42 lines
1.3 KiB
R
42 lines
1.3 KiB
R
## ItMLiHSmar2022
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## standardise.R, child script
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## Data standardisation, returns list
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## Andreas Gammelgaard Damsbo, agdamsbo@clin.au.dk
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standardise<-function(train,test,type){
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# From:
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# https://datascience.stackexchange.com/questions/13971/standardization-normalization-test-data-in-r
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sel<-sapply(Xtrain,is.numeric) # Deciding which to stadardise (only numeric)
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cnm<-colnames(Xtrain) # Saving column names for ordering
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# Subsetting
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## Data to treat
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train.tr<-train[,sel]
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test.tr<-test[,sel]
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## Data to save
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train.sv<-train[,!sel]
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test.sv<-test[,!sel]
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# Calculate mean and SD of train data
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trainMean <- sapply(train.tr,mean)
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trainSd <- sapply(train.tr,sd)
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if (type=="c"){
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## centered
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norm.trainData<-sweep(train.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise
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norm.testData<-sweep(test.tr, 2L, trainMean) # using the default "-" to subtract mean column-wise
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}
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if (type=="cs"){
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## centered AND scaled (Z-score standardisation)
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norm.trainData<-sweep(sweep(train.tr, 2L, trainMean), 2, trainSd, "/")
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norm.testData<-sweep(sweep(test.tr, 2L, trainMean), 2, trainSd, "/")
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}
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return(list(XtrainSt=cbind(norm.trainData,train.sv)[,cnm], # Reordering columns to original
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XtestSt=cbind(norm.testData,test.sv)[,cnm]))
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}
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