GSIM for categorical data
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1,
lambda.ridge) for the multinomial-SPLS method
Ridge Partial Least Square for categorical data
Determination of the ridge regularization parameter and the number of PLS
components to be used for classification with RPLS for categorical data
Heatmap visualization for matrix
Stability selection procedure to estimate probabilities of selection of
covariates for the multinomial-SPLS method
Determination of the number of latent components to be used for
classification with PLS and LDA
Determination of the ridge regularization parameter and the bandwidth to be used for
classification with GSIM for categorical data
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
Classification with PLS Dimension Reduction and Linear Discriminant
Analysis
Multivariate Partial Least Squares Regression
Generates covariate matrix X with correlated block of covariates and
a binary random reponse depening on X through a logistic model
Ridge Partial Least Square for binary data
Generates covariate matrix X with correlated block of covariates and
a multi-label random reponse depening on X through a multinomial model
Deprecated function(s) in the 'plsgenomics' package
preprocess for microarray data
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
Determination of the ridge regularization parameter and the number of PLS
components to be used for classification with RPLS for binary data
Internal Functions for the 'plsgenomics' package
Determination of the number of latent components to be used in PLS regression
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1)
of the Adaptive Sparse PLS regression
Stability selection procedure to select covariates for the sparse PLS,
LOGIT-SPLS and multinomial-SPLS methods
Adaptive Sparse Partial Least Squares (SPLS) regression
Stability selection procedure to estimate probabilities of selection of
covariates for the sparse PLS method
Variable selection using the PLS weights
stability.selection.heatmap
Heatmap visualization of estimated probabilities of selection for each
covariate
GSIM for binary data
Determination of the ridge regularization parameter and the bandwidth to be used for
classification with GSIM for binary data
Stability selection procedure to estimate probabilities of selection of
covariates for the LOGIT-SPLS method
Gene expression data from Khan et al. (2001)
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
Cross-validation procedure to calibrate the parameters (ncomp, lambda.l1,
lambda.ridge) for the LOGIT-SPLS method
Gene expression data from Alon et al. (1999)
Ecoli gene expression and connectivity data from Kao et al. (2003)
Gene expression data from Golub et al. (1999)
Prediction of Transcription Factor Activities using PLS