plot.tune {mixOmics} | R Documentation

## Plot for model performance

### Description

Function to plot performance criteria, such as classification error rate or balanced error rate on a tune.splsda result.

### Usage

    ## S3 method for class 'tune.splsda'
    plot(x, optimal = TRUE, sd = TRUE, col, ...)

    ## S3 method for class 'tune.block.splsda'
    plot(x, sd = TRUE, col, ...)

### Arguments

`x` | an `tune.splsda` object.
`optimal` | If TRUE, highlights the optimal keepX per component
`sd` | If 'nrepeat' was used in the call to 'tune.splsda', error bar shows the standard deviation if sd=TRUE
`col` | character (or symbol) color to be used, possibly vector. One color per component.
`...` | Further arguments sent to `xyplot` function.

### Details

`plot.tune.splsda` plots the classification error rate or the balanced error rate from x$error.rate, for each component of the model. A lozenge highlights the optimal number of variables on each component.

`plot.tune.block.splsda` plots the classification error rate or the balanced error rate from x$error.rate, for each component of the model. The error rate is ordered by increasing value, the yaxis shows the optimal combination of keepX at the top (e.g. ‘keepX on block 1’_‘keepX on block 2’_‘keepX on block 3’)

### Value

none

### Author(s)

Kim-Anh Lê Cao, Florian Rohart, Francois Bartolo.

### See Also

`tune.mint.splsda`, `tune.splsda` `tune.block.splsda` and http://www.mixOmics.org for more details.

### Examples

    ## Not run:
    ## validation for objects of class 'splsda'

    data(breast.tumors)
    X = breast.tumors$gene.exp
    Y = as.factor(breast.tumors$sample$treatment)
    out = tune.splsda(X, Y, ncomp = 3, nrepeat = 5, logratio = "none",
    test.keepX = c(5, 10, 15), folds = 10, dist = "max.dist",
    progressBar = TRUE)

    plot(out)
    plot(out, sd=FALSE)

    \dontrun{
    ## validation for objects of class 'mint.splsda'

    data(stemcells)
    data = stemcells$gene
    type.id = stemcells$celltype
    exp = stemcells$study

    out = tune(method="mint.splsda", X=data,Y=type.id, ncomp=2, study=exp, test.keepX=seq(1,10,1))
    out$choice.keepX

    plot(out)

    ## validation for objects of class 'mint.splsda'

    data("breast.TCGA")
    # this is the X data as a list of mRNA and miRNA; the Y data set is a single data set of proteins
    data = list(mrna = breast.TCGA$data.train$mrna, mirna = breast.TCGA$data.train$mirna,
    protein = breast.TCGA$data.train$protein)
    # set up a full design where every block is connected
    # could also consider other weights, see our mixOmics manuscript
    design = matrix(1, ncol = length(data), nrow = length(data),
    dimnames = list(names(data), names(data)))
    diag(design) =  0
    design
    # set number of component per data set
    ncomp = 5

    # Tuning the first two components
    # -------------

    # definition of the keepX value to be tested for each block mRNA miRNA and protein
    # names of test.keepX must match the names of 'data'
    test.keepX = list(mrna = seq(10,40,20), mirna = seq(10,30,10), protein = seq(1,10,5))

    # the following may take some time to run, note that for through tuning
    # nrepeat should be > 1
    tune = tune.block.splsda(X = data, Y = breast.TCGA$data.train$subtype,
    ncomp = ncomp, test.keepX = test.keepX, design = design, nrepeat = 3)

    tune$choice.ncomp
    tune$choice.keepX

    plot(tune)
    }

    ## End(Not run)

[Package mixOmics version 6.11.11 Index]