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3
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1 args <- commandArgs(T)
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2
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3 arg1 <- args[1]
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4 arg2 <- args[2]
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5 arg3 <- args[3]
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6 arg4 <- args[4]
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7 arg5 <- args[5]
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8 arg6 <- args[6]
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9 arg7 <- args[7]
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10 arg8 <- args[8]
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11
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12 library(caret)
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13 load(arg1)
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14
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15 RAWDATA <- dataX
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16 RAWDATA$outcome <- dataY
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17 rawData <- dataX
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18 predictorNames <- names(rawData)
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19
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20 isNum <- apply(rawData[,predictorNames, drop = FALSE], 2, is.numeric)
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21 if(any(!isNum)) stop("all predictors in rawData should be numeric")
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22
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23 colRate <- apply(rawData[, predictorNames, drop = FALSE],
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24 2, function(x) mean(is.na(x)))
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4
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25 colExclude <- colRate > 0.1
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3
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26 if(any(colExclude)){
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27 predictorNames <- predictorNames[-which(colExclude)]
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28 rawData <- RAWDATA[, c(predictorNames,"outcome")]
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29 } else {
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30 rawData <- RAWDATA
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31 }
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32 rowRate <- apply(rawData[, predictorNames, drop = FALSE],
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33 1, function(x) mean(is.na(x)))
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34
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4
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35
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36 rowExclude <- rowRate > 0
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3
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37 if(any(rowExclude)){
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38 rawData <- rawData[!rowExclude, ]
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39 ##hasMissing <- apply(rawData[, predictorNames, drop = FALSE],
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40 ##1, function(x) mean(is.na(x)))
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41
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42 ############################################################################
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43
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44
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45 ###############################################################################
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46 } else {
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47 rawData <- rawData[complete.cases(rawData),]
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48
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49 }
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50
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4
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51 set.seed(2)
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3
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52
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53 #print(dim(dataX))
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54 #print(dim(rawData))
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55 #print(length(dataY))
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56
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57 nzv <- nearZeroVar(rawData[,1:(length(rawData) - 1)])
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58 if(length(nzv) > 0) {
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59 #nzvVars <- names(rawData)[nzv]
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60 rawData <- rawData[,-nzv]
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61 #rawData$outcome <- dataY
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62 }
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63
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64 predictorNames <- names(rawData)[names(rawData) != "outcome"]
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65
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66 dx <- rawData[,1:length(rawData)-1]
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67 dy <- rawData[,length(rawData)]
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68 corrThresh <- as.numeric(arg8)
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69 highCorr <- findCorrelation(cor(dx, use = "pairwise.complete.obs"),corrThresh)
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70 dx <- dx[, -highCorr]
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71 subsets <- seq(1,length(dx),by=5)
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72 normalization <- preProcess(dx)
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73 dx <- predict(normalization, dx)
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74 dx <- as.data.frame(dx)
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75
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76 if (arg4 == "lmFuncs"){
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77 ctrl1 <- rfeControl(functions = lmFuncs,
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78 method = arg5 ,
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79 repeats = as.numeric(arg6),
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80 number = as.numeric(arg7),
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81 verbose = FALSE)
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82 } else if(arg4 == "rfFuncs"){
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83 ctrl1 <- rfeControl(functions = rfFuncs,
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84 method = arg5 ,
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85 repeats = as.numeric(arg6),
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86 number = as.numeric(arg7),
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87 verbose = FALSE)
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88 }else if (arg4 == "treebagFuncs"){
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89 ctrl1 <- rfeControl(functions = treebagFuncs,
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90 method = arg5 ,
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91 repeats = as.numeric(arg6),
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92 number = as.numeric(arg7),
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93 verbose = FALSE)
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94 }else {
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95
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96 ctrl1 <- rfeControl(functions = nbFuncs,
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97 method = arg5 ,
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98 repeats = as.numeric(arg6),
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99 number = as.numeric(arg7),
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100 verbose = FALSE)
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101 }
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102
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103
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104
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105
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106 Profile <- rfe(dx, dy,sizes = subsets,rfeControl = ctrl1)
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107
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108 pred11 <- predictors(Profile)
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109 save(Profile,file=arg2)
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110 dataX <- rawData[,pred11]
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111 dataY <- rawData$outcome
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112
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113 save(dataX,dataY,file=arg3)
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114 rm(dataX)
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115 rm(dataY)
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116
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