mixOmics (version 6.2.0)

plot.tune.splsda: 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, …)

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.

See Also

tune.mint.splsda, tune.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.constraint

plot(out)

# }