161 integer 951 0 1.000
## 163 163 FP162 integer 951 0 1.000
## 164 164 FP163 integer 951 0 1.000
## 165 165 FP164 integer 951 0 1.000
## 166 166 FP165 integer 951 0 1.000
## 167 167 FP166 integer 951 0 1.000
## 168 168 FP167 integer 951 0 1.000
## 169 169 FP168 integer 951 0 1.000
## 170 170 FP169 integer 951 0 1.000
## 171 171 FP170 integer 951 0 1.000
## 172 172 FP171 integer 951 0 1.000
## 173 173 FP172 integer 951 0 1.000
## 174 174 FP173 integer 951 0 1.000
## 175 175 FP174 integer 951 0 1.000
## 176 176 FP175 integer 951 0 1.000
## 177 177 FP176 integer 951 0 1.000
## 178 178 FP177 integer 951 0 1.000
## 179 179 FP178 integer 951 0 1.000
## 180 180 FP179 integer 951 0 1.000
## 181 181 FP180 integer 951 0 1.000
## 182 182 FP181 integer 951 0 1.000
## 183 183 FP182 integer 951 0 1.000
## 184 184 FP183 integer 951 0 1.000
## 185 185 FP184 integer 951 0 1.000
## 186 186 FP185 integer 951 0 1.000
## 187 187 FP186 integer 951 0 1.000
## 188 188 FP187 integer 951 0 1.000
## 189 189 FP188 integer 951 0 1.000
## 190 190 FP189 integer 951 0 1.000
## 191 191 FP190 integer 951 0 1.000
## 192 192 FP191 integer 951 0 1.000
## 193 193 FP192 integer 951 0 1.000
## 194 194 FP193 integer 951 0 1.000
## 195 195 FP194 integer 951 0 1.000
## 196 196 FP195 integer 951 0 1.000
## 197 197 FP196 integer 951 0 1.000
## 198 198 FP197 integer 951 0 1.000
## 199 199 FP198 integer 951 0 1.000
## 200 200 FP199 integer 951 0 1.000
## 201 201 FP200 integer 951 0 1.000
## 202 202 FP201 integer 951 0 1.000
## 203 203 FP202 integer 951 0 1.000
## 204 204 FP203 integer 951 0 1.000
## 205 205 FP204 integer 951 0 1.000
## 206 206 FP205 integer 951 0 1.000
## 207 207 FP206 integer 951 0 1.000
## 208 208 FP207 integer 951 0 1.000
## 209 209 FP208 integer 951 0 1.000
## 210 210 MolWeight numeric 951 0 1.000
## 211 211 NumAtoms integer 951 0 1.000
## 212 212 NumNonHAtoms integer 951 0 1.000
## 213 213 NumBonds integer 951 0 1.000
## 214 214 NumNonHBonds integer 951 0 1.000
## 215 215 NumMultBonds integer 951 0 1.000
## 216 216 NumRotBonds integer 951 0 1.000
## 217 217 NumDblBonds integer 951 0 1.000
## 218 218 NumAromaticBonds integer 951 0 1.000
## 219 219 NumHydrogen integer 951 0 1.000
## 220 220 NumCarbon integer 951 0 1.000
## 221 221 NumNitrogen integer 951 0 1.000
## 222 222 NumOxygen integer 951 0 1.000
## 223 223 NumSulfer integer 951 0 1.000
## 224 224 NumChlorine integer 951 0 1.000
## 225 225 NumHalogen integer 951 0 1.000
## 226 226 NumRings integer 951 0 1.000
## 227 227 HydrophilicFactor numeric 951 0 1.000
## 228 228 SurfaceArea1 numeric 951 0 1.000
## 229 229 SurfaceArea2 numeric 951 0 1.000##################################
# Listing all predictors
##################################
DQA.Predictors <- DQA[,!names(DQA) %in% c("solTrainY")]
##################################
# 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("solTrainY")]
##################################
# 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: | |
| numeric | 229 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| solTrainY | 0 | 1 | -2.72 | 2.05 | -11.62 | -3.96 | -2.51 | -1.36 | 1.58 | ▁▁▃▇▃ |
| 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: | |
| numeric | 226 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| solTrainY | 0 | 1 | -2.72 | 2.05 | -11.62 | -3.96 | -2.51 | -1.36 | 1.58 | ▁▁▃▇▃ |
| 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("solTrainY")]
##################################
# 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: | |
| numeric | 226 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| solTrainY | 0 | 1 | -2.72 | 2.05 | -11.62 | -3.96 | -2.51 | -1.36 | 1.58 | ▁▁▃▇▃ |
| FP001 | 0 | 1 | 0.49 | 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 | ▇▁▁▁▅ |
| 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 | ▇▂▁▁▁ |
###################################
# 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("solTrainY")]
##################################
# 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: | |
| numeric | 227 |
| ________________________ | |
| Group variables | None |
Variable type: numeric
| skim_variable | n_missing | complete_rate | mean | sd | p0 | p25 | p50 | p75 | p100 | hist |
|---|---|---|---|---|---|---|---|---|---|---|
| solTrainY | 0 | 1 | -2.72 | 2.05 | -11.62 | -3.96 | -2.51 | -1.36 | 1.58 | ▁▁▃▇▃ |
| 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 | ▇▆▂▁▁ |
| 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 | ▇▂▁▁▁ |
| 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_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("solTrainY")]
##################################
# 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("solTrainY")]
##################################
# 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 = Class variable (numeric)
[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 <- DPA$solTrainY
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,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 | 205 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| Log_Solubility | 0 | 1 | -2.72 | 2.05 | -11.62 | -3.96 | -2.51 | -1.36 | 1.58 | ▁▁▃▇▃ |
| MolWeight | 0 | 1 | 0.00 | 1.00 | -2.84 | -0.80 | -0.01 | 0.80 | 2.72 | ▁▆▇▆▁ |
| NumBonds | 0 | 1 | 0.00 | 1.00 | -2.92 | -0.61 | -0.04 | 0.60 | 3.23 | ▁▅▇▃▁ |
| NumMultBonds | 0 | 1 | 0.00 | 1.00 | -1.19 | -1.00 | -0.03 | 0.74 | 3.65 | ▇▇▃▁▁ |
| NumRotBonds | 0 | 1 | 0.00 | 1.00 | -0.93 | -0.93 | -0.10 | 0.52 | 5.71 | ▇▂▁▁▁ |
| NumDblBonds | 0 | 1 | 0.00 | 1.00 | -0.83 | -0.83 | -0.01 | 0.82 | 4.95 | ▇▂▁▁▁ |
| NumCarbon | 0 | 1 | 0.00 | 1.00 | -2.64 | -0.69 | -0.01 | 0.54 | 3.06 | ▂▇▇▃▁ |
| NumNitrogen | 0 | 1 | 0.00 | 1.00 | -0.69 | -0.69 | -0.69 | 0.16 | 4.37 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 0.00 | 1.00 | -0.91 | -0.91 | -0.33 | 0.25 | 6.61 | ▇▂▁▁▁ |
| NumSulfer | 0 | 1 | 0.00 | 1.00 | -0.34 | -0.34 | -0.34 | -0.34 | 7.86 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.00 | 1.00 | -0.40 | -0.40 | -0.40 | -0.40 | 6.74 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.00 | 1.00 | -0.47 | -0.47 | -0.47 | 0.20 | 6.32 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 0.00 | 1.00 | -1.08 | -1.08 | -0.31 | 0.46 | 4.31 | ▇▃▂▁▁ |
| HydrophilicFactor | 0 | 1 | 0.00 | 1.00 | -0.86 | -0.66 | -0.26 | 0.30 | 11.99 | ▇▁▁▁▁ |
| SurfaceArea1 | 0 | 1 | 0.00 | 1.00 | -1.03 | -0.77 | -0.21 | 0.48 | 8.37 | ▇▂▁▁▁ |
| SurfaceArea2 | 0 | 1 | 0.00 | 1.00 | -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("solTestY")]
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
# train set
##################################
Log_Solubility <- DPA_Test$solTestY
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,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 | 205 |
| numeric | 16 |
| ________________________ | |
| Group variables | None |
Variable type: factor
| skim_variable | n_missing | complete_rate | ordered | n_unique | top_counts |
|---|---|---|---|---|---|
| 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 |
|---|---|---|---|---|---|---|---|---|---|---|
| Log_Solubility | 0 | 1 | -2.8 | 2.08 | -10.41 | -3.95 | -2.48 | -1.37 | 1.07 | ▁▁▅▇▅ |
| MolWeight | 0 | 1 | 0.0 | 1.00 | -2.46 | -0.78 | -0.06 | 0.81 | 2.18 | ▁▇▇▇▃ |
| NumBonds | 0 | 1 | 0.0 | 1.00 | -2.92 | -0.67 | 0.03 | 0.57 | 2.55 | ▁▂▇▃▂ |
| NumMultBonds | 0 | 1 | 0.0 | 1.00 | -1.24 | -1.04 | -0.06 | 0.72 | 4.06 | ▇▇▅▁▁ |
| NumRotBonds | 0 | 1 | 0.0 | 1.00 | -0.82 | -0.82 | -0.40 | 0.44 | 5.94 | ▇▁▁▁▁ |
| NumDblBonds | 0 | 1 | 0.0 | 1.00 | -0.76 | -0.76 | 0.09 | 0.09 | 4.35 | ▇▁▁▁▁ |
| NumCarbon | 0 | 1 | 0.0 | 1.00 | -2.71 | -0.70 | -0.21 | 0.56 | 2.23 | ▁▂▇▅▂ |
| NumNitrogen | 0 | 1 | 0.0 | 1.00 | -0.63 | -0.63 | -0.63 | 0.26 | 4.71 | ▇▂▁▁▁ |
| NumOxygen | 0 | 1 | 0.0 | 1.00 | -0.92 | -0.92 | -0.26 | 0.40 | 5.02 | ▇▃▁▁▁ |
| NumSulfer | 0 | 1 | 0.0 | 1.00 | -0.28 | -0.28 | -0.28 | -0.28 | 8.06 | ▇▁▁▁▁ |
| NumChlorine | 0 | 1 | 0.0 | 1.00 | -0.40 | -0.40 | -0.40 | -0.40 | 6.02 | ▇▁▁▁▁ |
| NumHalogen | 0 | 1 | 0.0 | 1.00 | -0.48 | -0.48 | -0.48 | 0.20 | 5.57 | ▇▁▁▁▁ |
| NumRings | 0 | 1 | 0.0 | 1.00 | -1.14 | -0.32 | -0.32 | 0.49 | 3.74 | ▇▃▁▁▁ |
| HydrophilicFactor | 0 | 1 | 0.0 | 1.00 | -0.90 | -0.68 | -0.30 | 0.32 | 5.19 | ▇▂▁▁▁ |
| SurfaceArea1 | 0 | 1 | 0.0 | 1.00 | -1.04 | -0.75 | -0.21 | 0.53 | 5.37 | ▇▃▁▁▁ |
| SurfaceArea2 | 0 | 1 | 0.0 | 1.00 | -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 linear or non-linear relationships with the Log_Solubility 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 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] FP076 variable (factor)
[B.13] FP059 variable (factor)
[B.14] FP049 variable (factor)
[B.15] FP044 variable (factor)
[B.16] FP014 variable (factor)
[B.17] FP013 variable (factor)
Code Chunk | Output
##################################
# Loading dataset
##################################
EDA <- PMA_PreModelling_Train
##################################
# Listing all predictors
##################################
EDA.Predictors <- EDA[,!names(EDA) %in% c("Log_Solubility")]
##################################
# 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 scatter plots
##################################
featurePlot(x = EDA.Predictors.Numeric,
y = EDA$Log_Solubility,
between = list(x = 1, y = 1),
type = c("g", "p", "smooth"),
labels = rep("", 2))
##################################
# Restructuring the dataset for
# for boxplot analysis
##################################
Log_Solubility <- DPA$solTrainY
EDA.Boxplot.Source <- cbind(Log_Solubility,
EDA.Predictors.Factor)
EDA.Boxplot.Gathered.Group1 <- gather(EDA.Boxplot.Source,
'FP001','FP002','FP003','FP004','FP005',
'FP006','FP007','FP008','FP009','FP010',
'FP011','FP012','FP013','FP014','FP015',
'FP016','FP017','FP018','FP019','FP020',
'FP021','FP022','FP023','FP024','FP025',
'FP026','FP027','FP028','FP029','FP030',
'FP031','FP032','FP033','FP034','FP035',
'FP036','FP037','FP038','FP039','FP040',
key="Descriptor",
value="Structure")
EDA.Boxplot.Gathered.Group2 <- gather(EDA.Boxplot.Source,
'FP041','FP042','FP043','FP044','FP045',
'FP046','FP047','FP048','FP049','FP050',
'FP051','FP052','FP053','FP054','FP055',
'FP056','FP057','FP058','FP059','FP060',
'FP061','FP062','FP063','FP064','FP065',
'FP066','FP067','FP068','FP069','FP070',
'FP071','FP072','FP073','FP074','FP075',
'FP076','FP077','FP078','FP079','FP080',
key="Descriptor",
value="Structure")
EDA.Boxplot.Gathered.Group3 <- gather(EDA.Boxplot.Source,
'FP081','FP082','FP083','FP084','FP085',
'FP086','FP087','FP088','FP089','FP090',
'FP091','FP092','FP093','FP094','FP095',
'FP096','FP097','FP098','FP099','FP100',
'FP101','FP102','FP103','FP104','FP105',
'FP106','FP107','FP108','FP109','FP110',
'FP111','FP112','FP113','FP114','FP115',
'FP116','FP117','FP118','FP119','FP120',
key="Descriptor",
value="Structure")
EDA.Boxplot.Gathered.Group4 <- gather(EDA.Boxplot.Source,
'FP121','FP122','FP123','FP124','FP125',
'FP126','FP127','FP128','FP129','FP130',
'FP131','FP132','FP133','FP134','FP135',
'FP136','FP137','FP138','FP139','FP140',
'FP141','FP142','FP143','FP144','FP145',
'FP146','FP147','FP148','FP149','FP150',
'FP151','FP152','FP153','FP155','FP156',
'FP157','FP158','FP159','FP160','FP161',
key="Descriptor",
value="Structure")
EDA.Boxplot.Gathered.Group5 <- gather(EDA.Boxplot.Source,
'FP162','FP163','FP164','FP165','FP166',
'FP167','FP168','FP169','FP170','FP171',
'FP172','FP173','FP174','FP175','FP176',
'FP177','FP178','FP179','FP180','FP181',
'FP182','FP183','FP184','FP185','FP186',
'FP187','FP188','FP189','FP190','FP191',
'FP192','FP193','FP194','FP195','FP196',
'FP197','FP198','FP201','FP202','FP203',
'FP204','FP205','FP206','FP207','FP208',
key="Descriptor",
value="Structure")
bwplot(Log_Solubility~Structure|Descriptor,
data=EDA.Boxplot.Gathered.Group5,
ylab="Log Solubility",
xlab="Structure",
layout=(c(9,5)))
bwplot(Log_Solubility~Structure|Descriptor,
data=EDA.Boxplot.Gathered.Group4,
ylab="Log Solubility",
xlab="Structure",
layout=(c(9,5)))
bwplot(Log_Solubility~Structure|Descriptor,
data=EDA.Boxplot.Gathered.Group3,
ylab="Log Solubility",
xlab="Structure",
layout=(c(9,5)))
bwplot(Log_Solubility~Structure|Descriptor,
data=EDA.Boxplot.Gathered.Group2,
ylab="Log Solubility",
xlab="Structure",
layout=(c(9,5)))
bwplot(Log_Solubility~Structure|Descriptor,
data=EDA.Boxplot.Gathered.Group1,
ylab="Log Solubility",
xlab="Structure",
layout=(c(9,5)))
1.5 Model-Independent Feature Importance Metrics
1.5.1 Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (LOWESS_PR)
Locally Weighted Scatterplot Smoothing Pseudo-R-Squared computes the R-squared statistic - a goodness-of-fit measure which represents explained variability, improvement from null to fitted model and square of the correlation on predicted values obtained from a locally weighted scatterplot smoothing process. LOWESS consists of computing a series of local linear regressions, with each local regression restricted to a window of x-values. Smoothness is achieved by using overlapping windows and by gradually down-weighting points in each regression according to their distance from the anchor point of the window (tri-cube weighting).
[A] The locally weighted scatterplot smoothing pseudo-r-squared statistic was obtained using the caret package. High raw values of the metric indicate that the numeric predictors were associated with the numeric response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.44437
[B.2] NumCarbon = 0.36580
[B.3] NumMultBonds = 0.27588
[B.4] NumHalogen = 0.25415
[B.5] NumChlorine = 0.25407
Code Chunk | Output
##################################
# Filtering in the numeric predictors
# with the numeric 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 : num -3.97 -3.98 -3.99 -4 -4.06 -4.08 -4.08 -4.1 -4.1 -4.11 ...
## $ 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 MolWeight NumBonds NumMultBonds
## Min. :-11.620 Min. :-2.835229 Min. :-2.9239 Min. :-1.18881
## 1st Qu.: -3.955 1st Qu.:-0.798923 1st Qu.:-0.6096 1st Qu.:-0.99545
## Median : -2.510 Median :-0.008626 Median :-0.0356 Median :-0.02867
## Mean : -2.719 Mean : 0.000000 Mean : 0.0000 Mean : 0.00000
## 3rd Qu.: -1.360 3rd Qu.: 0.800109 3rd Qu.: 0.5994 3rd Qu.: 0.74476
## Max. : 1.580 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 LOWESS pseudo-R-Squared
##################################
LOWESS_PR <- filterVarImp(x = PMA_PreModelling_Train_Numeric[, 2:ncol(PMA_PreModelling_Train_Numeric)],
y = PMA_PreModelling_Train_Numeric$Log_Solubility,
nonpara = TRUE)
##################################
# Formulating the summary table
##################################
LOWESS_PR_Summary <- LOWESS_PR
LOWESS_PR_Summary$Predictor <- rownames(LOWESS_PR)
names(LOWESS_PR_Summary)[1] <- "LOWESS_PR"
LOWESS_PR_Summary$Metric <- rep("LOWESS_PR",nrow(LOWESS_PR))
LOWESS_PR_Summary## LOWESS_PR Predictor Metric
## MolWeight 4.443734e-01 MolWeight LOWESS_PR
## NumBonds 2.093744e-01 NumBonds LOWESS_PR
## NumMultBonds 2.758859e-01 NumMultBonds LOWESS_PR
## NumRotBonds 2.230342e-02 NumRotBonds LOWESS_PR
## NumDblBonds 1.530295e-06 NumDblBonds LOWESS_PR
## NumCarbon 3.657997e-01 NumCarbon LOWESS_PR
## NumNitrogen 1.045101e-02 NumNitrogen LOWESS_PR
## NumOxygen 1.710199e-02 NumOxygen LOWESS_PR
## NumSulfer 8.357325e-03 NumSulfer LOWESS_PR
## NumChlorine 2.540713e-01 NumChlorine LOWESS_PR
## NumHalogen 2.541532e-01 NumHalogen LOWESS_PR
## NumRings 2.384330e-01 NumRings LOWESS_PR
## HydrophilicFactor 1.505216e-01 HydrophilicFactor LOWESS_PR
## SurfaceArea1 1.941711e-01 SurfaceArea1 LOWESS_PR
## SurfaceArea2 2.080820e-01 SurfaceArea2 LOWESS_PR##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ LOWESS_PR | Metric,
LOWESS_PR_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 Pearson’s Correlation Coefficient (PCC)
Pearson’s Correlation Coefficient is a parametric measure of the linear correlation for a pair of features by calculating the ratio between their covariance and the product of their standard deviations. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
[A] The Pearson’s correlation coefficient was obtained using the stats package. High absolute values of the metric indicate that the numeric predictors were associated with the numeric response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.65849
[B.2] NumCarbon = 0.60481
[B.3] NumMultBonds = 0.52525
[B.4] NumHalogen = 0.50414
[B.5] NumChlorine = 0.50405
Code Chunk | Output
##################################
# Obtaining the Pearson correlation coefficient
##################################
PCC <- abs(cor(PMA_PreModelling_Train_Numeric, method="pearson")[-1,1])
##################################
# Formulating the summary table
##################################
PCC_Summary <- data.frame(Predictor = names(PMA_PreModelling_Train_Numeric)[2:ncol(PMA_PreModelling_Train_Numeric)],
PCC = PCC,
Metric = rep("PCC", length(PCC)))
PCC_Summary## Predictor PCC Metric
## MolWeight MolWeight 0.658495868 PCC
## NumBonds NumBonds 0.457574429 PCC
## NumMultBonds NumMultBonds 0.525248387 PCC
## NumRotBonds NumRotBonds 0.149343282 PCC
## NumDblBonds NumDblBonds 0.001237051 PCC
## NumCarbon NumCarbon 0.604813811 PCC
## NumNitrogen NumNitrogen 0.102230176 PCC
## NumOxygen NumOxygen 0.130774566 PCC
## NumSulfer NumSulfer 0.091418407 PCC
## NumChlorine NumChlorine 0.504054819 PCC
## NumHalogen NumHalogen 0.504136055 PCC
## NumRings NumRings 0.488295986 PCC
## HydrophilicFactor HydrophilicFactor 0.309022159 PCC
## SurfaceArea1 SurfaceArea1 0.193769382 PCC
## SurfaceArea2 SurfaceArea2 0.143941883 PCC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ PCC | Metric,
PCC_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 Spearman’s Rank Correlation Coefficient (SRCC)
Spearman’s Rank Correlation Coefficient is a non-parametric measure of the linear correlation for a pair of features by applying the Spearman’s rank equation to the sum of the squared differences between their ranks. The presence of high absolute correlation values indicate the univariate association between the numeric predictors and the numeric response.
[A] The Spearman’s correlation coefficient was obtained using the stats package. High absolute values of the metric indicate that the numeric predictors were associated with the numeric response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.68530
[B.2] NumCarbon = 0.67359
[B.3] NumBonds = 0.54840
[B.4] NumRings = 0.50942
[B.5] NumMultBonds = 0.47971
Code Chunk | Output
##################################
# Obtaining the Spearman correlation coefficient
##################################
SRCC <- abs(cor(PMA_PreModelling_Train_Numeric, method="spearman")[-1,1])
##################################
# Formulating the summary table
##################################
SRCC_Summary <- data.frame(Predictor = names(PMA_PreModelling_Train_Numeric)[2:ncol(PMA_PreModelling_Train_Numeric)],
SRCC = SRCC,
Metric = rep("SRCC", length(SRCC)))
SRCC_Summary## Predictor SRCC Metric
## MolWeight MolWeight 0.68529880 SRCC
## NumBonds NumBonds 0.54839850 SRCC
## NumMultBonds NumMultBonds 0.47971353 SRCC
## NumRotBonds NumRotBonds 0.14976036 SRCC
## NumDblBonds NumDblBonds 0.02042731 SRCC
## NumCarbon NumCarbon 0.67359114 SRCC
## NumNitrogen NumNitrogen 0.10078218 SRCC
## NumOxygen NumOxygen 0.14954994 SRCC
## NumSulfer NumSulfer 0.12090249 SRCC
## NumChlorine NumChlorine 0.35707519 SRCC
## NumHalogen NumHalogen 0.38111965 SRCC
## NumRings NumRings 0.50941815 SRCC
## HydrophilicFactor HydrophilicFactor 0.36469127 SRCC
## SurfaceArea1 SurfaceArea1 0.19339720 SRCC
## SurfaceArea2 SurfaceArea2 0.14057885 SRCC##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ SRCC | Metric,
SRCC_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 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 numeric 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 numeric response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] MolWeight = 0.46793
[B.2] NumCarbon = 0.44341
[B.3] NumBonds = 0.32687
[B.4] HydrophilicFactor = 0.32085
[B.5] NumRings = 0.31618
Code Chunk | Output
##################################
# Obtaining the maximal information coefficient
##################################
MIC <- mine(x = PMA_PreModelling_Train_Numeric[, 2:ncol(PMA_PreModelling_Train_Numeric)],
y = PMA_PreModelling_Train_Numeric$Log_Solubility)$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 0.4679277 MIC
## 2 NumBonds 0.3268683 MIC
## 3 NumMultBonds 0.2792600 MIC
## 4 NumRotBonds 0.1754215 MIC
## 5 NumDblBonds 0.1688472 MIC
## 6 NumCarbon 0.4434121 MIC
## 7 NumNitrogen 0.1535738 MIC
## 8 NumOxygen 0.1527421 MIC
## 9 NumSulfer 0.1297052 MIC
## 10 NumChlorine 0.2011708 MIC
## 11 NumHalogen 0.2017841 MIC
## 12 NumRings 0.3161828 MIC
## 13 HydrophilicFactor 0.3208456 MIC
## 14 SurfaceArea1 0.2054896 MIC
## 15 SurfaceArea2 0.2274047 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), ...)
})
1.5.5 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. Random instances and the corresponding K-nearest instances are selected, with the the weights for the different prediction values, different attributes and different prediction consolidated. The rank of the instance in a sequence of instances ordered by the distance is taken into account based on a a user-defined parameter controlling the influence of the distance. The contributions of each K-nearest instances are normalized by dividing the results with the sum of all K contributions. The presence of high relief values indicate the univariate association between the numeric predictors and the numeric 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 numeric response.
[B] The numeric predictors which demonstrated the best feature importance in terms of this metric are as follows:
[B.1] NumNitrogen = 0.11583
[B.2] NumOxygen = 0.10808
[B.3] MolWeight = 0.10335
[B.4] NumMultBonds = 0.06807
[B.5] SurfaceArea2 = 0.06623
Code Chunk | Output
##################################
# Obtaining the relief values
##################################
RV <- attrEval(Log_Solubility ~ .,
data = PMA_PreModelling_Train_Numeric,
estimator = "RReliefFequalK")
##################################
# 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 0.10334945 RV
## NumBonds NumBonds 0.03989134 RV
## NumMultBonds NumMultBonds 0.06807077 RV
## NumRotBonds NumRotBonds 0.06121663 RV
## NumDblBonds NumDblBonds 0.02974081 RV
## NumCarbon NumCarbon 0.06435924 RV
## NumNitrogen NumNitrogen 0.11583417 RV
## NumOxygen NumOxygen 0.10808031 RV
## NumSulfer NumSulfer 0.05065068 RV
## NumChlorine NumChlorine 0.02781883 RV
## NumHalogen NumHalogen 0.06144457 RV
## NumRings NumRings 0.05518173 RV
## HydrophilicFactor HydrophilicFactor 0.04195617 RV
## SurfaceArea1 SurfaceArea1 0.05366733 RV
## SurfaceArea2 SurfaceArea2 0.06623467 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), ...)
})
1.5.6 Volcano Plot - T-Test (VP_T)
T-Test P-Values define the probability of obtaining an effect during hypothesis testing, at least as large as the one actually observed in the sample data, specifically assuming that the null hypothesis is true. The test of hypothesis involved applying T-Test to evaluate for any significant differences in the means of the numeric response between two categorical predictors. The presence of low T-Test p-values indicate the univariate discrimination of the categorical predictors in terms of the numeric response.
[A] The volcano plot - t-Test p-value was obtained using the stats package. High raw values of the negative logarithm of the metric, and the absolute difference between group means indicate that the factor predictors were associated with the numeric response.
[B] The factor predictors which demonstrated the best feature importance in terms of the negative logarithm of the t-Test p-value are as follows:
[B.1] FP076 = 56.66337
[B.2] FP089 = 41.12150
[B.3] FP065 = 38.13254
[B.4] FP168 = 33.20049
[B.5] FP079 = 31.58345
[C] The factor predictors which demonstrated the best feature importance in terms of the absolute difference between group means are as follows:
[C.1] FP044 = 4.10986
[C.2] FP193 = 3.11773
[C.3] FP184 = 2.67768
[C.4] FP149 = 2.64671
[C.5] FP172 = 2.46454
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 <- 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
## 0:706 0:842 0:857 0:877 0:894 0:897 0:844 Min. :-11.620
## 1:245 1:109 1: 94 1: 74 1: 57 1: 54 1:107 1st Qu.: -3.955
## Median : -2.510
## Mean : -2.719
## 3rd Qu.: -1.360
## Max. : 1.580##################################
# Obtaining the t-test statistics
##################################
VP_T <- apply(PMA_PreModelling_Train_Factor[, 1:(ncol(PMA_PreModelling_Train_Factor)-1)],
2,
function(x, y){
tStats <- t.test(y ~ x)[c("statistic", "p.value", "estimate")]
unlist(tStats)},
y=PMA_PreModelling_Train_Factor$Log_Solubility)
##################################
# Formulating the summary table
##################################
VP_T_Summary <- as.data.frame(t(VP_T))
names(VP_T_Summary) <- c("t.Statistic", "t.Test_P.Value", "Mean0", "Mean1")
VP_T_Summary$MeanDifference <- VP_T_Summary$Mean1 - VP_T_Summary$Mean0
VP_T_Summary$Predictor <- names(PMA_PreModelling_Train_Factor[,-ncol(PMA_PreModelling_Train_Factor)])
VP_T_Summary$Metric <- rep("VP_T", nrow(VP_T_Summary))
VP_T_Summary$NegativeLog10_t.Test_P.Value <- -log10(VP_T_Summary$t.Test_P.Value)
VP_T_Summary$AbsoluteMeanDifference <- abs(VP_T_Summary$Mean1 - VP_T_Summary$Mean0)
VP_T_Summary$Group <- ifelse(rownames(VP_T_Summary)=="FP076","FP076",
ifelse(rownames(VP_T_Summary)=="FP089","FP089",
ifelse(rownames(VP_T_Summary)=="FP065","FP065",
ifelse(rownames(VP_T_Summary)=="FP044","FP044",
ifelse(rownames(VP_T_Summary)=="FP193","FP193",
"Others")))))
VP_T_Summary## t.Statistic t.Test_P.Value Mean0 Mean1 MeanDifference Predictor
## FP001 -4.02204024 6.287404e-05 -2.978465 -2.451471 0.526993515 FP001
## FP002 10.28672686 1.351580e-23 -2.021347 -3.313860 -1.292512617 FP002
## FP003 -2.03644225 4.198619e-02 -2.832164 -2.571855 0.260308757 FP003
## FP004 -4.94895770 9.551772e-07 -3.128380 -2.427428 0.700951689 FP004
## FP005 10.28247538 1.576549e-23 -1.969000 -3.262722 -1.293722323 FP005
## FP006 -7.87583806 9.287835e-15 -3.109421 -2.133832 0.975589032 FP006
## FP007 -0.88733923 3.751398e-01 -2.759967 -2.646185 0.113781971 FP007
## FP008 3.32843788 9.119521e-04 -2.582652 -2.999613 -0.416960797 FP008
## FP009 11.49360533 7.467714e-27 -2.249591 -3.926278 -1.676686955 FP009
## FP010 -4.11392307 4.973603e-05 -2.824302 -2.232824 0.591478647 FP010
## FP011 -7.01680213 1.067782e-11 -2.934645 -1.927353 1.007292306 FP011
## FP012 -1.89255407 5.953582e-02 -2.773755 -2.461369 0.312385742 FP012
## FP013 11.73267872 1.088092e-24 -2.365485 -4.490696 -2.125210704 FP013
## FP014 11.47456176 1.157457e-23 -2.375401 -4.508431 -2.133030370 FP014
## FP015 -7.73718733 1.432769e-12 -4.404286 -2.444487 1.959799162 FP015
## FP016 -0.61719794 5.377695e-01 -2.733559 -2.631007 0.102551919 FP016
## FP017 2.73915987 6.681864e-03 -2.654607 -3.098613 -0.444006259 FP017
## FP018 4.26743510 2.806561e-05 -2.643402 -3.215280 -0.571878063 FP018
## FP019 -2.31045847 2.207143e-02 -2.766910 -2.370603 0.396306731 FP019
## FP020 -3.44119896 7.251032e-04 -2.785806 -2.224912 0.560894171 FP020
## FP021 3.35165112 1.009498e-03 -2.642392 -3.272348 -0.629955482 FP021
## FP022 -0.66772403 5.051252e-01 -2.728040 -2.637071 0.090969199 FP022
## FP023 2.18958532 2.989162e-02 -2.673106 -3.042650 -0.369544057 FP023
## FP024 -2.43189276 1.617811e-02 -2.766457 -2.340841 0.425616224 FP024
## FP025 -2.68651403 7.981132e-03 -2.771677 -2.312545 0.459131121 FP025
## FP026 0.58596455 5.591541e-01 -2.709082 -2.821875 -0.112793485 FP026
## FP027 -4.46177875 1.714807e-05 -2.793800 -2.024516 0.769283405 FP027
## FP028 -3.36478123 1.011310e-03 -2.791941 -2.101089 0.690852068 FP028
## FP029 1.50309317 1.346711e-01 -2.696475 -2.913093 -0.216617374 FP029
## FP030 -4.18564626 5.684141e-05 -2.799582 -1.933933 0.865649782 FP030
## FP031 -0.19030898 8.494207e-01 -2.721986 -2.683765 0.038221437 FP031
## FP032 -2.86824205 5.100440e-03 -2.757832 -2.224429 0.533403438 FP032
## FP033 -2.48343886 1.492327e-02 -2.751062 -2.282879 0.468183359 FP033
## FP034 0.81786492 4.147985e-01 -2.709737 -2.820263 -0.110526015 FP034
## FP035 4.17698556 6.851675e-05 -2.659660 -3.471594 -0.811934339 FP035
## FP036 -5.31186085 6.344823e-07 -2.787224 -1.880417 0.906807452 FP036
## FP037 1.37213471 1.734895e-01 -2.700271 -2.960000 -0.259728507 FP037
## FP038 -2.55044552 1.224045e-02 -2.764833 -2.228293 0.536540459 FP038
## FP039 6.83856010 1.396591e-09 -2.588330 -4.332817 -1.744487356 FP039
## FP040 -4.96957478 3.640553e-06 -2.788036 -1.771692 1.016343810 FP040
## FP041 3.86443922 2.274448e-04 -2.672424 -3.403833 -0.731409091 FP041
## FP042 -1.10149897 2.742144e-01 -2.729509 -2.536852 0.192657624 FP042
## FP043 -0.18525729 8.535189e-01 -2.721284 -2.680317 0.040966323 FP043
## FP044 15.19844350 1.458342e-22 -2.472237 -6.582105 -4.109868127 FP044
## FP045 3.26197779 1.781037e-03 -2.678118 -3.403962 -0.725844224 FP045
## FP046 7.19096539 1.949765e-12 -2.405146 -3.398700 -0.993554071 FP046
## FP047 3.08813847 2.106659e-03 -2.611605 -3.013676 -0.402071305 FP047
## FP048 0.78156187 4.354510e-01 -2.703337 -2.826102 -0.122764360 FP048
## FP049 9.32620107 1.541509e-16 -2.494036 -4.334828 -1.840791658 FP049
## FP050 1.78989997 7.537562e-02 -2.684810 -2.984860 -0.300049387 FP050
## FP051 3.85923300 1.590148e-04 -2.656482 -3.224231 -0.567749069 FP051
## FP052 -1.37622794 1.707261e-01 -2.736296 -2.542529 0.193767561 FP052
## FP053 7.79872544 3.863769e-12 -2.565418 -4.201910 -1.636492479 FP053
## FP054 4.71268264 7.815108e-06 -2.656678 -3.474167 -0.817488623 FP054
## FP055 -2.15047129 3.539774e-02 -2.743122 -2.285294 0.457828105 FP055
## FP056 6.56517336 8.289424e-09 -2.598841 -4.435323 -1.836481186 FP056
## FP057 1.55970276 1.207241e-01 -2.686667 -2.952807 -0.266140351 FP057
## FP058 1.31266618 1.913070e-01 -2.691483 -2.930000 -0.238517200 FP058
## FP059 5.30327181 1.388228e-06 -2.662258 -3.692115 -1.029857320 FP059
## FP060 -6.34967826 3.396521e-10 -3.112819 -2.294192 0.818627333 FP060
## FP061 -3.23528852 1.258017e-03 -2.903859 -2.489247 0.414612257 FP061
## FP062 -4.68040368 3.284921e-06 -2.978056 -2.384856 0.593200306 FP062
## FP063 -5.90647947 4.865776e-09 -3.037509 -2.288593 0.748916565 FP063
## FP064 -3.19849081 1.427257e-03 -2.887640 -2.481616 0.406023478 FP064
## FP065 13.67947483 7.369864e-39 -1.740827 -3.389468 -1.648641212 FP065
## FP066 -3.50425986 4.936856e-04 -3.034043 -2.516776 0.517267265 FP066
## FP067 -3.71025855 2.192910e-04 -2.894797 -2.430554 0.464242594 FP067
## FP068 -4.50468714 7.534223e-06 -2.923921 -2.356221 0.567699992 FP068
## FP069 -1.39582672 1.631126e-01 -2.782438 -2.605872 0.176566128 FP069
## FP070 11.33500604 6.532630e-27 -2.155840 -3.739142 -1.583301881 FP070
## FP071 9.16039412 1.012284e-18 -2.295828 -3.588521 -1.292692775 FP071
## FP072 -9.86673490 4.502526e-21 -3.674277 -2.222396 1.451880757 FP072
## FP073 -6.31556184 4.773987e-10 -2.972104 -2.154780 0.817323998 FP073
## FP074 -3.16365915 1.617158e-03 -2.849299 -2.446958 0.402341137 FP074
## FP075 -4.83159241 1.618286e-06 -2.926916 -2.311584 0.615331888 FP075
## FP076 18.19671006 2.170836e-57 -1.949953 -4.292756 -2.342803359 FP076
## FP077 -0.24434665 8.070283e-01 -2.728715 -2.697082 0.031633203 FP077
## FP078 -0.49694487 6.193690e-01 -2.737523 -2.675156 0.062366949 FP078
## FP079 12.46647477 2.609452e-32 -1.649763 -3.199207 -1.549444605 FP079
## FP080 -4.44534892 1.029202e-05 -2.896848 -2.308160 0.588687940 FP080
## FP081 0.11125946 9.114457e-01 -2.714519 -2.729057 -0.014537653 FP081
## FP082 12.55490234 3.329065e-32 -1.573824 -3.177143 -1.603319328 FP082
## FP083 -6.28835488 5.760827e-10 -2.932735 -2.149385 0.783350551 FP083
## FP084 -3.43524930 6.332047e-04 -2.851414 -2.386949 0.464465314 FP084
## FP085 10.47209331 1.134762e-22 -2.307585 -3.916008 -1.608423485 FP085
## FP086 1.02088695 3.077271e-01 -2.682101 -2.817578 -0.135477406 FP086
## FP087 11.07193302 5.850147e-26 -1.684808 -3.107540 -1.422732105 FP087
## FP088 -4.82078133 1.873320e-06 -2.891398 -2.233960 0.657438003 FP088
## FP089 15.68684642 7.559612e-42 -2.131606 -4.506936 -2.375330025 FP089
## FP090 0.72850761 4.666345e-01 -2.693950 -2.792743 -0.098793036 FP090
## FP091 -1.97821299 4.847758e-02 -2.777626 -2.515187 0.262438593 FP091
## FP092 12.71461669 9.160201e-31 -2.250250 -4.169957 -1.919706549 FP092
## FP093 2.40580805 1.652056e-02 -2.636787 -2.972026 -0.335238658 FP093
## FP094 -1.08529331 2.783195e-01 -2.751874 -2.607909 0.143965054 FP094
## FP095 -4.83150303 1.885749e-06 -2.863571 -2.203780 0.659791524 FP095
## FP096 -0.05816460 9.536450e-01 -2.720323 -2.712271 0.008052049 FP096
## FP097 9.06740092 4.508890e-18 -2.420977 -3.684420 -1.263443027 FP097
## FP098 -3.09495737 2.088014e-03 -2.820538 -2.391460 0.429077754 FP098
## FP099 4.51553294 8.153915e-06 -2.575959 -3.203843 -0.627883409 FP099
## FP100 -4.26730797 2.354655e-05 -2.846430 -2.293727 0.552702276 FP100
## FP101 -3.33565277 9.211008e-04 -2.828760 -2.363022 0.465738108 FP101
## FP102 1.25032500 2.119440e-01 -2.683373 -2.857708 -0.174335474 FP102
## FP103 2.51185846 1.236590e-02 -2.644038 -2.984808 -0.340770007 FP103
## FP104 1.23433987 2.176989e-01 -2.681746 -2.846934 -0.165188360 FP104
## FP105 2.56644125 1.063908e-02 -2.640201 -3.003756 -0.363555025 FP105
## FP106 2.42187970 1.595574e-02 -2.652367 -2.998297 -0.345929993 FP106
## FP107 10.92623859 2.395320e-23 -2.328707 -4.173284 -1.844576915 FP107
## FP108 -0.88386799 3.773218e-01 -2.744087 -2.619641 0.124446276 FP108
## FP109 1.72666429 8.493856e-02 -2.681392 -2.891845 -0.210453156 FP109
## FP110 -4.30633122 2.083157e-05 -2.839272 -2.253622 0.585649074 FP110
## FP111 0.07891212 9.371465e-01 -2.716361 -2.727594 -0.011232326 FP111
## FP112 13.31169435 4.090297e-31 -2.293512 -4.478541 -2.185028791 FP112
## FP113 -4.25438885 2.743420e-05 -2.842824 -2.207527 0.635296648 FP113
## FP114 0.38442341 7.009005e-01 -2.711034 -2.759459 -0.048425836 FP114
## FP115 -0.49398272 6.216320e-01 -2.730653 -2.663059 0.067594185 FP115
## FP116 -3.39726200 7.657795e-04 -2.815911 -2.310055 0.505856814 FP116
## FP117 3.16005628 1.769096e-03 -2.623060 -3.157353 -0.534292762 FP117
## FP118 -3.88255786 1.272871e-04 -2.835755 -2.226776 0.608979252 FP118
## FP119 -0.71996857 4.720764e-01 -2.734485 -2.636839 0.097646215 FP119
## FP120 -3.25854728 1.280523e-03 -2.807793 -2.270759 0.537033697 FP120
## FP121 0.62156119 5.349141e-01 -2.704487 -2.805188 -0.100701417 FP121
## FP122 -2.44169102 1.530759e-02 -2.781836 -2.396154 0.385682632 FP122
## FP123 3.52755166 4.929055e-04 -2.628914 -3.165157 -0.536243091 FP123
## FP124 -3.58983366 3.953044e-04 -2.806888 -2.261494 0.545394825 FP124
## FP125 -2.91655379 3.853055e-03 -2.786364 -2.350743 0.435620393 FP125
## FP126 -1.44180023 1.505173e-01 -2.748395 -2.547234 0.201161019 FP126
## FP127 -2.66597987 8.213408e-03 -2.773386 -2.381429 0.391957737 FP127
## FP128 -3.37747584 8.536233e-04 -2.794086 -2.284752 0.509334647 FP128
## FP129 3.28855844 1.192299e-03 -2.642100 -3.193030 -0.550930181 FP129
## FP130 1.02990587 3.048783e-01 -2.698555 -2.888900 -0.190345358 FP130
## FP131 -0.49682548 6.198471e-01 -2.727954 -2.653583 0.074370939 FP131
## FP132 -5.89680424 1.633112e-08 -2.832055 -1.925126 0.906929238 FP132
## FP133 -1.83896087 6.756107e-02 -2.757100 -2.451750 0.305349880 FP133
## FP134 3.16620016 1.761695e-03 -2.661506 -3.110000 -0.448493976 FP134
## FP135 -2.94236705 3.709259e-03 -2.783827 -2.266667 0.517160048 FP135
## FP136 -2.02006233 4.501990e-02 -2.761938 -2.403304 0.358633451 FP136
## FP137 -0.07855180 9.374873e-01 -2.720131 -2.706636 0.013494433 FP137
## FP138 -1.44829927 1.496787e-01 -2.748083 -2.483302 0.264780953 FP138
## FP139 -0.22212826 8.246439e-01 -2.721936 -2.680897 0.041038417 FP139
## FP140 -1.86990507 6.355486e-02 -2.758036 -2.403962 0.354073239 FP140
## FP141 4.15441700 4.792655e-05 -2.650655 -3.232523 -0.581867761 FP141
## FP142 -2.92307611 4.047862e-03 -2.779233 -2.224519 0.554713355 FP142
## FP143 0.83414756 4.061300e-01 -2.705904 -2.862338 -0.156433772 FP143
## FP144 -4.98991305 1.904653e-06 -2.819214 -1.852424 0.966789373 FP144
## FP145 -3.99831545 1.002597e-04 -2.787077 -2.128990 0.658087566 FP145
## FP146 6.08904552 1.064009e-08 -2.608687 -3.675000 -1.066313013 FP146
## FP147 -2.98364059 3.376138e-03 -2.776357 -2.226800 0.549557227 FP147
## FP148 -4.00444775 1.101041e-04 -2.780300 -2.073012 0.707287491 FP148
## FP149 9.67498002 8.530838e-16 -2.479225 -5.125930 -2.646704799 FP149
## FP150 -1.59224059 1.145443e-01 -2.742808 -2.435467 0.307341553 FP150
## FP151 -1.68674372 9.608846e-02 -2.736013 -2.423019 0.312994495 FP151
## FP152 2.02103329 4.549820e-02 -2.692325 -3.012308 -0.319982377 FP152
## FP153 0.83775227 4.044086e-01 -2.703900 -2.892432 -0.188532775 FP153
## FP155 4.93743429 3.813516e-06 -2.653412 -3.592273 -0.938860298 FP155
## FP156 2.70254904 8.178498e-03 -2.685045 -3.160896 -0.475850274 FP156
## FP157 -1.19798365 2.351567e-01 -2.738105 -2.423220 0.314885042 FP157
## FP158 -3.18371959 2.293303e-03 -2.757078 -2.039020 0.718058170 FP158
## FP159 2.90626659 4.444806e-03 -2.687590 -3.127313 -0.439722935 FP159
## FP160 0.72930617 4.673596e-01 -2.711400 -2.816308 -0.104908144 FP160
## FP161 -8.02084404 8.158474e-12 -2.826779 -1.193333 1.633445946 FP161
## FP162 9.05654884 7.502729e-19 -2.147208 -3.300849 -1.153640924 FP162
## FP163 -4.73411111 2.565152e-06 -3.009759 -2.398455 0.611304290 FP163
## FP164 11.15556043 6.131703e-27 -1.830706 -3.245042 -1.414335661 FP164
## FP165 -3.26163144 1.150990e-03 -2.862294 -2.450602 0.411691613 FP165
## FP166 6.01599552 3.059094e-09 -2.441541 -3.277905 -0.836363881 FP166
## FP167 -3.77468033 1.718080e-04 -2.874742 -2.398718 0.476023835 FP167
## FP168 12.78784085 6.302482e-34 -1.659686 -3.250521 -1.590835792 FP168
## FP169 10.79840624 1.952902e-22 -2.370413 -4.241017 -1.870603512 FP169
## FP170 1.45059296 1.480425e-01 -2.674961 -2.911943 -0.236981517 FP170
## FP171 -3.56151646 4.354270e-04 -2.810722 -2.266398 0.544324003 FP171
## FP172 13.04070659 8.112523e-28 -2.345390 -4.809931 -2.464540221 FP172
## FP173 2.68918003 7.770466e-03 -2.653554 -3.111556 -0.458001634 FP173
## FP174 0.94721964 3.446525e-01 -2.699492 -2.845806 -0.146314311 FP174
## FP175 0.01020115 9.918704e-01 -2.718360 -2.719922 -0.001562215 FP175
## FP176 -2.29447613 2.298911e-02 -2.766395 -2.374310 0.392084865 FP176
## FP177 -1.08253877 2.802959e-01 -2.737548 -2.580609 0.156939151 FP177
## FP178 3.27582610 1.258481e-03 -2.656782 -3.167739 -0.510956834 FP178
## FP179 0.85670987 3.931634e-01 -2.703846 -2.854409 -0.150562448 FP179
## FP180 -2.83913345 5.188161e-03 -2.773274 -2.263235 0.510039146 FP180
## FP181 6.24259165 6.005980e-09 -2.617726 -3.695281 -1.077554681 FP181
## FP182 -2.11887211 3.595632e-02 -2.755239 -2.384255 0.370983887 FP182
## FP183 -2.62186301 1.015591e-02 -2.755210 -2.271250 0.483960466 FP183
## FP184 10.24979020 9.572172e-17 -2.493318 -5.171000 -2.677681975 FP184
## FP185 3.21519455 1.718715e-03 -2.667230 -3.270000 -0.602770115 FP185
## FP186 -2.10893733 3.756740e-02 -2.749818 -2.342740 0.407078042 FP186
## FP187 -0.14233858 8.871705e-01 -2.721122 -2.685942 0.035180420 FP187
## FP188 -2.76497219 7.083803e-03 -2.760011 -2.153692 0.606318979 FP188
## FP189 0.29230393 7.707177e-01 -2.713884 -2.774932 -0.061047680 FP189
## FP190 8.23796541 2.799252e-12 -2.574785 -4.556522 -1.981737159 FP190
## FP191 -1.62000293 1.089976e-01 -2.742364 -2.404627 0.337737388 FP191
## FP192 0.55100083 5.833593e-01 -2.711377 -2.829310 -0.117932965 FP192
## FP193 11.06173597 1.595927e-16 -2.525146 -5.642881 -3.117735616 FP193
## FP194 -1.03294441 3.047671e-01 -2.728916 -2.553214 0.175701915 FP194
## FP195 -5.88072667 1.035398e-07 -2.786495 -1.672759 1.113736340 FP195
## FP196 6.42707826 1.269199e-08 -2.651126 -3.838889 -1.187762913 FP196
## FP197 3.82944792 3.167065e-04 -2.670555 -3.583800 -0.913245061 FP197
## FP198 -3.87872401 2.598433e-04 -2.776165 -1.761852 1.014313143 FP198
## FP201 -3.92629512 2.100852e-04 -2.757414 -2.018600 0.738813984 FP201
## FP202 5.92935333 6.082278e-09 -2.496969 -3.357143 -0.860174019 FP202
## FP203 1.09341446 2.759667e-01 -2.695582 -2.896147 -0.200564841 FP203
## FP204 2.86078975 4.868444e-03 -2.672159 -3.141702 -0.469543435 FP204
## FP205 5.61427744 2.488511e-07 -2.605564 -4.057838 -1.452273414 FP205
## FP206 3.58353985 6.162975e-04 -2.674519 -3.409474 -0.734954669 FP206
## FP207 8.34894566 1.153650e-11 -2.595151 -4.768704 -2.173553202 FP207
## FP208 1.37823055 1.702203e-01 -2.690237 -2.942056 -0.251819108 FP208
## Metric NegativeLog10_t.Test_P.Value AbsoluteMeanDifference Group
## FP001 VP_T 4.201528604 0.526993515 Others
## FP002 VP_T 22.869158221 1.292512617 Others
## FP003 VP_T 1.376893575 0.260308757 Others
## FP004 VP_T 6.019916058 0.700951689 Others
## FP005 VP_T 22.802292476 1.293722323 Others
## FP006 VP_T 14.032085507 0.975589032 Others
## FP007 VP_T 0.425806855 0.113781971 Others
## FP008 VP_T 3.040027990 0.416960797 Others
## FP009 VP_T 26.126812307 1.676686955 Others
## FP010 VP_T 4.303328923 0.591478647 Others
## FP011 VP_T 10.971517457 1.007292306 Others
## FP012 VP_T 1.225221683 0.312385742 Others
## FP013 VP_T 23.963334377 2.125210704 Others
## FP014 VP_T 22.936495310 2.133030370 Others
## FP015 VP_T 11.843823827 1.959799162 Others
## FP016 VP_T 0.269403810 0.102551919 Others
## FP017 VP_T 2.175102341 0.444006259 Others
## FP018 VP_T 4.551825465 0.571878063 Others
## FP019 VP_T 1.656169620 0.396306731 Others
## FP020 VP_T 3.139600190 0.560894171 Others
## FP021 VP_T 2.995894414 0.629955482 Others
## FP022 VP_T 0.296600971 0.090969199 Others
## FP023 VP_T 1.524450486 0.369544057 Others
## FP024 VP_T 1.791072101 0.425616224 Others
## FP025 VP_T 2.097935479 0.459131121 Others
## FP026 VP_T 0.252468464 0.112793485 Others
## FP027 VP_T 4.765784712 0.769283405 Others
## FP028 VP_T 2.995115516 0.690852068 Others
## FP029 VP_T 0.870725522 0.216617374 Others
## FP030 VP_T 4.245335156 0.865649782 Others
## FP031 VP_T 0.070877154 0.038221437 Others
## FP032 VP_T 2.292392384 0.533403438 Others
## FP033 VP_T 1.826136061 0.468183359 Others
## FP034 VP_T 0.382162790 0.110526015 Others
## FP035 VP_T 4.164203229 0.811934339 Others
## FP036 VP_T 6.197580475 0.906807452 Others
## FP037 VP_T 0.760726840 0.259728507 Others
## FP038 VP_T 1.912202707 0.536540459 Others
## FP039 VP_T 8.854930829 1.744487356 Others
## FP040 VP_T 5.438832659 1.016343810 Others
## FP041 VP_T 3.643123974 0.731409091 Others
## FP042 VP_T 0.561909802 0.192657624 Others
## FP043 VP_T 0.068786845 0.040966323 Others
## FP044 VP_T 21.836140499 4.109868127 FP044
## FP045 VP_T 2.749327017 0.725844224 Others
## FP046 VP_T 11.710017638 0.993554071 Others
## FP047 VP_T 2.676405787 0.402071305 Others
## FP048 VP_T 0.361060666 0.122764360 Others
## FP049 VP_T 15.812053987 1.840791658 Others
## FP050 VP_T 1.122769076 0.300049387 Others
## FP051 VP_T 3.798562513 0.567749069 Others
## FP052 VP_T 0.767700036 0.193767561 Others
## FP053 VP_T 11.412988867 1.636492479 Others
## FP054 VP_T 5.107065013 0.817488623 Others
## FP055 VP_T 1.451024435 0.457828105 Others
## FP056 VP_T 8.081475624 1.836481186 Others
## FP057 VP_T 0.918205855 0.266140351 Others
## FP058 VP_T 0.718269116 0.238517200 Others
## FP059 VP_T 5.857539068 1.029857320 Others
## FP060 VP_T 9.468965656 0.818627333 Others
## FP061 VP_T 2.900313656 0.414612257 Others
## FP062 VP_T 5.483475029 0.593200306 Others
## FP063 VP_T 8.312847852 0.748916565 Others
## FP064 VP_T 2.845497682 0.406023478 Others
## FP065 VP_T 38.132540555 1.648641212 FP065
## FP066 VP_T 3.306549572 0.517267265 Others
## FP067 VP_T 3.658979139 0.464242594 Others
## FP068 VP_T 5.122961557 0.567699992 Others
## FP069 VP_T 0.787512584 0.176566128 Others
## FP070 VP_T 26.184911955 1.583301881 Others
## FP071 VP_T 17.994697417 1.292692775 Others
## FP072 VP_T 20.346543773 1.451880757 Others
## FP073 VP_T 9.321118800 0.817323998 Others
## FP074 VP_T 2.791247434 0.402341137 Others
## FP075 VP_T 5.790944794 0.615331888 Others
## FP076 VP_T 56.663372962 2.342803359 FP076
## FP077 VP_T 0.093111212 0.031633203 Others
## FP078 VP_T 0.208050512 0.062366949 Others
## FP079 VP_T 31.583450764 1.549444605 Others
## FP080 VP_T 4.987499431 0.588687940 Others
## FP081 VP_T 0.040269209 0.014537653 Others
## FP082 VP_T 31.477677785 1.603319328 Others
## FP083 VP_T 9.239515137 0.783350551 Others
## FP084 VP_T 3.198455901 0.464465314 Others
## FP085 VP_T 21.945095151 1.608423485 Others
## FP086 VP_T 0.511834307 0.135477406 Others
## FP087 VP_T 25.232833199 1.422732105 Others
## FP088 VP_T 5.727388075 0.657438003 Others
## FP089 VP_T 41.121500514 2.375330025 FP089
## FP090 VP_T 0.331023127 0.098793036 Others
## FP091 VP_T 1.314459073 0.262438593 Others
## FP092 VP_T 30.038095004 1.919706549 Others
## FP093 VP_T 1.781975360 0.335238658 Others
## FP094 VP_T 0.555456425 0.143965054 Others
## FP095 VP_T 5.724516186 0.659791524 Others
## FP096 VP_T 0.020613278 0.008052049 Others
## FP097 VP_T 17.345930392 1.263443027 Others
## FP098 VP_T 2.680266578 0.429077754 Others
## FP099 VP_T 5.088633828 0.627883409 Others
## FP100 VP_T 4.628072635 0.552702276 Others
## FP101 VP_T 3.035692821 0.465738108 Others
## FP102 VP_T 0.673778966 0.174335474 Others
## FP103 VP_T 1.907774119 0.340770007 Others
## FP104 VP_T 0.662143682 0.165188360 Others
## FP105 VP_T 1.973096120 0.363555025 Others
## FP106 VP_T 1.797083057 0.345929993 Others
## FP107 VP_T 22.620636474 1.844576915 Others
## FP108 VP_T 0.423288091 0.124446276 Others
## FP109 VP_T 1.070895104 0.210453156 Others
## FP110 VP_T 4.681277976 0.585649074 Others
## FP111 VP_T 0.028192507 0.011232326 Others
## FP112 VP_T 30.388245112 2.185028791 Others
## FP113 VP_T 4.561707729 0.635296648 Others
## FP114 VP_T 0.154343648 0.048425836 Others
## FP115 VP_T 0.206466608 0.067594185 Others
## FP116 VP_T 3.115896288 0.505856814 Others
## FP117 VP_T 2.752248553 0.534292762 Others
## FP118 VP_T 3.895215544 0.608979252 Others
## FP119 VP_T 0.325987674 0.097646215 Others
## FP120 VP_T 2.892612706 0.537033697 Others
## FP121 VP_T 0.271715970 0.100701417 Others
## FP122 VP_T 1.815093207 0.385682632 Others
## FP123 VP_T 3.307236303 0.536243091 Others
## FP124 VP_T 3.403068381 0.545394825 Others
## FP125 VP_T 2.414194812 0.435620393 Others
## FP126 VP_T 0.822413480 0.201161019 Others
## FP127 VP_T 2.085476582 0.391957737 Others
## FP128 VP_T 3.068733732 0.509334647 Others
## FP129 VP_T 2.923614965 0.550930181 Others
## FP130 VP_T 0.515873515 0.190345358 Others
## FP131 VP_T 0.207715419 0.074370939 Others
## FP132 VP_T 7.786984099 0.906929238 Others
## FP133 VP_T 1.170303502 0.305349880 Others
## FP134 VP_T 2.754069157 0.448493976 Others
## FP135 VP_T 2.430712786 0.517160048 Others
## FP136 VP_T 1.346595486 0.358633451 Others
## FP137 VP_T 0.028034596 0.013494433 Others
## FP138 VP_T 0.824839912 0.264780953 Others
## FP139 VP_T 0.083733572 0.041038417 Others
## FP140 VP_T 1.196851246 0.354073239 Others
## FP141 VP_T 4.319423830 0.581867761 Others
## FP142 VP_T 2.392774312 0.554713355 Others
## FP143 VP_T 0.391334955 0.156433772 Others
## FP144 VP_T 5.720184136 0.966789373 Others
## FP145 VP_T 3.998873639 0.658087566 Others
## FP146 VP_T 7.973054883 1.066313013 Others
## FP147 VP_T 2.471579818 0.549557227 Others
## FP148 VP_T 3.958196591 0.707287491 Others
## FP149 VP_T 15.069008282 2.646704799 Others
## FP150 VP_T 0.941026543 0.307341553 Others
## FP151 VP_T 1.017328768 0.312994495 Others
## FP152 VP_T 1.342005745 0.319982377 Others
## FP153 VP_T 0.393179623 0.188532775 Others
## FP155 VP_T 5.418674482 0.938860298 Others
## FP156 VP_T 2.087326470 0.475850274 Others
## FP157 VP_T 0.628642607 0.314885042 Others
## FP158 VP_T 2.639538515 0.718058170 Others
## FP159 VP_T 2.352147186 0.439722935 Others
## FP160 VP_T 0.330348826 0.104908144 Others
## FP161 VP_T 11.088391050 1.633445946 Others
## FP162 VP_T 18.124780712 1.153640924 Others
## FP163 VP_T 5.590886912 0.611304290 Others
## FP164 VP_T 26.212418905 1.414335661 Others
## FP165 VP_T 2.938928297 0.411691613 Others
## FP166 VP_T 8.514407198 0.836363881 Others
## FP167 VP_T 3.764956674 0.476023835 Others
## FP168 VP_T 33.200488373 1.590835792 Others
## FP169 VP_T 21.709319649 1.870603512 Others
## FP170 VP_T 0.829613712 0.236981517 Others
## FP171 VP_T 3.361084630 0.544324003 Others
## FP172 VP_T 27.090844062 2.464540221 Others
## FP173 VP_T 2.109552932 0.458001634 Others
## FP174 VP_T 0.462618628 0.146314311 Others
## FP175 VP_T 0.003545084 0.001562215 Others
## FP176 VP_T 1.638477843 0.392084865 Others
## FP177 VP_T 0.552383284 0.156939151 Others
## FP178 VP_T 2.900153450 0.510956834 Others
## FP179 VP_T 0.405426904 0.150562448 Others
## FP180 VP_T 2.284986523 0.510039146 Others
## FP181 VP_T 8.221416123 1.077554681 Others
## FP182 VP_T 1.444224705 0.370983887 Others
## FP183 VP_T 1.993281105 0.483960466 Others
## FP184 VP_T 16.018989514 2.677681975 Others
## FP185 VP_T 2.764796188 0.602770115 Others
## FP186 VP_T 1.425188897 0.407078042 Others
## FP187 VP_T 0.051992920 0.035180420 Others
## FP188 VP_T 2.149733499 0.606318979 Others
## FP189 VP_T 0.113104683 0.061047680 Others
## FP190 VP_T 11.552957932 1.981737159 Others
## FP191 VP_T 0.962583182 0.337737388 Others
## FP192 VP_T 0.234063851 0.117932965 Others
## FP193 VP_T 15.796986918 3.117735616 FP193
## FP194 VP_T 0.516031959 0.175701915 Others
## FP195 VP_T 6.984892592 1.113736340 Others
## FP196 VP_T 7.896470322 1.187762913 Others
## FP197 VP_T 3.499343085 0.913245061 Others
## FP198 VP_T 3.585288501 1.014313143 Others
## FP201 VP_T 3.677604573 0.738813984 Others
## FP202 VP_T 8.215933727 0.860174019 Others
## FP203 VP_T 0.559143393 0.200564841 Others
## FP204 VP_T 2.312609803 0.469543435 Others
## FP205 VP_T 6.604060360 1.452273414 Others
## FP206 VP_T 3.210209626 0.734954669 Others
## FP207 VP_T 10.937926026 2.173553202 Others
## FP208 VP_T 0.768988609 0.251819108 Others##################################
# Selecting the best-performing
# predictors based from metrics
##################################
VP_T_Summary_Top15_TTestPValue <- VP_T_Summary[order(VP_T_Summary$NegativeLog10_t.Test_P.Value,decreasing=TRUE),]
(VP_T_Summary_Top15_TTestPValue <- VP_T_Summary_Top15_TTestPValue[1:15,])## t.Statistic t.Test_P.Value Mean0 Mean1 MeanDifference Predictor
## FP076 18.19671 2.170836e-57 -1.949953 -4.292756 -2.342803 FP076
## FP089 15.68685 7.559612e-42 -2.131606 -4.506936 -2.375330 FP089
## FP065 13.67947 7.369864e-39 -1.740827 -3.389468 -1.648641 FP065
## FP168 12.78784 6.302482e-34 -1.659686 -3.250521 -1.590836 FP168
## FP079 12.46647 2.609452e-32 -1.649763 -3.199207 -1.549445 FP079
## FP082 12.55490 3.329065e-32 -1.573824 -3.177143 -1.603319 FP082
## FP112 13.31169 4.090297e-31 -2.293512 -4.478541 -2.185029 FP112
## FP092 12.71462 9.160201e-31 -2.250250 -4.169957 -1.919707 FP092
## FP172 13.04071 8.112523e-28 -2.345390 -4.809931 -2.464540 FP172
## FP164 11.15556 6.131703e-27 -1.830706 -3.245042 -1.414336 FP164
## FP070 11.33501 6.532630e-27 -2.155840 -3.739142 -1.583302 FP070
## FP009 11.49361 7.467714e-27 -2.249591 -3.926278 -1.676687 FP009
## FP087 11.07193 5.850147e-26 -1.684808 -3.107540 -1.422732 FP087
## FP013 11.73268 1.088092e-24 -2.365485 -4.490696 -2.125211 FP013
## FP014 11.47456 1.157457e-23 -2.375401 -4.508431 -2.133030 FP014
## Metric NegativeLog10_t.Test_P.Value AbsoluteMeanDifference Group
## FP076 VP_T 56.66337 2.342803 FP076
## FP089 VP_T 41.12150 2.375330 FP089
## FP065 VP_T 38.13254 1.648641 FP065
## FP168 VP_T 33.20049 1.590836 Others
## FP079 VP_T 31.58345 1.549445 Others
## FP082 VP_T 31.47768 1.603319 Others
## FP112 VP_T 30.38825 2.185029 Others
## FP092 VP_T 30.03810 1.919707 Others
## FP172 VP_T 27.09084 2.464540 Others
## FP164 VP_T 26.21242 1.414336 Others
## FP070 VP_T 26.18491 1.583302 Others
## FP009 VP_T 26.12681 1.676687 Others
## FP087 VP_T 25.23283 1.422732 Others
## FP013 VP_T 23.96333 2.125211 Others
## FP014 VP_T 22.93650 2.133030 OthersVP_T_Summary_Top15_AbsoluteMeanDifference <- VP_T_Summary[order(VP_T_Summary$AbsoluteMeanDifference,decreasing=TRUE),]
(VP_T_Summary_Top15_AbsoluteMeanDifference <- VP_T_Summary_Top15_AbsoluteMeanDifference[1:15,])## t.Statistic t.Test_P.Value Mean0 Mean1 MeanDifference Predictor
## FP044 15.198443 1.458342e-22 -2.472237 -6.582105 -4.109868 FP044
## FP193 11.061736 1.595927e-16 -2.525146 -5.642881 -3.117736 FP193
## FP184 10.249790 9.572172e-17 -2.493318 -5.171000 -2.677682 FP184
## FP149 9.674980 8.530838e-16 -2.479225 -5.125930 -2.646705 FP149
## FP172 13.040707 8.112523e-28 -2.345390 -4.809931 -2.464540 FP172
## FP089 15.686846 7.559612e-42 -2.131606 -4.506936 -2.375330 FP089
## FP076 18.196710 2.170836e-57 -1.949953 -4.292756 -2.342803 FP076
## FP112 13.311694 4.090297e-31 -2.293512 -4.478541 -2.185029 FP112
## FP207 8.348946 1.153650e-11 -2.595151 -4.768704 -2.173553 FP207
## FP014 11.474562 1.157457e-23 -2.375401 -4.508431 -2.133030 FP014
## FP013 11.732679 1.088092e-24 -2.365485 -4.490696 -2.125211 FP013
## FP190 8.237965 2.799252e-12 -2.574785 -4.556522 -1.981737 FP190
## FP015 -7.737187 1.432769e-12 -4.404286 -2.444487 1.959799 FP015
## FP092 12.714617 9.160201e-31 -2.250250 -4.169957 -1.919707 FP092
## FP169 10.798406 1.952902e-22 -2.370413 -4.241017 -1.870604 FP169
## Metric NegativeLog10_t.Test_P.Value AbsoluteMeanDifference Group
## FP044 VP_T 21.83614 4.109868 FP044
## FP193 VP_T 15.79699 3.117736 FP193
## FP184 VP_T 16.01899 2.677682 Others
## FP149 VP_T 15.06901 2.646705 Others
## FP172 VP_T 27.09084 2.464540 Others
## FP089 VP_T 41.12150 2.375330 FP089
## FP076 VP_T 56.66337 2.342803 FP076
## FP112 VP_T 30.38825 2.185029 Others
## FP207 VP_T 10.93793 2.173553 Others
## FP014 VP_T 22.93650 2.133030 Others
## FP013 VP_T 23.96333 2.125211 Others
## FP190 VP_T 11.55296 1.981737 Others
## FP015 VP_T 11.84382 1.959799 Others
## FP092 VP_T 30.03810 1.919707 Others
## FP169 VP_T 21.70932 1.870604 Others##################################
# Exploring predictor performance
##################################
dotplot(Predictor ~ NegativeLog10_t.Test_P.Value | Metric,
VP_T_Summary_Top15_TTestPValue,
origin = 0,
xlab = "-Log10(T-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 ~ AbsoluteMeanDifference | Metric,
VP_T_Summary_Top15_AbsoluteMeanDifference,
origin = 0,
xlab = "Absolute (Mean With Structure - Mean Without Structure)",
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] LOWESS_PR: Locally Weighted Scatterplot Smoothing Pseudo-R-Squared (caret package)
[A.2] PCC: Pearson’s Correlation Coefficient (stats package)
[A.3] SRCC: Spearman’s Rank Correlation Coefficient (stats package)
[A.4] MIC: Maximal Information Coefficient (minerva package)
[A.5] 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] NumHalogen
[B.5] NumRings
[C] The model-independent feature importance for all factor predictors were evaluated in terms of the following metrics:
[C.1] VP_T: Volcano Plot - T-Test (stats 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] FP065
Code Chunk | Output
##################################
# Consolidating all performance metrics
# for the numeric predictors
##################################
NumericPredictor_Metrics <- cbind(LOWESS_PR_Summary$LOWESS_PR,
PCC_Summary$PCC,
SRCC_Summary$SRCC,
MIC_Summary$MIC,
RV_Summary$RV)
colnames(NumericPredictor_Metrics) <- c("LOWESS_PR",
"PCC",
"SRCC",
"MIC",
"RV")
rownames(NumericPredictor_Metrics) <- names(PMA_PreModelling_Train_Numeric)[2:ncol(PMA_PreModelling_Train_Numeric)]
NumericPredictor_Metrics <- as.data.frame(NumericPredictor_Metrics)
NumericPredictor_Metrics$Group <- ifelse(rownames(NumericPredictor_Metrics)=="MolWeight","MolWeight",
ifelse(rownames(NumericPredictor_Metrics)=="NumCarbon","NumCarbon",
ifelse(rownames(NumericPredictor_Metrics)=="NumMultBonds","NumMultBonds",
ifelse(rownames(NumericPredictor_Metrics)=="NumHalogen","NumHalogen",
ifelse(rownames(NumericPredictor_Metrics)=="NumRings","NumRings",
"Others")))))
NumericPredictor_Metrics## LOWESS_PR PCC SRCC MIC RV
## MolWeight 4.443734e-01 0.658495868 0.68529880 0.4679277 0.10334945
## NumBonds 2.093744e-01 0.457574429 0.54839850 0.3268683 0.03989134
## NumMultBonds 2.758859e-01 0.525248387 0.47971353 0.2792600 0.06807077
## NumRotBonds 2.230342e-02 0.149343282 0.14976036 0.1754215 0.06121663
## NumDblBonds 1.530295e-06 0.001237051 0.02042731 0.1688472 0.02974081
## NumCarbon 3.657997e-01 0.604813811 0.67359114 0.4434121 0.06435924
## NumNitrogen 1.045101e-02 0.102230176 0.10078218 0.1535738 0.11583417
## NumOxygen 1.710199e-02 0.130774566 0.14954994 0.1527421 0.10808031
## NumSulfer 8.357325e-03 0.091418407 0.12090249 0.1297052 0.05065068
## NumChlorine 2.540713e-01 0.504054819 0.35707519 0.2011708 0.02781883
## NumHalogen 2.541532e-01 0.504136055 0.38111965 0.2017841 0.06144457
## NumRings 2.384330e-01 0.488295986 0.50941815 0.3161828 0.05518173
## HydrophilicFactor 1.505216e-01 0.309022159 0.36469127 0.3208456 0.04195617
## SurfaceArea1 1.941711e-01 0.193769382 0.19339720 0.2054896 0.05366733
## SurfaceArea2 2.080820e-01 0.143941883 0.14057885 0.2274047 0.06623467
## Group
## MolWeight MolWeight
## NumBonds Others
## NumMultBonds NumMultBonds
## NumRotBonds Others
## NumDblBonds Others
## NumCarbon NumCarbon
## NumNitrogen Others
## NumOxygen Others
## NumSulfer Others
## NumChlorine Others
## NumHalogen NumHalogen
## NumRings NumRings
## HydrophilicFactor Others
## SurfaceArea1 Others
## SurfaceArea2 Otherssplom(~NumericPredictor_Metrics[,c(1:5)],
groups = NumericPredictor_Metrics$Group,
pch = 16,
cex = 2,
alpha = 0.45,
varnames = c("LOWESS_PR", "PCC", "SRCC", "MIC", "RV"),
auto.key = list(points=TRUE, space="top", columns=2),
main = "Feature Importance Comparison for Numeric Predictors",
xlab = "Scatterplot Matrix of Feature Importance Metrics" )
##################################
# Consolidating all performance metrics
# for the factor predictors
##################################
TTEST_P.Value_Plot <- xyplot(NegativeLog10_t.Test_P.Value ~ MeanDifference,
groups = VP_T_Summary$Group,
data = VP_T_Summary,
xlab = "Mean With Structure - Mean Without Structure",
ylab = "-Log10(T-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 Numeric Predictors")
grid.arrange(TTEST_P.Value_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] Correlation in R: Pearson and Spearman Correlation Matrix by Daniel Johnson
[Article] Correlation (Pearson, Kendall, Spearman) by Statistics Solutions Team
[Article] A Comparison of the Pearson and Spearman Correlation Methods by Minitab Support Team
[Article] How to Perform Lowess Smoothing in R (Step-by-Step) by Statology Team
[Article] Maximal Information Coefficient by R Bloggers 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] Robust Locally Weighted Regression and Smoothing Scatterplots by William Cleveland (Journal of the American Statistical Association)
[Publication] Mathematical Contributions to the Theory of Evolution: Regression, Heredity and Panmixia by Karl Pearson (Royal Society)
[Publication] The Proof and Measurement of Association between Two Things by Charles Spearman (The American Journal of Psychology)
[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)
[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