plsda: Partial least squares discriminant analysis

In o2plsda: Multiomics Data Integration

Partial least squares discriminant analysis

Description

Perform a PLS discriminant analysis

Usage

plsda(X, Y, nc, scale = TRUE, center = TRUE, cv = TRUE, nr_folds = 5)

Arguments

X	a matrix of predictor variables.
Y	a single vector indicate the group
nc	the number of pls components (the one joint components + number of orthogonal components ).
scale	logical indicating whether X must be scaled (suggest TRUE).
center	logical indicating whether X must be centered (suggest TRUE).
cv	logical indicating whether cross-validation will be performed or not (suggest TRUE).
nr_folds	nr_folds Integer to indicate the folds for cross validation.

Value

a list containing the following elements:

• nc the number of components used(one joint components + number of orthogonal components

• scores a matrix of scores corresponding to the observations in X, The components retrieved correspond to the ones optimized or specified.

• Xloadings a matrix of loadings corresponding to the explanatory variables. The components retrieved correspond to the ones optimized or specified.

• vip the VIP matrix.

• xvar variance explained of X by each single component.

• R2Y variance explained of Y by each single component.

• PRESS The residual sum of squares for the samples which were not used to fit the model

• Q2 quality of cross-validation

Author(s)

Kai Guo

Examples

X <- matrix(rnorm(500),10,50)
Y <- rep(c("a","b"),each=5)
fit <- plsda(X,Y,2)

o2plsda documentation built on Sept. 11, 2024, 8:28 p.m.