Package: sNPLS 1.0.40
sNPLS: NPLS Regression with L1 Penalization
Tools for performing variable selection in three-way data using N-PLS in combination with L1 penalization, Selectivity Ratio and VIP scores. The N-PLS model (Rasmus Bro, 1996 <doi:10.1002/(SICI)1099-128X(199601)10:1%3C47::AID-CEM400%3E3.0.CO;2-C>) is the natural extension of PLS (Partial Least Squares) to N-way structures, and tries to maximize the covariance between X and Y data arrays. The package also adds variable selection through L1 penalization, Selectivity Ratio and VIP scores.
Authors:
sNPLS_1.0.40.tar.gz
sNPLS_1.0.40.zip(r-4.5)sNPLS_1.0.40.zip(r-4.4)sNPLS_1.0.40.zip(r-4.3)
sNPLS_1.0.40.tgz(r-4.4-any)sNPLS_1.0.40.tgz(r-4.3-any)
sNPLS_1.0.40.tar.gz(r-4.5-noble)sNPLS_1.0.40.tar.gz(r-4.4-noble)
sNPLS_1.0.40.tgz(r-4.4-emscripten)sNPLS_1.0.40.tgz(r-4.3-emscripten)
sNPLS.pdf |sNPLS.html✨
sNPLS/json (API)
# Install 'sNPLS' in R: |
install.packages('sNPLS', repos = c('https://david-hervas.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/david-hervas/snpls/issues
- bread - Bread data
Last updated 11 months agofrom:373054a149. Checks:OK: 1 NOTE: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 25 2024 |
R-4.5-win | NOTE | Oct 25 2024 |
R-4.5-linux | NOTE | Oct 25 2024 |
R-4.4-win | NOTE | Oct 25 2024 |
R-4.4-mac | NOTE | Oct 25 2024 |
R-4.3-win | NOTE | Oct 25 2024 |
R-4.3-mac | NOTE | Oct 25 2024 |
Exports:auroccv_fitcv_snplsga_snplsrepeat_cvsNPLSSR
Dependencies:beeswarmcliclickRcodetoolscolorspacecrayondigestfansifarverFNNforeachfuturefuture.applyGAggplot2ggrepelglobalsgluegtableisobanditeratorskernlabKernSmoothkslabelinglatticelifecyclelistenvmagrittrMASSMatrixmclustmgcvmulticoolmunsellmvtnormnlmeparallellypbapplypillarpkgconfigplyrpracmapROCR6RColorBrewerRcppRcppArmadillorlangscalesstringdisttibbleutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
AUC for sNPLS-DA model | auroc |
Bread data | bread |
Coefficients from a sNPLS model | coef.sNPLS |
Internal function for 'cv_snpls' | cv_fit |
Cross-validation for a sNPLS model | cv_snpls |
Fitted method for sNPLS models | fitted.sNPLS |
Genetic Algorithm for selection of hyperparameter values | ga_snpls |
Internal function for 'plot.sNPLS' | plot_T |
Internal function for 'plot.sNPLS' | plot_time |
Internal function for 'plot.sNPLS' | plot_U |
Internal function for 'plot.sNPLS' | plot_variables |
Internal function for 'plot.sNPLS' | plot_Wj |
Internal function for 'plot.sNPLS' | plot_Wk |
Plot cross validation results for sNPLS objects | plot.cvsNPLS |
Density plot for repeat_cv results | plot.repeatcv |
Plots for sNPLS model fits | plot.sNPLS |
Predict for sNPLS models | predict.sNPLS |
Repeated cross-validation for sNPLS models | repeat_cv |
R-matrix from a sNPLS model fit | Rmatrix |
Fit a sNPLS model | sNPLS |
Compute Selectivity Ratio for a sNPLS model | SR |
Summary for sNPLS models | summary.sNPLS |
Unfolding of three-way arrays | unfold3w |