sPLSDA {guidedPLS}

Sparse Partial Least Squares Discriminant Analysis (sPLS-DA)

Description

Two matrices X and Y sharing a row are required

Usage

sPLSDA(X, Y, k=.minDim(X, Y), cortest=FALSE, lambda=1, thr=1e-10, fullrank=TRUE,
    num.iter=10, verbose=FALSE)

Arguments

X

The input matrix which has N-rows and M-columns.

Y

The input matrix which has N-rows and L-columns.

k

The number of low-dimension (k < N, M, L, Default: .minDim(X, Y))

cortest

If cortest is set as TRUE, t-test of correlation coefficient is performed (Default: FALSE)

lambda

Penalty parameter to control the sparseness of u and v. The larger the value, the sparser the solution (Default: 1).

thr

Threshold to stop the iteration (Default: 1e-10).

fullrank

If fullrank is set as TRUE, irlba is used, otherwise fullrank SVD is used (Default: TRUE)

num.iter

The number of iterations in each rank (Default: 10)

verbose

Verbose option (Default: FALSE)

Value

scoreX : Score matrix which has M-rows and K-columns. loadingX : Loading matrix which has N-rows and K-columns. scoreY : Score matrix which has L-rows and K-columns. loadingY : Loading matrix which has N-rows and K-columns. d : K-length singular value vector of the cross-product matrix X'Y. corX: Correlation Coefficient (Default: NULL) corY: Correlation Coefficient (Default: NULL) pvalX: P-value vector of corX (Default: NULL) pvalY: P-value vector of corY (Default: NULL) qvalX: Q-value vector of BH method against pvalX (Default: NULL) qvalY: Q-value vector of BH method against pvalY (Default: NULL)

Author(s)

Koki Tsuyuzaki

References

Le Cao, et al. (2008). A Sparse PLS for Variable Selection when Integrating Omics Data. Statistical Applications in Genetics and Molecular Biology, 7(1)

Examples

# Test data
data <- toyModel()

# Simple usage
out <- sPLSDA(X=data$X1, Y=data$Y1, k=4)