plotVar.kernel.pca: Plot importance of variables in kernel PCA

In mixKernel: Omics Data Integration Using Kernel Methods

Plot importance of variables in kernel PCA

Description

Provides a representation of variable importance in kernel PCA.

Usage

plotVar.kernel.pca(
  object,
  blocks = unique(object$cc.blocks),
  ndisplay = 5,
  ncol = 2,
  ...
)

Arguments

object	: a kernel.pca object returned by kernel.pca.
blocks	a numerical vector indicating the block variables to display.
ndisplay	integer. The number of important variables per blocks shown in the representation. Default: 5.
ncol	integer. Each block of variables is displayed in a separate subfigure. ncol sets the number of columns for the global figure. Default: 2.
...	external arguments.

Details

plotVar.kernel.pca produces a barplot for each block. The variables for which the importance has been computed with kernel.pca.permute are displayed. The representation is limited to the ndisplay most important variables.

Author(s)

Jerome Mariette <[e-mail]> Nathalie Vialaneix <[e-mail]>

References

Crone L. and Crosby D. (1995). Statistical applications of a metric on subspaces to satellite meteorology. Technometrics, 37(3), 324-328.

See Also

kernel.pca, kernel.pca.permute

Examples

data(TARAoceans)

# compute one kernel for the psychem dataset
phychem.kernel <- compute.kernel(TARAoceans$phychem, kernel.func = "linear")
# perform a KPCA
kernel.pca.result <- kernel.pca(phychem.kernel)
# compute importance for all variables in this kernel
kernel.pca.result <- kernel.pca.permute(kernel.pca.result, phychem = colnames(TARAoceans$phychem))

## Not run: plotVar.kernel.pca(kernel.pca.result, ndisplay = 10)

mixKernel documentation built on Sept. 18, 2023, 5:16 p.m.