PLS Analyses for Genomics

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Documentation for package ‘plsgenomics’ version 1.5-3

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Colon Gene expression data from Alon et al. (1999)
Ecoli Ecoli gene expression and connectivity data from Kao et al. (2003)
gsim GSIM for binary data
gsim.cv Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for binary data
leukemia Gene expression data from Golub et al. (1999)
logit.pls Ridge Partial Least Square for binary data
logit.pls.cv Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for binary data
logit.spls Classification procedure for binary response based on a logistic model, solved by a combination of the Ridge Iteratively Reweighted Least Squares (RIRLS) algorithm and the Adaptive Sparse PLS (SPLS) regression
logit.spls.cv Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the LOGIT-SPLS method
logit.spls.stab Stability selection procedure to estimate probabilities of selection of covariates for the LOGIT-SPLS method
m.rirls.spls Deprecated function(s) in the 'plsgenomics' package
m.rirls.spls.stab Deprecated function(s) in the 'plsgenomics' package
m.rirls.spls.tune Deprecated function(s) in the 'plsgenomics' package
matrix.heatmap Heatmap visualization for matrix
mgsim GSIM for categorical data
mgsim.cv Determination of the ridge regularization parameter and the bandwidth to be used for classification with GSIM for categorical data
mrpls Ridge Partial Least Square for categorical data
mrpls.cv Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for categorical data
multinom.spls Classification procedure for multi-label response based on a multinomial model, solved by a combination of the multinomial Ridge Iteratively Reweighted Least Squares (multinom-RIRLS) algorithm and the Adaptive Sparse PLS (SPLS) regression
multinom.spls.cv Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1, lambda.ridge) for the multinomial-SPLS method
multinom.spls.stab Stability selection procedure to estimate probabilities of selection of covariates for the multinomial-SPLS method
pls.lda Classification with PLS Dimension Reduction and Linear Discriminant Analysis
pls.lda.cv Determination of the number of latent components to be used for classification with PLS and LDA
pls.regression Multivariate Partial Least Squares Regression
pls.regression.cv Determination of the number of latent components to be used in PLS regression
plsgenomics-deprecated Deprecated function(s) in the 'plsgenomics' package
preprocess preprocess for microarray data
rirls.spls Deprecated function(s) in the 'plsgenomics' package
rirls.spls.stab Deprecated function(s) in the 'plsgenomics' package
rirls.spls.tune Deprecated function(s) in the 'plsgenomics' package
rpls Ridge Partial Least Square for binary data
rpls.cv Determination of the ridge regularization parameter and the number of PLS components to be used for classification with RPLS for binary data
sample.bin Generates covariate matrix X with correlated block of covariates and a binary random reponse depening on X through a logistic model
sample.cont Generates design matrix X with correlated block of covariates and a continuous random reponse Y depening on X through gaussian linear model Y=XB+E
sample.multinom Generates covariate matrix X with correlated block of covariates and a multi-label random reponse depening on X through a multinomial model
spls Adaptive Sparse Partial Least Squares (SPLS) regression
spls.adapt Deprecated function(s) in the 'plsgenomics' package
spls.adapt.tune Deprecated function(s) in the 'plsgenomics' package
spls.cv Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1) of the Adaptive Sparse PLS regression
spls.stab Stability selection procedure to estimate probabilities of selection of covariates for the sparse PLS method
SRBCT Gene expression data from Khan et al. (2001)
stability.selection Stability selection procedure to select covariates for the sparse PLS, LOGIT-SPLS and multinomial-SPLS methods
stability.selection.heatmap Heatmap visualization of estimated probabilities of selection for each covariate
TFA.estimate Prediction of Transcription Factor Activities using PLS
variable.selection Variable selection using the PLS weights