msma: Multiblock Sparse Multivariable Analysis

Share:

There are several functions to implement the method for analysis in a multiblock multivariable data. If the input is only a matrix, then the principal components analysis (PCA) is implemented. If the input is a list of matrices, then the multiblock PCA is implemented. If the input is two matrices for exploratory and objective variables, then the partial least squares (PLS) analysis is implemented. If the input is two list of matrices for exploratory and objective variables, then the multiblock PLS analysis is implemented. Moreover, if the extra outcome variable is specified, then the supervised version for the methods above is implemented. For each methods, the sparse modeling is also incorporated. Functions to select the number of components and the regularized parameters are also provided.

Author
Atsushi Kawaguchi
Date of publication
2016-01-01 21:47:02
Maintainer
Atsushi Kawaguchi <kawa_a24@yahoo.co.jp>
License
GPL (>= 2)
Version
0.7

View on CRAN

Man pages

cvmsma
Cross-Validation
msma
Multiblock Sparse Multivariable Analysis
msma-internal
Internal functions
msma-package
Multiblock Sparse Multivariable Analysis Package
ncompsearch
Search for Number of Components
predict.msma
Prediction
regparasearch
Regularized Parameters Search
simdata
Generate Test Data Sets
summary.msma
Summarizing Fits

Files in this package

msma
msma/NAMESPACE
msma/R
msma/R/src.r
msma/MD5
msma/DESCRIPTION
msma/man
msma/man/regparasearch.Rd
msma/man/ncompsearch.Rd
msma/man/msma.Rd
msma/man/msma-package.Rd
msma/man/msma-internal.Rd
msma/man/summary.msma.Rd
msma/man/cvmsma.Rd
msma/man/simdata.Rd
msma/man/predict.msma.Rd