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 |

**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

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
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