# plotTrainTest: Plot predictions for both a I/II train/test split, and the... In hddplot: Use Known Groups in High-Dimensional Data to Derive Scores for Plots

## Description

A division of data is specified, for use of linear discriminant analysis, into a training and test set. Feature selection and model fitting is formed, first with I/II as training/test, then with II/I as training/test. Two graphs are plotted – for the I (training) /II (test) scores, and for the II/I scores.

## Usage

 ```1 2 3``` ```plotTrainTest(x, nfeatures, cl, traintest, titles = c("A: I/II (train with I, scores are for II)", "B: II/I (train with II, scores are for I)")) ```

## Arguments

 `x` Matrix; rows are features, and columns are observations ('samples') `nfeatures` integer: numbers of features for which calculations are required `cl` Factor that classifies columns into groups that will classify the data for purposes of discriminant calculations `traintest` Values that specify a division of observations into two groups. In the first pass (fold), one to be training and the other test, with the roles then reversed in a second pass or fold. `titles` A character vector of length 2 giving titles for the two graphs

## Value

Two graphs are plotted.

John Maindonald

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40``` ```mat <- matrix(rnorm(1000), ncol=20) cl <- factor(rep(1:3, c(7,9,4))) gp.id <- divideUp(cl, nset=2) plotTrainTest(x=mat, cl=cl, traintest=gp.id, nfeatures=c(2,3)) ## The function is currently defined as function(x, nfeatures, cl, traintest, titles=c("A: I/II (train with I, scores are for II)", "B: II/I (train with II, scores are for I)")){ oldpar <- par(mfrow=c(1,2), pty="s") on.exit(par(oldpar)) if(length(nfeatures)==1)nfeatures <- rep(nfeatures,2) traintest <- factor(traintest) train <- traintest==levels(traintest)[1] testset <- traintest==levels(traintest)[2] cl1 <- cl[train] cl2 <- cl[testset] nf1 <- nfeatures[1] ord1 <- orderFeatures(x, cl, subset=train) df1 <- data.frame(t(x[ord1[1:nf1], train])) df2 <- data.frame(t(x[ord1[1:nf1], testset])) df1.lda <- lda(df1, cl1) scores <- predict(df1.lda, newdata=df2)\$x scoreplot(scorelist=list(scores=scores, cl=cl2, nfeatures=nfeatures[1], other=NULL, cl.other=NULL), prefix.title="") mtext(side=3, line=2, titles[1], adj=0) nf2 <- nfeatures[2] ord2 <- orderFeatures(x, cl, subset=testset) df2 <- data.frame(t(x[ord2[1:nf2], testset])) df1 <- data.frame(t(x[ord2[1:nf2], train])) df2.lda <- lda(df2, cl2) scores <- predict(df2.lda, newdata=df1)\$x scoreplot(scorelist=list(scores=scores, cl=cl1, nfeatures=nfeatures[2], other=NULL, cl.other=NULL), prefix.title="") mtext(side=3, line=2, titles[2], adj=0) } ```

hddplot documentation built on June 16, 2018, 1:35 p.m.