lmwPcaEffects: PCA on the effect matrices
In bgovaerts/LMWiRe: Linear Models for Wide Responses

lmwPcaEffects

R Documentation

PCA on the effect matrices

Description

Performs a PCA on each of the effect matrices from the outputs of lmwEffectMatrices. It has an option to determine the method applied: ASCA, APCA or ASCA-E. Combined effects (i.e. linear combinations of original effect matrices) can also be created and decomposed.

Usage

lmwPcaEffects(
  resLmwEffectMatrices,
  method = c("ASCA", "APCA", "ASCA-E"),
  combineEffects = NULL,
  verbose = FALSE
)

Arguments

`resLmwEffectMatrices`	A resLmwEffectMatrices list from `lmwEffectMatrices`.
`method`	The method used to compute the PCA. One of `c("ASCA","APCA","ASCA-E")`.
`combineEffects`	If not `NULL`, a list of vectors containing the names of the effects to be combined.
`verbose`	If `TRUE`, will display a message with the duration of execution.

Details

The function allows 3 different methods :

ASCA: The PCA is applied directly on the pure effect matrix.
ASCA-E: The PCA is applied directly on the pure effect matrix but scores are updated.
APCA: The PCA is applied on the augmented effect matrix.

The ASCA-E method adds the residuals to the scores. The APCA method adds the residuals to the effect matrix before the PCA.

Value

A list of PCA results from pcaBySvd for each effect matrix. Those results contain :

scores: Scores from the PCA for each principal component.
loadings: Loadings from the PCA for each principal component.
eigval: Eigenvalues of each principal component.
singvar: Singular values of each principal component.
var: Explained variances of each principal component.
cumvar: Cumulated explained variances of each principal component.
original.dataset: Original dataset.

There are also others outputs :

lmwDataList: The initial object: a list of outcomes, design and formula.
effectsNamesUnique: A character vector with the p names of the model terms, each repeated once.
method: The dimension reduction method used: c("ASCA","APCA","ASCA-E").
type3SS: A vector with the type III SS for each model term.
variationPercentages: A vector with the percentage of variance explained by each model term.

Examples

data('UCH')
resLmwModelMatrix = lmwModelMatrix(UCH)
resLmwEffectMatrices = lmwEffectMatrices(resLmwModelMatrix)
resLmwPcaEffects = lmwPcaEffects(resLmwEffectMatrices, method="ASCA-E")

bgovaerts/LMWiRe documentation built on Sept. 17, 2022, 12:32 a.m.