Mercurial > repos > anmoljh > rcaret_classification_model

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planemo upload commit b5238645b0390bc72071841b6af1eba8fdc24ab1
author anmoljh
date Sat, 26 May 2018 17:49:59 -0400
parents ab806d671e22
children
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1 def __template4Rnw():
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2
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3 template4Rnw = r'''%% Classification Modeling Script
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4 %% Max Kuhn (max.kuhn@pfizer.com, mxkuhn@gmail.com)
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5 %% Version: 1.00
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6 %% Created on: 2010/10/02
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7 %%
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8 %% The originla file hs been improved by
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9 %% Deepak Bharti, Andrew M. Lynn , Anmol J. Hemrom
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10 %% Version : 1.01
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11 %% created on : 2014/08/12
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12 %% This is an Sweave template for building and describing
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13 %% classification models. It mixes R and LaTeX code. The document can
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14 %% be processing using R's Sweave function to produce a tex file.
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15 %%
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16 %% The inputs are:
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17 %% - the initial data set in a data frame called 'rawData'
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18 %% - a factor column in the data set called 'class'. this should be the
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19 %% outcome variable
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20 %% - all other columns in rawData should be predictor variables
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21 %% - the type of model should be in a variable called 'modName'.
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22 %%
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23 %% The script attempts to make some intelligent choices based on the
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24 %% model being used. For example, if modName is "pls", the script will
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25 %% automatically center and scale the predictor data. There are
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26 %% situations where these choices can (and should be) changed.
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27 %%
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28 %% There are other options that may make sense to change. For example,
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29 %% the user may want to adjust the type of resampling. To find these
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30 %% parts of the script, search on the string 'OPTION'. These parts of
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31 %% the code will document the options.
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32
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33 \documentclass[14pt]{report}
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34 \usepackage{amsmath}
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35 \usepackage[pdftex]{graphicx}
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36 \usepackage{color}
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37 \usepackage{ctable}
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38 \usepackage{xspace}
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39 \usepackage{fancyvrb}
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40 \usepackage{fancyhdr}
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41 \usepackage{lastpage}
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42 \usepackage{longtable}
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43 \usepackage{algorithm2e}
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44 \usepackage[
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45 colorlinks=true,
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46 linkcolor=blue,
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47 citecolor=blue,
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48 urlcolor=blue]
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49 {hyperref}
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50 \usepackage{lscape}
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51 \usepackage{Sweave}
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52 \SweaveOpts{keep.source = TRUE}
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53
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54 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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55
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56 % define new colors for use
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57 \definecolor{darkgreen}{rgb}{0,0.6,0}
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58 \definecolor{darkred}{rgb}{0.6,0.0,0}
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59 \definecolor{lightbrown}{rgb}{1,0.9,0.8}
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60 \definecolor{brown}{rgb}{0.6,0.3,0.3}
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61 \definecolor{darkblue}{rgb}{0,0,0.8}
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62 \definecolor{darkmagenta}{rgb}{0.5,0,0.5}
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63
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64 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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65
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66 \newcommand{\bld}[1]{\mbox{\boldmath $$#1$$}}
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67 \newcommand{\shell}[1]{\mbox{$$#1$$}}
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68 \renewcommand{\vec}[1]{\mbox{\bf {#1}}}
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69
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70 \newcommand{\ReallySmallSpacing}{\renewcommand{\baselinestretch}{.6}\Large\normalsize}
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71 \newcommand{\SmallSpacing}{\renewcommand{\baselinestretch}{1.1}\Large\normalsize}
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72
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73 \newcommand{\halfs}{\frac{1}{2}}
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74
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75 \setlength{\oddsidemargin}{-.25 truein}
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76 \setlength{\evensidemargin}{0truein}
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77 \setlength{\topmargin}{-0.2truein}
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78 \setlength{\textwidth}{7 truein}
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79 \setlength{\textheight}{8.5 truein}
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80 \setlength{\parindent}{0.20truein}
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81 \setlength{\parskip}{0.10truein}
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82
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83 \setcounter{LTchunksize}{50}
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84
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85 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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86 \pagestyle{fancy}
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87 \lhead{}
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88 %% OPTION Report header name
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89 \chead{Classification Model Script}
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90 \rhead{}
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91 \lfoot{}
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92 \cfoot{}
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93 \rfoot{\thepage\ of \pageref{LastPage}}
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94 \renewcommand{\headrulewidth}{1pt}
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95 \renewcommand{\footrulewidth}{1pt}
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96 %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
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97
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98 %% OPTION Report title and modeler name
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99 \title{Classification Model Script using $METHOD}
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100 \author{"Lynn Group with M. Kuhn, SCIS, JNU, New Delhi"}
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101
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102 \begin{document}
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103
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104 \maketitle
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105
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106 \thispagestyle{empty}
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107 <<dummy, eval=TRUE, echo=FALSE, results=hide>>=
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108 # sets values for variables used later in the program to prevent the \Sexpr error on parsing with Sweave
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109 numSamples=''
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110 classDistString=''
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111 missingText=''
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112 numPredictors=''
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113 numPCAcomp=''
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114 pcaText=''
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115 nzvText=''
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116 corrText=''
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117 ppText=''
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118 varText=''
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119 splitText="Dummy Text"
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120 nirText="Dummy Text"
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121 # pctTrain is a variable that is initialised in Data splitting, and reused later in testPred
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122 pctTrain=0.8
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123 Smpling=''
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124 nzvText1=''
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125 classDistString1=''
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126 dwnsmpl=''
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127 upsmpl=''
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128
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129 @
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130 <<startup, eval= TRUE, results = hide, echo = FALSE>>=
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131 library(Hmisc)
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132 library(caret)
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133 library(pROC)
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134 versionTest <- compareVersion(packageDescription("caret")$$Version,
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135 "4.65")
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136 if(versionTest < 0) stop("caret version 4.65 or later is required")
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137
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138 library(RColorBrewer)
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139
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140
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141 listString <- function (x, period = FALSE, verbose = FALSE)
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142 {
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143 if (verbose) cat("\n entering listString\n")
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144 flush.console()
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145 if (!is.character(x))
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146 x <- as.character(x)
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147 numElements <- length(x)
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148 out <- if (length(x) > 0) {
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149 switch(min(numElements, 3), x, paste(x, collapse = " and "),
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150 {
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151 x <- paste(x, c(rep(",", numElements - 2), " and", ""), sep = "")
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152 paste(x, collapse = " ")
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153 })
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154 }
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155 else ""
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156 if (period) out <- paste(out, ".", sep = "")
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157 if (verbose) cat(" leaving listString\n\n")
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158 flush.console()
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159 out
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160 }
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161
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162 resampleStats <- function(x, digits = 3)
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163 {
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164 bestPerf <- x$$bestTune
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165 colnames(bestPerf) <- gsub("^\\.", "", colnames(bestPerf))
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166 out <- merge(x$$results, bestPerf)
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167 out <- out[, colnames(out) %in% x$$perfNames]
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168 names(out) <- gsub("ROC", "area under the ROC curve", names(out), fixed = TRUE)
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169 names(out) <- gsub("Sens", "sensitivity", names(out), fixed = TRUE)
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170 names(out) <- gsub("Spec", "specificity", names(out), fixed = TRUE)
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171 names(out) <- gsub("Accuracy", "overall accuracy", names(out), fixed = TRUE)
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172 names(out) <- gsub("Kappa", "Kappa statistics", names(out), fixed = TRUE)
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173
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174 out <- format(out, digits = digits)
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175 listString(paste(names(out), "was", out))
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176 }
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177
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178 twoClassNoProbs <- function (data, lev = NULL, model = NULL)
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179 {
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180 out <- c(sensitivity(data[, "pred"], data[, "obs"], lev[1]),
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181 specificity(data[, "pred"], data[, "obs"], lev[2]),
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182 confusionMatrix(data[, "pred"], data[, "obs"])$$overall["Kappa"])
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183
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184 names(out) <- c("Sens", "Spec", "Kappa")
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185 out
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186 }
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187
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188
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189
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190 ##OPTION: model name: see ?train for more values/models
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191 modName <- "$METHOD"
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192
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193
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194 load("$RDATA")
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195 rawData <- dataX
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196 rawData$$outcome <- dataY
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197
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198 @
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199
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200
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201 \section*{Data Sets}\label{S:data}
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202
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203 %% OPTION: provide some background on the problem, the experimental
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204 %% data, how the compounds were selected etc
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205
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206 <<getDataInfo, eval = $GETDATAINFOEVAL, echo = $GETDATAINFOECHO, results = $GETDATAINFORESULT>>=
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207 if(!any(names(rawData) == "outcome")) stop("a variable called outcome should be in the data set")
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208 if(!is.factor(rawData$$outcome)) stop("the outcome should be a factor vector")
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209
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210 ## OPTION: when there are only two classes, the first level of the
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211 ## factor is used as the "positive" or "event" for calculating
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212 ## sensitivity and specificity. Adjust the outcome factor accordingly.
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213 numClasses <- length(levels(rawData$$outcome))
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214 numSamples <- nrow(rawData)
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215 numPredictors <- ncol(rawData) - 1
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216 predictorNames <- names(rawData)[names(rawData) != "outcome"]
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217
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218 isNum <- apply(rawData[,predictorNames, drop = FALSE], 2, is.numeric)
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219 if(any(!isNum)) stop("all predictors in rawData should be numeric")
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220
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221 classTextCheck <- all.equal(levels(rawData$$outcome), make.names(levels(rawData$$outcome)))
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222 if(!classTextCheck) warning("the class levels are not valid R variable names; this may cause errors")
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223
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224 ## Get the class distribution
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225 classDist <- table(rawData$$outcome)
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226 classDistString <- paste("``",
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227 names(classDist),
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228 "'' ($$n$$=",
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229 classDist,
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230 ")",
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231 sep = "")
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232 classDistString <- listString(classDistString)
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233 @
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234
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235 <<missingFilter, eval = $MISSINGFILTEREVAL, echo = $MISSINGFILTERECHO, results = $MISSINGFILTERRESULT>>=
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236 colRate <- apply(rawData[, predictorNames, drop = FALSE],
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237 2, function(x) mean(is.na(x)))
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238
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239 ##OPTION thresholds can be changed
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240 colExclude <- colRate > $MISSINGFILTERTHRESHC
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241
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242 missingText <- ""
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243
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244 if(any(colExclude))
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245 {
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246 missingText <- paste(missingText,
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247 ifelse(sum(colExclude) > 1,
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248 " There were ",
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249 " There was "),
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250 sum(colExclude),
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251 ifelse(sum(colExclude) > 1,
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252 " predictors ",
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253 " predictor "),
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254 "with an excessive number of ",
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255 "missing data. ",
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256 ifelse(sum(colExclude) > 1,
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257 " These were excluded. ",
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258 " This was excluded. "))
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259 predictorNames <- predictorNames[!colExclude]
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260 rawData <- rawData[, names(rawData) %in% c("outcome", predictorNames), drop = FALSE]
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261 }
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262
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263
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264 rowRate <- apply(rawData[, predictorNames, drop = FALSE],
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265 1, function(x) mean(is.na(x)))
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266
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267 rowExclude <- rowRate > $MISSINGFILTERTHRESHR
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268
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269
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270 if(any(rowExclude)) {
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271 missingText <- paste(missingText,
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272 ifelse(sum(rowExclude) > 1,
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273 " There were ",
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274 " There was "),
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275 sum(colExclude),
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276 ifelse(sum(rowExclude) > 1,
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277 " samples ",
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278 " sample "),
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279 "with an excessive number of ",
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280 "missing data. ",
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281 ifelse(sum(rowExclude) > 1,
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282 " These were excluded. ",
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283 " This was excluded. "),
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284 "After filtering, ",
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285 sum(!rowExclude),
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286 " samples remained.")
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287 rawData <- rawData[!rowExclude, ]
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288 hasMissing <- apply(rawData[, predictorNames, drop = FALSE],
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289 1, function(x) mean(is.na(x)))
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290 } else {
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291 hasMissing <- apply(rawData[, predictorNames, drop = FALSE],
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292 1, function(x) any(is.na(x)))
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293 missingText <- paste(missingText,
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294 ifelse(missingText == "",
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295 "There ",
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296 "Subsequently, there "),
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297 ifelse(sum(hasMissing) == 1,
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298 "was ",
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299 "were "),
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300 ifelse(sum(hasMissing) > 0,
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301 sum(hasMissing),
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302 "no"),
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303 ifelse(sum(hasMissing) == 1,
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304 "sample ",
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305 "samples "),
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306 "with missing values.")
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307
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308 rawData <- rawData[complete.cases(rawData),]
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309
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310 }
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311
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312 rawData1 <- rawData[,1:length(rawData)-1]
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313 rawData2 <- rawData[,length(rawData)]
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314
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315 set.seed(222)
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316 nzv1 <- nearZeroVar(rawData1)
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317 if(length(nzv1) > 0)
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318 {
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319 nzvVars1 <- names(rawData1)[nzv1]
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320 rawData <- rawData1[, -nzv1]
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321 rawData$outcome <- rawData2
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322 nzvText1 <- paste("There were ",
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323 length(nzv1),
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324 " predictors that were removed from original data due to",
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325 " severely unbalanced distributions that",
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326 " could negatively affect the model fit",
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327 ifelse(length(nzv1) > 10,
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328 ".",
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329 paste(": ",
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330 listString(nzvVars1),
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331 ".",
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332 sep = "")),
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333 sep = "")
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334
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335 } else {
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336 rawData <- rawData1
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337 rawData$outcome <- rawData2
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338 nzvText1 <- ""
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339
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340 }
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341
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342 remove("rawData1")
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343 remove("rawData2")
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344
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345 @
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346
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347 The initial data set consisted of \Sexpr{numSamples} samples and
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348 \Sexpr{numPredictors} predictor variables. The breakdown of the
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349 outcome data classes were: \Sexpr{classDistString}.
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350
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351 \Sexpr{missingText}
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352
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353 \Sexpr{nzvText1}
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354
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355 <<pca, eval= $PCAEVAL, echo = $PCAECHO, results = $PCARESULT>>=
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356
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357 predictorNames <- names(rawData)[names(rawData) != "outcome"]
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358 numPredictors <- length(predictorNames)
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359 predictors <- rawData[, predictorNames, drop = FALSE]
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360 ## PCA will fail with predictors having less than 2 unique values
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361 isZeroVar <- apply(predictors, 2,
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362 function(x) length(unique(x)) < 2)
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363 if(any(isZeroVar)) predictors <- predictors[, !isZeroVar, drop = FALSE]
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364 ## For whatever, only the formula interface to prcomp
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365 ## handles missing values
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366 pcaForm <- as.formula(
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367 paste("~",
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368 paste(names(predictors), collapse = "+")))
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369 pca <- prcomp(pcaForm,
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370 data = predictors,
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371 center = TRUE,
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372 scale. = TRUE,
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373 na.action = na.omit)
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374 ## OPTION: the number of components plotted/discussed can be set
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375 numPCAcomp <- $PCACOMP
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376 pctVar <- pca$$sdev^2/sum(pca$$sdev^2)*100
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377 pcaText <- paste(round(pctVar[1:numPCAcomp], 1),
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378 "\\\\%",
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379 sep = "")
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380 pcaText <- listString(pcaText)
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381 @
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382
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383 To get an initial assessment of the separability of the classes,
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384 principal component analysis (PCA) was used to distill the
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385 \Sexpr{numPredictors} predictors down into \Sexpr{numPCAcomp}
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386 surrogate variables (i.e. the principal components) in a manner that
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387 attempts to maximize the amount of information preserved from the
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388 original predictor set. Figure \ref{F:inititalPCA} contains plots of
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389 the first \Sexpr{numPCAcomp} components, which accounted for
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390 \Sexpr{pcaText} percent of the variability in the original predictors
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391 (respectively).
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392
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393
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394 %% OPTION: remark on how well (or poorly) the data separated
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395
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396 \setkeys{Gin}{width = 0.8\textwidth}
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397 \begin{figure}[p]
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398 \begin{center}
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399
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400 <<pcaPlot, eval = $PCAPLOTEVAL, echo = $PCAPLOTECHO, results = $PCAPLOTRESULT, fig = $PCAPLOTFIG, width = 8, height = 8>>=
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401 trellis.par.set(caretTheme(), warn = TRUE)
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402 if(numPCAcomp == 2)
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403 {
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404 axisRange <- extendrange(pca$$x[, 1:2])
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405 print(
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406 xyplot(PC1 ~ PC2,
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407 data = as.data.frame(pca$$x),
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408 type = c("p", "g"),
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409 groups = rawData$$outcome,
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410 auto.key = list(columns = 2),
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411 xlim = axisRange,
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412 ylim = axisRange))
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413 } else {
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414 axisRange <- extendrange(pca$$x[, 1:numPCAcomp])
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415 print(
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416 splom(~as.data.frame(pca$$x)[, 1:numPCAcomp],
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417 type = c("p", "g"),
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418 groups = rawData$$outcome,
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419 auto.key = list(columns = 2),
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420 as.table = TRUE,
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421 prepanel.limits = function(x) axisRange
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422 ))
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423
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424 }
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425
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426 @
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427
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428 \caption[PCA Plot]{A plot of the first \Sexpr{numPCAcomp}
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429 principal components for the original data set.}
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430 \label{F:inititalPCA}
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431 \end{center}
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432 \end{figure}
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433
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434
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435
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436 <<initialDataSplit, eval = $INITIALDATASPLITEVAL, echo = $INITIALDATASPLITECHO, results = $INITIALDATASPLITRESULT>>=
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437
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438 ## OPTION: in small samples sizes, you may not want to set aside a
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439 ## training set and focus on the resampling results.
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440
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441 set.seed(1234)
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442 dataX <- rawData[,1:length(rawData)-1]
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443 dataY <- rawData[,length(rawData)]
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444
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445 Smpling <- "$SAAMPLING"
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446
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447 if(Smpling=="downsampling")
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448 {
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449 dwnsmpl <- downSample(dataX,dataY)
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450 rawData <- dwnsmpl[,1:length(dwnsmpl)-1]
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451 rawData$outcome <- dwnsmpl[,length(dwnsmpl)]
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452 remove("dwnsmpl")
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453 remove("dataX")
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454 remove("dataY")
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455 }else if(Smpling=="upsampling"){
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456 upsmpl <- upSample(dataX,dataY)
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457 rawData <- upsmpl[,1:length(upsmpl)-1]
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458 rawData$outcome <- upsmpl[,length(upsmpl)]
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459 remove("upsmpl")
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460 remove("dataX")
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461 remove("dataY")
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462 }else{remove("dataX")
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463 remove("dataY")
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464 }
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465
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466
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467
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468 numSamples <- nrow(rawData)
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469
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470 predictorNames <- names(rawData)[names(rawData) != "outcome"]
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471 numPredictors <- length(predictorNames)
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472
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473
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474 classDist1 <- table(rawData$outcome)
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475 classDistString1 <- paste("``",
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476 names(classDist1),
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477 "'' ($n$=",
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478 classDist1,
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479 ")",
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480 sep = "")
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481 classDistString1 <- listString(classDistString1)
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482
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483 pctTrain <- $PERCENT
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484
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485 if(pctTrain < 1)
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486 {
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487 ## OPTION: seed number can be changed
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488 set.seed(1)
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489 inTrain <- createDataPartition(rawData$$outcome,
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490 p = pctTrain,
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491 list = FALSE)
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492 trainX <- rawData[ inTrain, predictorNames]
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493 testX <- rawData[-inTrain, predictorNames]
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494 trainY <- rawData[ inTrain, "outcome"]
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495 testY <- rawData[-inTrain, "outcome"]
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496 splitText <- paste("The original data were split into ",
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497 "a training set ($$n$$=",
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498 nrow(trainX),
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499 ") and a test set ($$n$$=",
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500 nrow(testX),
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501 ") in a manner that preserved the ",
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502 "distribution of the classes.",
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503 sep = "")
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504 isZeroVar <- apply(trainX, 2,
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505 function(x) length(unique(x)) < 2)
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parents:
diff changeset
506 if(any(isZeroVar))
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diff changeset
507 {
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508 trainX <- trainX[, !isZeroVar, drop = FALSE]
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parents:
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509 testX <- testX[, !isZeroVar, drop = FALSE]
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parents:
diff changeset
510 }
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511
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diff changeset
512 } else {
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513 trainX <- rawData[, predictorNames]
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parents:
diff changeset
514 testX <- NULL
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parents:
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515 trainY <- rawData[, "outcome"]
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parents:
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516 testY <- NULL
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517 splitText <- "The entire data set was used as the training set."
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parents:
diff changeset
518 }
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parents:
diff changeset
519 trainDist <- table(trainY)
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parents:
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520 nir <- max(trainDist)/length(trainY)*100
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parents:
diff changeset
521 niClass <- names(trainDist)[which.max(trainDist)]
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parents:
diff changeset
522 nirText <- paste("The non--information rate is the accuracy that can be ",
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parents:
diff changeset
523 "achieved by predicting all samples using the most ",
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parents:
diff changeset
524 "dominant class. For these data, the rate is ",
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parents:
diff changeset
525 round(nir, 2), "\\\\% using the ``",
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parents:
diff changeset
526 niClass,
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parents:
diff changeset
527 "'' class.",
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parents:
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528 sep = "")
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529
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parents:
diff changeset
530 remove("rawData")
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parents:
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531
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parents:
diff changeset
532 if((!is.null(testX)) && (!is.null(testY))){
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parents:
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533 #save(trainX,trainY,testX,testY,file="datasets.RData")
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parents:
diff changeset
534 } else {
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parents:
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535 save(trainX,trainY,file="datasets.RData")
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parents:
diff changeset
536 }
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parents:
diff changeset
537
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parents:
diff changeset
538 @
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parents:
diff changeset
539
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parents:
diff changeset
540 \Sexpr{splitText}
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parents:
diff changeset
541
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diff changeset
542 \Sexpr{nirText}
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parents:
diff changeset
543
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parents:
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544 The data set for model building consisted of \Sexpr{numSamples} samples and
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parents:
diff changeset
545 \Sexpr{numPredictors} predictor variables. The breakdown of the
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parents:
diff changeset
546 outcome data classes were: \Sexpr{classDistString1}.
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parents:
diff changeset
547
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parents:
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548 <<nzv, eval= $NZVEVAL, results = $NZVRESULT, echo = $NZVECHO>>=
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parents:
diff changeset
549 ## OPTION: other pre-processing steps can be used
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anmoljh
parents:
diff changeset
550 ppSteps <- caret:::suggestions(modName)
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parents:
diff changeset
551
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parents:
diff changeset
552 set.seed(2)
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parents:
diff changeset
553 if(ppSteps["nzv"])
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parents:
diff changeset
554 {
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parents:
diff changeset
555 nzv <- nearZeroVar(trainX)
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parents:
diff changeset
556 if(length(nzv) > 0)
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557 {
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558 nzvVars <- names(trainX)[nzv]
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559 trainX <- trainX[, -nzv]
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parents:
diff changeset
560 nzvText <- paste("There were ",
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parents:
diff changeset
561 length(nzv),
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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diff changeset
562 " predictors that were removed from train set due to",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
563 " severely unbalanced distributions that",
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parents:
diff changeset
564 " could negatively affect the model",
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parents:
diff changeset
565 ifelse(length(nzv) > 10,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
566 ".",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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diff changeset
567 paste(": ",
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parents:
diff changeset
568 listString(nzvVars),
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parents:
diff changeset
569 ".",
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anmoljh
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570 sep = "")),
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571 sep = "")
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572 testX <- testX[, -nzv]
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
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573 } else nzvText <- ""
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
574 } else nzvText <- ""
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parents:
diff changeset
575 @
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diff changeset
576
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diff changeset
577 \Sexpr{nzvText}
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diff changeset
578
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diff changeset
579
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580 <<corrFilter, eval = $CORRFILTEREVAL, results = $CORRFILTERRESULT, echo = $CORRFILTERECHO>>=
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parents:
diff changeset
581 if(ppSteps["corr"])
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parents:
diff changeset
582 {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
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583 ## OPTION:
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parents:
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584 corrThresh <- $THRESHHOLDCOR
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parents:
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585 highCorr <- findCorrelation(cor(trainX, use = "pairwise.complete.obs"),
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parents:
diff changeset
586 corrThresh)
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diff changeset
587 if(length(highCorr) > 0)
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parents:
diff changeset
588 {
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diff changeset
589 corrVars <- names(trainX)[highCorr]
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parents:
diff changeset
590 trainX <- trainX[, -highCorr]
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parents:
diff changeset
591 corrText <- paste("There were ",
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parents:
diff changeset
592 length(highCorr),
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anmoljh
parents:
diff changeset
593 " predictors that were removed due to",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
594 " large between--predictor correlations that",
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parents:
diff changeset
595 " could negatively affect the model fit",
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596 ifelse(length(highCorr) > 10,
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parents:
diff changeset
597 ".",
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anmoljh
parents:
diff changeset
598 paste(": ",
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parents:
diff changeset
599 listString(highCorr),
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parents:
diff changeset
600 ".",
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diff changeset
601 sep = "")),
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diff changeset
602 " Removing these predictors forced",
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parents:
diff changeset
603 " all pair--wise correlations to be",
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parents:
diff changeset
604 " less than ",
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parents:
diff changeset
605 corrThresh,
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parents:
diff changeset
606 ".",
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parents:
diff changeset
607 sep = "")
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608 testX <- testX[, -highCorr]
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parents:
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609 } else corrText <- "No correlation among data on given threshold"
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parents:
diff changeset
610 }else corrText <- ""
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parents:
diff changeset
611 @
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parents:
diff changeset
612
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parents:
diff changeset
613 \Sexpr{corrText}
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614
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615 <<preProc, eval = $PREPROCEVAL, echo = $PREPROCECHO, results = $PREPROCRESULT>>=
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616 ppMethods <- NULL
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617 if(ppSteps["center"]) ppMethods <- c(ppMethods, "center")
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parents:
diff changeset
618 if(ppSteps["scale"]) ppMethods <- c(ppMethods, "scale")
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parents:
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619 if(any(hasMissing) > 0) ppMethods <- c(ppMethods, "knnImpute")
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620 ##OPTION other methods, such as spatial sign, can be added to this list
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621
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622 if(length(ppMethods) > 0)
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623 {
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624 ppInfo <- preProcess(trainX, method = ppMethods)
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625 trainX <- predict(ppInfo, trainX)
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626 if(pctTrain < 1) testX <- predict(ppInfo, testX)
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627 ppText <- paste("The following pre--processing methods were",
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parents:
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628 " applied to the training",
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629 ifelse(pctTrain < 1, " and test", ""),
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
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630 " data: ",
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631 listString(ppMethods),
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632 ".",
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633 sep = "")
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634 ppText <- gsub("center", "mean centering", ppText)
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635 ppText <- gsub("scale", "scaling to unit variance", ppText)
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636 ppText <- gsub("knnImpute",
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637 paste(ppInfo$$k, "--nearest neighbor imputation", sep = ""),
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638 ppText)
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639 ppText <- gsub("spatialSign", "the spatial sign transformation", ppText)
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640 ppText <- gsub("pca", "principal component feature extraction", ppText)
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641 ppText <- gsub("ica", "independent component feature extraction", ppText)
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642 } else {
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643 ppInfo <- NULL
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644 ppText <- ""
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645 }
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646
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647 predictorNames <- names(trainX)
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648 if(nzvText != "" | corrText != "" | ppText != "")
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649 {
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650 varText <- paste("After pre--processing, ",
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651 ncol(trainX),
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652 "predictors remained for modeling.")
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653 } else varText <- ""
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654
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655 @
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656
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657 \Sexpr{ppText}
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658 \Sexpr{varText}
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659
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660 \clearpage
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661
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662 \section*{Model Building}
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663
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664 <<setupWorkers, eval = TRUE, echo = $SETUPWORKERSECHO, results = $SETUPWORKERSRESULT>>=
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665 numWorkers <- $NUMWORKERS
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666 ##OPTION: turn up numWorkers to use MPI
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667 if(numWorkers > 1)
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668 {
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669 mpiCalcs <- function(X, FUN, ...)
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670 {
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671 theDots <- list(...)
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672 parLapply(theDots$$cl, X, FUN)
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673 }
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674
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675 library(snow)
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676 cl <- makeCluster(numWorkers, "MPI")
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677 }
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678 @
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679
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680 <<setupResampling, echo = $SETUPRESAMPLINGECHO, results = $SETUPRESAMPLINGRESULT>>=
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diff changeset
681 ##OPTION: the resampling options can be changed. See
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diff changeset
682 ## ?trainControl for details
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683
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684 resampName <- "$RESAMPNAME"
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685 resampNumber <- $RESAMPLENUMBER
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686 numRepeat <- $NUMREPEAT
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parents:
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687 resampP <- $RESAMPLENUMBERPERCENT
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688
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689 modelInfo <- modelLookup(modName)
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690
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691 if(numClasses == 2)
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692 {
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693 foo <- if(any(modelInfo$$probModel)) twoClassSummary else twoClassNoProbs
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694 } else foo <- defaultSummary
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695
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696 set.seed(3)
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697 ctlObj <- trainControl(method = resampName,
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698 number = resampNumber,
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diff changeset
699 repeats = numRepeat,
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700 p = resampP,
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diff changeset
701 classProbs = any(modelInfo$$probModel),
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parents:
diff changeset
702 summaryFunction = foo)
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703
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704
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diff changeset
705 ##OPTION select other performance metrics as needed
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706 optMetric <- if(numClasses == 2 & any(modelInfo$$probModel)) "ROC" else "Kappa"
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parents:
diff changeset
707
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708 if(numWorkers > 1)
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parents:
diff changeset
709 {
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710 ctlObj$$workers <- numWorkers
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parents:
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711 ctlObj$$computeFunction <- mpiCalcs
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712 ctlObj$$computeArgs <- list(cl = cl)
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713 }
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714 @
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parents:
diff changeset
715
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parents:
diff changeset
716 <<setupGrid, results = $SETUPGRIDRESULT, echo = $SETUPGRIDECHO>>=
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diff changeset
717 ##OPTION expand or contract these grids as needed (or
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parents:
diff changeset
718 ## add more models
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diff changeset
719
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720 gridSize <- $SETUPGRIDSIZE
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721
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722 if(modName %in% c("svmPoly", "svmRadial", "svmLinear", "lvq", "ctree2", "ctree")) gridSize <- 5
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parents:
diff changeset
723 if(modName %in% c("earth", "fda")) gridSize <- 7
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parents:
diff changeset
724 if(modName %in% c("knn", "rocc", "glmboost", "rf", "nodeHarvest")) gridSize <- 10
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parents:
diff changeset
725
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parents:
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726 if(modName %in% c("nb")) gridSize <- 2
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parents:
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727 if(modName %in% c("pam", "rpart")) gridSize <- 15
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728 if(modName %in% c("pls")) gridSize <- min(20, ncol(trainX))
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729
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730 if(modName == "gbm")
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731 {
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732 tGrid <- expand.grid(.interaction.depth = -1 + (1:5)*2 ,
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733 .n.trees = (1:10)*20,
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parents:
diff changeset
734 .shrinkage = .1)
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735 }
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736
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737 if(modName == "nnet")
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738 {
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739 tGrid <- expand.grid(.size = -1 + (1:5)*2 ,
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740 .decay = c(0, .001, .01, .1))
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parents:
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741 }
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742
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743 if(modName == "ada")
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744 {
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745 tGrid <- expand.grid(.maxdepth = 1, .iter = c(100,200,300,400), .nu = 1 )
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746
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747 }
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748
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749
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750 @
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751
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752 <<fitModel, results = $FITMODELRESULT, echo = $FITMODELECHO, eval = $FITMODELEVAL>>=
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753 ##OPTION alter as needed
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754
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755 set.seed(4)
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756 modelFit <- switch(modName,
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757 gbm =
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758 {
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diff changeset
759 mix <- sample(seq(along = trainY))
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parents:
diff changeset
760 train(
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parents:
diff changeset
761 trainX[mix,], trainY[mix], modName,
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parents:
diff changeset
762 verbose = FALSE,
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parents:
diff changeset
763 bag.fraction = .9,
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parents:
diff changeset
764 metric = optMetric,
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parents:
diff changeset
765 trControl = ctlObj,
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parents:
diff changeset
766 tuneGrid = tGrid)
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parents:
diff changeset
767 },
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parents:
diff changeset
768
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parents:
diff changeset
769 multinom =
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parents:
diff changeset
770 {
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parents:
diff changeset
771 train(
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parents:
diff changeset
772 trainX, trainY, modName,
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parents:
diff changeset
773 trace = FALSE,
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parents:
diff changeset
774 metric = optMetric,
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parents:
diff changeset
775 maxiter = 1000,
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parents:
diff changeset
776 MaxNWts = 5000,
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parents:
diff changeset
777 trControl = ctlObj,
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parents:
diff changeset
778 tuneLength = gridSize)
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parents:
diff changeset
779 },
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parents:
diff changeset
780
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parents:
diff changeset
781 nnet =
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diff changeset
782 {
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parents:
diff changeset
783 train(
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parents:
diff changeset
784 trainX, trainY, modName,
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parents:
diff changeset
785 metric = optMetric,
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diff changeset
786 linout = FALSE,
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parents:
diff changeset
787 trace = FALSE,
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parents:
diff changeset
788 maxiter = 1000,
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diff changeset
789 MaxNWts = 5000,
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parents:
diff changeset
790 trControl = ctlObj,
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parents:
diff changeset
791 tuneGrid = tGrid)
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parents:
diff changeset
792
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diff changeset
793 },
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diff changeset
794
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parents:
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795 svmRadial =, svmPoly =, svmLinear =
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parents:
diff changeset
796 {
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parents:
diff changeset
797 train(
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parents:
diff changeset
798 trainX, trainY, modName,
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parents:
diff changeset
799 metric = optMetric,
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parents:
diff changeset
800 scaled = TRUE,
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parents:
diff changeset
801 trControl = ctlObj,
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parents:
diff changeset
802 tuneLength = gridSize)
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parents:
diff changeset
803 },
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parents:
diff changeset
804 {
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parents:
diff changeset
805 train(trainX, trainY, modName,
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parents:
diff changeset
806 trControl = ctlObj,
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parents:
diff changeset
807 metric = optMetric,
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parents:
diff changeset
808 tuneLength = gridSize)
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parents:
diff changeset
809 })
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parents:
diff changeset
810
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parents:
diff changeset
811 @
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diff changeset
812
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813 <<modelDescr, echo = $MODELDESCRECHO, results = $MODELDESCRRESULT>>=
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814 summaryText <- ""
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815
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816 resampleName <- switch(tolower(modelFit$$control$$method),
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diff changeset
817 boot = paste("the bootstrap (", length(modelFit$$control$$index), " reps)", sep = ""),
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diff changeset
818 boot632 = paste("the bootstrap 632 rule (", length(modelFit$$control$$index), " reps)", sep = ""),
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diff changeset
819 cv = paste("cross-validation (", modelFit$$control$$number, " fold)", sep = ""),
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diff changeset
820 repeatedcv = paste("cross-validation (", modelFit$$control$$number, " fold, repeated ",
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diff changeset
821 modelFit$$control$$repeats, " times)", sep = ""),
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diff changeset
822 lgocv = paste("repeated train/test splits (", length(modelFit$$control$$index), " reps, ",
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diff changeset
823 round(modelFit$$control$$p, 2), "$$\\%$$)", sep = ""))
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diff changeset
824
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825 tuneVars <- latexTranslate(tolower(modelInfo$$label))
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diff changeset
826 tuneVars <- gsub("\\#", "the number of ", tuneVars, fixed = TRUE)
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827 if(ncol(modelFit$$bestTune) == 1 && colnames(modelFit$$bestTune) == ".parameter")
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diff changeset
828 {
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diff changeset
829 summaryText <- paste(summaryText,
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diff changeset
830 "\n\n",
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diff changeset
831 "There are no tuning parameters associated with this model.",
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diff changeset
832 "To characterize the model performance on the training set,",
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parents:
diff changeset
833 resampleName,
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parents:
diff changeset
834 "was used.",
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diff changeset
835 "Table \\\\ref{T:resamps} and Figure \\\\ref{F:profile}",
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parents:
diff changeset
836 "show summaries of the resampling results. ")
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diff changeset
837
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838 } else {
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diff changeset
839 summaryText <- paste("There",
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parents:
diff changeset
840 ifelse(nrow(modelInfo) > 1, "are", "is"),
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parents:
diff changeset
841 nrow(modelInfo),
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parents:
diff changeset
842 ifelse(nrow(modelInfo) > 1, "tuning parameters", "tuning parameter"),
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parents:
diff changeset
843 "associated with this model:",
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parents:
diff changeset
844 listString(tuneVars, period = TRUE))
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parents:
diff changeset
845
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diff changeset
846
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diff changeset
847
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diff changeset
848 paramNames <- gsub(".", "", names(modelFit$$bestTune), fixed = TRUE)
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diff changeset
849 ## (i in seq(along = paramNames))
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parents:
diff changeset
850 ## {
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diff changeset
851 ## check <- modelInfo$$parameter %in% paramNames[i]
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parents:
diff changeset
852 ## if(any(check))
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parents:
diff changeset
853 ## {
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parents:
diff changeset
854 ## paramNames[i] <- modelInfo$$label[which(check)]
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parents:
diff changeset
855 ## }
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parents:
diff changeset
856 ## }
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diff changeset
857
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diff changeset
858 paramNames <- gsub("#", "the number of ", paramNames, fixed = TRUE)
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parents:
diff changeset
859 ## Check to see if there was only one combination fit
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parents:
diff changeset
860 summaryText <- paste(summaryText,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
861 "To choose",
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diff changeset
862 ifelse(nrow(modelInfo) > 1,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
863 "appropriate values of the tuning parameters,",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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diff changeset
864 "an appropriate value of the tuning parameter,"),
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parents:
diff changeset
865 resampleName,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
866 "was used to generated a profile of performance across the",
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diff changeset
867 nrow(modelFit$$results),
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diff changeset
868 ifelse(nrow(modelInfo) > 1,
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diff changeset
869 "combinations of the tuning parameters.",
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parents:
diff changeset
870 "candidate values."),
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parents:
diff changeset
871
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diff changeset
872 "Table \\\\ref{T:resamps} and Figure \\\\ref{F:profile} show",
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parents:
diff changeset
873 "summaries of the resampling profile. ", "The final model fitted to the entire training set was:",
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diff changeset
874 listString(paste(latexTranslate(tolower(paramNames)), "=", modelFit$$bestTune[1,]), period = TRUE))
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parents:
diff changeset
875
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parents:
diff changeset
876 }
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parents:
diff changeset
877 @
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parents:
diff changeset
878
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parents:
diff changeset
879 \Sexpr{summaryText}
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parents:
diff changeset
880
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diff changeset
881 <<resampTable, echo = $RESAMPTABLEECHO, results = $RESAMPTABLERESULT>>=
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parents:
diff changeset
882 tableData <- modelFit$$results
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parents:
diff changeset
883
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diff changeset
884 if(all(modelInfo$$parameter == "parameter") && resampName == "boot632")
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parents:
diff changeset
885 {
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parents:
diff changeset
886 tableData <- tableData[,-1, drop = FALSE]
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parents:
diff changeset
887 colNums <- c( length(modelFit$$perfNames), length(modelFit$$perfNames), length(modelFit$$perfNames))
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parents:
diff changeset
888 colLabels <- c("Mean", "Standard Deviation","Apparant")
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parents:
diff changeset
889 constString <- ""
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parents:
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890 isConst <- NULL
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parents:
diff changeset
891 } else if (all(modelInfo$$parameter == "parameter") && (resampName == "boot" | resampName == "cv" | resampName == "repeatedcv" )){
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parents:
diff changeset
892 tableData <- tableData[,-1, drop = FALSE]
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parents:
diff changeset
893 colNums <- c(length(modelFit$$perfNames), length(modelFit$$perfNames))
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anmoljh
parents:
diff changeset
894 colLabels <- c("Mean", "Standard Deviation")
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parents:
diff changeset
895 constString <- ""
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parents:
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896 isConst <- NULL
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parents:
diff changeset
897 } else if (all(modelInfo$$parameter == "parameter") && resampName == "LOOCV" ){
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parents:
diff changeset
898 tableData <- tableData[,-1, drop = FALSE]
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anmoljh
parents:
diff changeset
899 colNums <- length(modelFit$$perfNames)
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parents:
diff changeset
900 colLabels <- c("Measures")
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parents:
diff changeset
901 constString <- ""
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parents:
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902 isConst <- NULL
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parents:
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903 } else {
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parents:
diff changeset
904 if (all(modelInfo$$parameter != "parameter") && resampName == "boot632" ){
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anmoljh
parents:
diff changeset
905 isConst <- apply(tableData[, modelInfo$$parameter, drop = FALSE],
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anmoljh
parents:
diff changeset
906 2,
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parents:
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907 function(x) length(unique(x)) == 1)
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anmoljh
parents:
diff changeset
908
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parents:
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909 numParamInTable <- sum(!isConst)
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anmoljh
parents:
diff changeset
910
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anmoljh
parents:
diff changeset
911 if(any(isConst))
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anmoljh
parents:
diff changeset
912 {
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anmoljh
parents:
diff changeset
913 constParam <- modelInfo$$parameter[isConst]
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parents:
diff changeset
914 constValues <- format(tableData[, constParam, drop = FALSE], digits = 4)[1,,drop = FALSE]
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parents:
diff changeset
915 tableData <- tableData[, !(names(tableData) %in% constParam), drop = FALSE]
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parents:
diff changeset
916 constString <- paste("The tuning",
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parents:
diff changeset
917 ifelse(sum(isConst) > 1,
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parents:
diff changeset
918 "parmeters",
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parents:
diff changeset
919 "parameter"),
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parents:
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920 listString(paste("``", names(constValues), "''", sep = "")),
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parents:
diff changeset
921 ifelse(sum(isConst) > 1,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
922 "were",
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parents:
diff changeset
923 "was"),
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parents:
diff changeset
924 "held constant at",
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parents:
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925 ifelse(sum(isConst) > 1,
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parents:
diff changeset
926 "a value of",
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anmoljh
parents:
diff changeset
927 "values of"),
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parents:
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928 listString(constValues[1,]))
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parents:
diff changeset
929
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930 } else constString <- ""
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parents:
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931
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parents:
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932 cn <- colnames(tableData)
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parents:
diff changeset
933 ## for(i in seq(along = cn))
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parents:
diff changeset
934 ## {
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parents:
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935 ## check <- modelInfo$$parameter %in% cn[i]
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parents:
diff changeset
936 ## if(any(check))
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parents:
diff changeset
937 ## {
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parents:
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938 ## cn[i] <- modelInfo$$label[which(check)]
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parents:
diff changeset
939 ## }
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parents:
diff changeset
940 ## }
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parents:
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941 ## colnames(tableData) <- cn
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anmoljh
parents:
diff changeset
942
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parents:
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943 colNums <- c(numParamInTable,
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parents:
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944 length(modelFit$$perfNames),
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parents:
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945 length(modelFit$$perfNames),
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parents:
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946 length(modelFit$$perfNames))
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parents:
diff changeset
947 colLabels <- c("", "Mean", "Standard Deviation", "Apparant")
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parents:
diff changeset
948
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parents:
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949 }else if (all(modelInfo$$parameter != "parameter") && (resampName == "boot" | resampName == "repeatedcv" | resampName == "cv") ){
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parents:
diff changeset
950 isConst <- apply(tableData[, modelInfo$$parameter, drop = FALSE],
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parents:
diff changeset
951 2,
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parents:
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952 function(x) length(unique(x)) == 1)
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parents:
diff changeset
953
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parents:
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954 numParamInTable <- sum(!isConst)
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parents:
diff changeset
955
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parents:
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956 if(any(isConst))
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parents:
diff changeset
957 {
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958 constParam <- modelInfo$$parameter[isConst]
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parents:
diff changeset
959 constValues <- format(tableData[, constParam, drop = FALSE], digits = 4)[1,,drop = FALSE]
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parents:
diff changeset
960 tableData <- tableData[, !(names(tableData) %in% constParam), drop = FALSE]
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parents:
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961 constString <- paste("The tuning",
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parents:
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962 ifelse(sum(isConst) > 1,
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parents:
diff changeset
963 "parmeters",
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parents:
diff changeset
964 "parameter"),
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parents:
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965 listString(paste("``", names(constValues), "''", sep = "")),
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parents:
diff changeset
966 ifelse(sum(isConst) > 1,
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parents:
diff changeset
967 "were",
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parents:
diff changeset
968 "was"),
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parents:
diff changeset
969 "held constant at",
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parents:
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970 ifelse(sum(isConst) > 1,
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parents:
diff changeset
971 "a value of",
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parents:
diff changeset
972 "values of"),
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parents:
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973 listString(constValues[1,]))
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parents:
diff changeset
974
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975 } else constString <- ""
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parents:
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976
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977 cn <- colnames(tableData)
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parents:
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978 ## for(i in seq(along = cn))
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parents:
diff changeset
979 ## {
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parents:
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980 ## check <- modelInfo$$parameter %in% cn[i]
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parents:
diff changeset
981 ## if(any(check))
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parents:
diff changeset
982 ## {
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parents:
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983 ## cn[i] <- modelInfo$$label[which(check)]
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parents:
diff changeset
984 ## }
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parents:
diff changeset
985 ## }
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parents:
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986 ## colnames(tableData) <- cn
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parents:
diff changeset
987
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parents:
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988 colNums <- c(numParamInTable,
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parents:
diff changeset
989 length(modelFit$$perfNames),
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parents:
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990 length(modelFit$$perfNames))
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parents:
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991 colLabels <- c("", "Mean", "Standard Deviation")
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parents:
diff changeset
992
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993 }
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parents:
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994 else if (all(modelInfo$$parameter != "parameter") && resampName == "LOOCV"){
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parents:
diff changeset
995 isConst <- apply(tableData[, modelInfo$$parameter, drop = FALSE],
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parents:
diff changeset
996 2,
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parents:
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997 function(x) length(unique(x)) == 1)
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parents:
diff changeset
998
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parents:
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999 numParamInTable <- sum(!isConst)
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parents:
diff changeset
1000
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parents:
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1001 if(any(isConst))
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parents:
diff changeset
1002 {
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1003 constParam <- modelInfo$$parameter[isConst]
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parents:
diff changeset
1004 constValues <- format(tableData[, constParam, drop = FALSE], digits = 4)[1,,drop = FALSE]
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parents:
diff changeset
1005 tableData <- tableData[, !(names(tableData) %in% constParam), drop = FALSE]
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parents:
diff changeset
1006 constString <- paste("The tuning",
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parents:
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1007 ifelse(sum(isConst) > 1,
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parents:
diff changeset
1008 "parmeters",
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parents:
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1009 "parameter"),
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parents:
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1010 listString(paste("``", names(constValues), "''", sep = "")),
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1011 ifelse(sum(isConst) > 1,
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1012 "were",
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1013 "was"),
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1014 "held constant at",
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1015 ifelse(sum(isConst) > 1,
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1016 "a value of",
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1017 "values of"),
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1018 listString(constValues[1,]))
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1019
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1020 } else constString <- ""
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1021
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1022 cn <- colnames(tableData)
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1023 ## for(i in seq(along = cn))
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1024 ## {
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1025 ## check <- modelInfo$$parameter %in% cn[i]
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1026 ## if(any(check))
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1027 ## {
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1028 ## cn[i] <- modelInfo$$label[which(check)]
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1029 ## }
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1030 ## }
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1031 ## colnames(tableData) <- cn
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1032
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1033 colNums <- c(numParamInTable,
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1034 length(modelFit$$perfNames))
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1035 colLabels <- c("", "Measures")
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1036
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1037 }
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1038
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1039 }
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1040
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1041
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1042
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1043 colnames(tableData) <- gsub("SD$$", "", colnames(tableData))
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1044 colnames(tableData) <- gsub("Apparent$$", "", colnames(tableData))
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1045 colnames(tableData) <- latexTranslate(colnames(tableData))
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1046 rownames(tableData) <- latexTranslate(rownames(tableData))
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1047
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1048 latex(tableData,
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1049 rowname = NULL,
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1050 file = "",
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1051 cgroup = colLabels,
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1052 n.cgroup = colNums,
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1053 where = "h!",
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1054 digits = 4,
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1055 longtable = nrow(tableData) > 30,
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1056 caption = paste(resampleName, "results from the model fit.", constString),
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1057 label = "T:resamps")
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1058 @
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1059
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1060 \setkeys{Gin}{ width = 0.9\textwidth}
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1061 \begin{figure}[b]
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1062 \begin{center}
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1063
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1064 <<profilePlot, echo = $PROFILEPLOTECHO, fig = $PROFILEPLOTFIG, width = 8, height = 6>>=
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1065 trellis.par.set(caretTheme(), warn = TRUE)
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1066 if(all(modelInfo$$parameter == "parameter") | all(isConst) | modName == "nb")
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1067 {
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1068 resultsPlot <- resampleHist(modelFit)
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1069 plotCaption <- paste("Distributions of model performance from the ",
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1070 "training set estimated using ",
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1071 resampleName)
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1072 } else {
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1073 if(modName %in% c("svmPoly", "svmRadial", "svmLinear"))
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1074 {
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1075 resultsPlot <- plot(modelFit,
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1076 metric = optMetric,
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1077 xTrans = function(x) log10(x))
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1078 resultsPlot <- update(resultsPlot,
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1079 type = c("g", "p", "l"),
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1080 ylab = paste(optMetric, " (", resampleName, ")", sep = ""))
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1081
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1082 } else {
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1083 resultsPlot <- plot(modelFit,
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1084 metric = optMetric)
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1085 resultsPlot <- update(resultsPlot,
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1086 type = c("g", "p", "l"),
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1087 ylab = paste(optMetric, " (", resampleName, ")", sep = ""))
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1088 }
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1089 plotCaption <- paste("A plot of the estimates of the",
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1090 optMetric,
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1091 "values calculated using",
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1092 resampleName)
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1093 }
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1094 print(resultsPlot)
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1095 @
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1096 \caption[Performance Plot]{\Sexpr{plotCaption}.}
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1097 \label{F:profile}
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1098 \end{center}
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1099 \end{figure}
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1100
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1101
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1102 <<stopWorkers, echo = $STOPWORKERSECHO, results = $STOPWORKERSRESULT>>=
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1103 if(numWorkers > 1) stopCluster(cl)
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1104 @
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1105
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1106 <<testPred, results = $TESTPREDRESULT, echo = $TESTPREDECHO>>=
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1107 if((!is.null(testX)) && (!is.null(testY))){
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1108 save(trainX,trainY,testX,testY,file="datasets.RData")
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1109 } else {
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1110 save(trainX,trainY,file="datasets.RData")
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1111 }
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1112
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1113 if(pctTrain < 1)
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1114 {
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1115 cat("\\clearpage\n\\section*{Test Set Results}\n\n")
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1116 classPred <- predict(modelFit, testX)
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1117 cm <- confusionMatrix(classPred, testY)
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1118 values <- cm$$overall[c("Accuracy", "Kappa", "AccuracyPValue", "McnemarPValue")]
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1119
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1120 values <- values[!is.na(values) & !is.nan(values)]
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1121 values <- c(format(values[1:2], digits = 3),
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1122 format.pval(values[-(1:2)], digits = 5))
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1123 nms <- c("the overall accuracy", "the Kappa statistic",
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1124 "the $$p$$--value that accuracy is greater than the no--information rate",
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1125 "the $$p$$--value of concordance from McNemar's test")
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1126 nms <- nms[seq(along = values)]
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1127 names(values) <- nms
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1128
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1129 if(any(modelInfo$$probModel))
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1130 {
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1131 classProbs <- extractProb(list(fit = modelFit),
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1132 testX = testX,
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1133 testY = testY)
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1134 classProbs <- subset(classProbs, dataType == "Test")
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1135 if(numClasses == 2)
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parents:
diff changeset
1136 {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1137 tmp <- twoClassSummary(classProbs, lev = levels(classProbs$$obs))
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1138 tmp <- c(format(tmp, digits = 3))
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1139 names(tmp) <- c("the area under the ROC curve", "the sensitivity", "the specificity")
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anmoljh
parents:
diff changeset
1140
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1141 values <- c(values, tmp)
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anmoljh
parents:
diff changeset
1142
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parents:
diff changeset
1143 }
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anmoljh
parents:
diff changeset
1144 probPlot <- plotClassProbs(classProbs)
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anmoljh
parents:
diff changeset
1145 }
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parents:
diff changeset
1146 testString <- paste("Based on the test set of",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1147 nrow(testX),
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1148 "samples,",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1149 listString(paste(names(values), "was", values), period = TRUE),
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1150 "The confusion matrix for the test set is shown in Table",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1151 "\\\\ref{T:cm}.")
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1152 testString <- paste(testString,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1153 " Using ", resampleName,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1154 ", the training set estimates were ",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1155 resampleStats(modelFit),
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1156 ".",
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parents:
diff changeset
1157 sep = "")
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parents:
diff changeset
1158
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parents:
diff changeset
1159 if(any(modelInfo$$probModel)) testString <- paste(testString,
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anmoljh
parents:
diff changeset
1160 "Histograms of the class probabilities",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1161 "for the test set samples are shown in",
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anmoljh
parents:
diff changeset
1162 "Figure \\\\ref{F:probs}",
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anmoljh
parents:
diff changeset
1163 ifelse(numClasses == 2,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1164 " and the test set ROC curve is in Figure \\\\ref{F:roc}.",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1165 "."))
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parents:
diff changeset
1166
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parents:
diff changeset
1167
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parents:
diff changeset
1168
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parents:
diff changeset
1169 latex(cm$$table,
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parents:
diff changeset
1170 title = "",
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parents:
diff changeset
1171 file = "",
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parents:
diff changeset
1172 where = "h",
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parents:
diff changeset
1173 cgroup = "Observed Values",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1174 n.cgroup = numClasses,
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anmoljh
parents:
diff changeset
1175 caption = "The confusion matrix for the test set",
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anmoljh
parents:
diff changeset
1176 label = "T:cm")
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parents:
diff changeset
1177
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parents:
diff changeset
1178 } else testString <- ""
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parents:
diff changeset
1179 @
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parents:
diff changeset
1180 \Sexpr{testString}
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parents:
diff changeset
1181
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parents:
diff changeset
1182
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parents:
diff changeset
1183 <<classProbsTex, results = $CLASSPROBSTEXRESULT, echo = $CLASSPROBSTEXECHO>>=
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anmoljh
parents:
diff changeset
1184 if(any(modelInfo$probModel) && pctTrain < 1 ) {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1185 cat(
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1186 paste("\\begin{figure}[p]\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1187 "\\begin{center}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1188 "\\includegraphics{classProbs}",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1189 "\\caption[PCA Plot]{Class probabilities",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1190 "for the test set. Each panel contains ",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1191 "separate classes}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1192 "\\label{F:probs}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1193 "\\end{center}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1194 "\\end{figure}"))
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parents:
diff changeset
1195 }
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anmoljh
parents:
diff changeset
1196 if(any(modelInfo$$probModel) & numClasses == 2 & pctTrain < 1 )
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1197 {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1198 cat(
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1199 paste("\\begin{figure}[p]\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1200 "\\begin{center}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1201 "\\includegraphics[clip, width = .8\\textwidth]{roc}",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1202 "\\caption[ROC Plot]{ROC Curve",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1203 "for the test set.}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1204 "\\label{F:roc}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1205 "\\end{center}\n",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1206 "\\end{figure}"))
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1207 } else {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1208 cat (paste(""))
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1209 }
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parents:
diff changeset
1210
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parents:
diff changeset
1211 @
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parents:
diff changeset
1212 <<classProbsTex, results = $CLASSPROBSTEXRESULT1, echo = $CLASSPROBSTEXECHO1 >>=
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1213 if(any(modelInfo$probModel) && pctTrain < 1) {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1214 pdf("classProbs.pdf", height = 7, width = 7)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1215 trellis.par.set(caretTheme(), warn = FALSE)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1216 print(probPlot)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1217 dev.off()
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1218 }
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1219 if(any(modelInfo$probModel) & numClasses == 2 & pctTrain < 1) {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1220 resPonse<-testY
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1221 preDictor<-classProbs[, levels(trainY)[1]]
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1222 pdf("roc.pdf", height = 8, width = 8)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1223 # from pROC example at http://web.expasy.org/pROC/screenshots.htm
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1224 plot.roc(resPonse, preDictor, # data
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1225 percent=TRUE, # show all values in percent
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1226 partial.auc=c(100, 90), partial.auc.correct=TRUE, # define a partial AUC (pAUC)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1227 print.auc=TRUE, #display pAUC value on the plot with following options:
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1228 print.auc.pattern="Corrected pAUC (100-90%% SP):\n%.1f%%", print.auc.col="#1c61b6",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1229 auc.polygon=TRUE, auc.polygon.col="#1c61b6", # show pAUC as a polygon
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1230 max.auc.polygon=TRUE, max.auc.polygon.col="#1c61b622", # also show the 100% polygon
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1231 main="Partial AUC (pAUC)")
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1232 plot.roc(resPonse, preDictor,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1233 percent=TRUE, add=TRUE, type="n", # add to plot, but don't re-add the ROC itself (useless)
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1234 partial.auc=c(100, 90), partial.auc.correct=TRUE,
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1235 partial.auc.focus="se", # focus pAUC on the sensitivity
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1236 print.auc=TRUE, print.auc.pattern="Corrected pAUC (100-90%% SE):\n%.1f%%", print.auc.col="#008600",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1237 print.auc.y=40, # do not print auc over the previous one
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1238 auc.polygon=TRUE, auc.polygon.col="#008600",
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1239 max.auc.polygon=TRUE, max.auc.polygon.col="#00860022")
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1240 dev.off()
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1241 } else {
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1242 cat("")
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1243 }
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parents:
diff changeset
1244
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parents:
diff changeset
1245 @
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parents:
diff changeset
1246
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1247 \section*{Versions}
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anmoljh
parents:
diff changeset
1248
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1249 <<versions, echo = FALSE, results = tex>>=
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1250 toLatex(sessionInfo())
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1251
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1252 @
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parents:
diff changeset
1253
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1254 <<save-data, echo = $SAVEDATAECHO, results = $SAVEDATARESULT>>=
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1255 ## change this to the name of modName....
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
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parents:
diff changeset
1256 Fit <- modelFit
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
diff changeset
1257 if(exists('ppInfo') && !is.null(ppInfo)){
1
ab806d671e22 planemo upload commit d09b58f291738a8b6c646eae5bb4c4bcea37f360
anmoljh
parents: 0
diff changeset
1258 save(Fit,ppInfo,cm,file="$METHOD-Fit.RData")
ab806d671e22 planemo upload commit d09b58f291738a8b6c646eae5bb4c4bcea37f360
anmoljh
parents: 0
diff changeset
1259 } else {save(Fit,cm,file="$METHOD-Fit.RData")}
0
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
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1260
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
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1261 @
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
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1262 The model was built using $METHOD and is saved as $METHOD Model for reuse. This contains the variable Fit.
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
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1263
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
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1264 \end{document}'''
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
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1265
c3cbddd52970 planemo upload commit e713bcfa1b1690f9a21ad0bd796c2d385f646e66-dirty
anmoljh
parents:
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1266 return template4Rnw