kernel.pca.permute.RdAssess importance of variables on a given PC component by computing the Crone-Crosby distance between original sample positions and sample positions obtained by a random permutation of the variables.
kernel.pca.permute(kpca.result, ncomp = 1, ..., directory = NULL)a kernel.pca object returned by the
kernel.pca function.
integer. Number of KPCA components used to compute the
importance. Default: 1.
list of character vectors. The parameter name must be the kernel
name to be considered for permutation of variables. Provided vectors length
has to be equal to the number of variables of the input dataset. A kernel is
performed on each unique variables values. Crone-Crosby distances are
computed on each KPCA performed on resulted kernels or meta-kernels and can
be displayed using the plotVar.kernel.pca.
character. To limit computational burden, this argument allows to store / read temporary computed kernels.
kernel.pca.permute returns a copy of the input
kpca.result results and add values in the three entries:
cc.distances, cc.variables and cc.blocks.
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.
Mariette J. and Villa-Vialaneix N. (2018). Unsupervised multiple kernel learning for heterogeneous data integration. Bioinformatics, 34(6), 1009-1015. DOI: doi:10.1093/bioinformatics/btx682
Crone L. and Crosby D. (1995). Statistical applications of a metric on subspaces to satellite meteorology. Technometrics, 37(3), 324-328.
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))