preprocess
(Package: plsgenomics) :
preprocess for microarray data
The function preprocess performs a preprocessing of microarray data.
● Data Source:
CranContrib
● Keywords:
● Alias: preprocess
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mrpls
(Package: plsgenomics) :
Ridge Partial Least Square for categorical data
The function mrpls performs prediction using Fort et al. (2005) MRPLS algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: mrpls
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TFA.estimate
(Package: plsgenomics) :
Prediction of Transcription Factor Activities using PLS
The function TFA.estimate estimates the transcription factor activities from gene expression data and ChIP data using the PLS multivariate regression approach described in Boulesteix and Strimmer (2005).
● Data Source:
CranContrib
● Keywords: regression
● Alias: TFA.estimate
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mrpls.cv
(Package: plsgenomics) :
Determination of the ridge regularization parameter and the number of PLS
The function mrpls.cv determines the best ridge regularization parameter and the best number of PLS components to be used for classification for Fort et al. (2005) MRPLS algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: mrpls.cv
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mgsim.cv
(Package: plsgenomics) :
Determination of the ridge regularization parameter and the bandwidth to be used for
The function mgsim.cv determines the best ridge regularization parameter and bandwidth to be used for classification with MGSIM as described in Lambert-Lacroix and Peyre (2005).
● Data Source:
CranContrib
● Keywords:
● Alias: mgsim.cv
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spls.adapt.tune
(Package: plsgenomics) :
Tuning parameters (ncomp, lambda.l1) for Adaptive Sparse PLS regression for continuous
The function rirls.spls.tune tuns the hyper-parameter values used in the spls.adapt procedure, by minimizing the mean squared error of prediction over the hyper-parameter grid, using Durif et al. (2015) adaptive SPLS algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: spls.adapt.tune
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sample.bin
(Package: plsgenomics) :
Generates design matrix X with correlated block of covariates and a binary random reponse
The function sample.bin generates a random sample with p predictors X, a binary response Y, and n observations, through a logistic model, where the response Y is generated as a Bernoulli random variable of parameter logit^{-1}(XB), the coefficients B are sparse, and the design matrix X is composed of correlated blocks of predictors.
● Data Source:
CranContrib
● Keywords:
● Alias: sample.bin
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pls.lda.cv
(Package: plsgenomics) :
Determination of the number of latent components to be used for
The function pls.lda.cv determines the best number of latent components to be used for classification with PLS dimension reduction and linear discriminant analysis as described in Boulesteix (2004).
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: pls.lda.cv
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pls.regression
(Package: plsgenomics) :
Multivariate Partial Least Squares Regression
The function pls.regression performs pls multivariate regression (with several response variables and several predictor variables) using de Jong's SIMPLS algorithm. This function is an adaptation of R. Wehrens' code from the package pls.pcr.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: pls.regression
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gsim
(Package: plsgenomics) :
GSIM for binary data
The function gsim performs prediction using Lambert-Lacroix and Peyre's GSIM algorithm.
● Data Source:
CranContrib
● Keywords:
● Alias: gsim
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