Return the specific contribution of each variable for each sample for the first dimension of each block.
Arguments
- A
The original data.
- res
The results of
sgcca()orrgcca().- scale
Logical value whether to scale or not the data.
- bias
Logical value whether to use bias or not when scaling the data.
Value
A list of matrices with the variables of each block and the value for each sample that helps place the sample on the reduced-dimensional space.
Examples
if (FALSE) {
# Download the dataset's package at http://biodev.cea.fr/sgcca/.
# --> gliomaData_0.4.tar.gz
require(gliomaData)
data(ge_cgh_locIGR)
A <- ge_cgh_locIGR$multiblocks
Loc <- factor(ge_cgh_locIGR$ylabel)
levels(Loc) <- colnames(ge_cgh_locIGR$multiblocks$y)
C <- matrix(c(0, 0, 1, 0, 0, 1, 1, 1, 0), 3, 3)
tau = c(1, 1, 0)
# rgcca algorithm using the dual formulation for X1 and X2
# and the dual formulation for X3
A[[3]] = A[[3]][, -3]
# sgcca algorithm
result.sgcca = sgcca(A, C, c1 = c(.071,.2, 1), ncomp = c(1, 1, 1),
scheme = "centroid", verbose = FALSE)
weights <- variables_contribution(A, result.sgcca)
}