groupPLS: Two-step group penalized partial least squares regression

Description Usage Arguments Details Value Author(s) Examples

View source: R/groupPLS.R

Description

Performs group penalized partial least squares regression.

Usage

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groupPLS(formula, scale.X = FALSE, scale.Y = TRUE, k,
  group.index = NA, group.pen = TRUE, lambda.min = 0.01,
  lambda.max = 0.1, ncomp = 20, seed = 1)

Arguments

formula

model formula, y~x, where y are the response variables and x are the predictors.

scale.X

scale x by the sample standard deviation of each variable. TRUE or FALSE

scale.Y

scale y by the sample standard deviation of each variable. TRUE or FALSE

k

number of groups. If group.index is not provided, then perform the hierarchical clustering based on a similarity matrix to define k clusters.

group.index

pre-defined group index for each predictor.

group.pen

group penalization on latent components. TRUE or FALSE.

lambda.min

lower bound of group penalty paramter.

lambda.max

upper bound of group penalty paramter.

ncomp

number of latent components in partial least squares.

seed

an integer.

Details

More details.

Value

Return groupPLS object.

ncomp.group

indices of latent components from PLS in each group.

latent.group

combined latent components from PLS in each group.

coef

regression coefficients of latent components on responses.

scale.X

if scaling of X is requested.

scale.Y

if scaling of Y is requested.

Xmean

mean of each variable in X.

Xsd

standard deviation of each variable in X.

Ymean

mean of each variable in Y.

Ysd

standard deviation of each variable in Y.

k

number of groups.

group.index

group index for each predictor.

group.lambda

group penalty paramter selected by cross validation.

call

function call.

Author(s)

Jiali Wang (jiali.wang@data61.csiro.au); Le Chang (le.chang@anu.edu.au)

Examples

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data<-read.csv("Spectra_eps.csv")
data<-data[complete.cases(data),]
rs<-groupPLS(data[,c(2,4:6)] ~ data[,7:dim(data)[2]], k=3)

jialiwang1211/groupPLS documentation built on Feb. 17, 2020, 12:35 a.m.