Preparation | Getting started | One Component | Two Component | Single Block | Principal Component Analysis (PCA) | Other functions in R for PCA | Sparse PCA | Supervised Sparse PCA | Partial Least Squres (PLS) | Sparse PLS | Supervised Sparse PLS | Multi Block | PCA | Nested Component | PLS | Parameter Selection | Number of Components | Nested | Combined Search
Package: msma 3.1
Atsushi Kawaguchi
msma: Multiblock Sparse Multivariable Analysis
Several functions can be used to analyze multiblock multivariable data. If the input is a single matrix, then principal components analysis (PCA) is implemented. If the input is a list of matrices, then multiblock PCA is implemented. If the input is two matrices, for exploratory and objective variables, then partial least squares (PLS) analysis is implemented. If the input is two lists of matrices, for exploratory and objective variables, then multiblock PLS analysis is implemented. Additionally, if an extra outcome variable is specified, then a supervised version of the methods above is implemented. For each method, sparse modeling is also incorporated. Functions for selecting the number of components and regularized parameters are also provided.
Authors:
msma_3.1.tar.gz
msma_3.1.tar.gz(r-4.7-any)msma_3.1.tar.gz(r-4.6-any)
msma_3.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
msma/json (API)
| # Install 'msma' in R: |
| install.packages('msma', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated from:2c2de7a3e4. Checks:4 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 112 | ||
| source / vignettes | OK | 296 | ||
| linux-release-x86_64 | OK | 107 | ||
| wasm-release | OK | 90 |
Exports:cvmsmahcmsmamsmancompsearchoptparasearchregparasearchsimdatastrsimdata
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Multiblock Sparse Matrix Analysis Package | msma-package |
| Cross-Validation | cvmsma |
| Hierarchical cluster analysis | hcmsma |
| Multiblock Sparse Partial Least Squares | msma msma.default print.msma |
| Search for Number of Components | ncompsearch plot.ncompsearch print.ncompsearch |
| Parameters Search | optparasearch print.optparasearch |
| Plot msma | plot.msma |
| Prediction | predict.msma |
| Regularized Parameters Search | print.regparasearch regparasearch |
| Simulate Data sets | simdata |
| Structured Simulate Data sets | strsimdata |
| Summarizing Fits | print.summary.msma summary.msma |