# PLS Analyses for Genomics

## Documentation for package ‘plsgenomics’ version 1.5-3

- DESCRIPTION file.

## Help Pages

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