plot.tune: Plot for model performance
In mixOmics: Omics Data Integration Project

Description Usage Arguments Details Author(s) See Also Examples

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

## S3 method for class 'tune.splsda'
plot(x, optimal = TRUE, sd = TRUE, legend.position = "topright", col, ...)
## S3 method for class 'tune.spls'
plot(x, optimal = TRUE, sd = TRUE, legend.position = "topright", col, ...)

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

`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
`legend.position`	position of the legend, one of "bottomright", "bottom", "bottomleft", -"left", "topleft", "top", "topright", "right" and "center".
`col`	character (or symbol) color to be used, possibly vector. One color per component.
`...`	Further arguments sent to `xyplot` function.

plot.tune.splsda plots the classification error rate or the balanced error rate from x$error.rate, for each component of the model. A circle highlights the optimal number of variables oneach 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’)

plot.tune.spls plots the tuning results for the measure of accuracy x$measure, for each component of the model and any tested keepX parameters when Y is fixed (contact us for more details).

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

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

## validation for objects of class 'splsda'
## Not run: 
data(breast.tumors)
X = breast.tumors$gene.exp
Y = as.factor(breast.tumors$sample$treatment)
out = tune.splsda(X, Y, ncomp = 3, nrepeat = 2, logratio = "none",
test.keepX = c(5, 10, 15), folds = 10, dist = "max.dist",
progressBar = TRUE)


plot(out)


## End(Not run)

## validation for objects of class 'mint.splsda'
## Not run: 
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)


## End(Not run)

## validation for objects of class 'mint.splsda'
## Not run: 
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)