mixOmics (version 6.3.2)

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

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.

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 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).

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'
# }
# 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)

# }
# 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)

# }
# 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)
# }
# NOT RUN {
# }