plotTrainTest {hddplot} | R Documentation

## Plot predictions for both a I/II train/test split, and the reverse

### 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

    plotTrainTest(x = Golub.BM, nfeatures = c(11, 11), cl = cancer.BM, traintest = divideUp(cancer.BM), 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

### Details

### Value

Two graphs are plotted.

### Note

### Author(s)

John Maindonald

### References

### See Also

### Examples

    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=Golub.BM, nfeatures=c(11,11), cl=cancer.BM,
               traintest=divideUp(cancer.BM),
               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)
      }

[Package hddplot version 0.51 Index]