Package: mvdalab 1.7
Nelson Lee Afanador
mvdalab: Multivariate Data Analysis Laboratory
An open-source implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods including low dimensional embedding, classical multivariate statistical tools, and tools for enhanced interpretation of machine learning methods (i.e. intelligible models to provide important information for end-users). Target domains include extension to dedicated applications e.g. for manufacturing process modeling, spectroscopic analyses, and data mining.
Authors:
mvdalab_1.7.tar.gz
mvdalab_1.7.tar.gz(r-4.5-noble)mvdalab_1.7.tar.gz(r-4.4-noble)
mvdalab_1.7.tgz(r-4.4-emscripten)mvdalab_1.7.tgz(r-4.3-emscripten)
mvdalab.pdf |mvdalab.html✨
mvdalab/json (API)
NEWS
| # Install 'mvdalab' in R: |
| install.packages('mvdalab', repos = 'https://cloud.r-project.org') |
- College - Data for College Level Examination Program and the College Qualification Test
- Penta - Penta data set
- Wang_Chen - Bivariate process data.
- Wang_Chen_Sim - Simulated process data from a plastics manufacturer.
- plusMinusDat - PlusMinusDat data set
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 2 years agofrom:21e9983ecc. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 10 2025 |
| R-4.5-linux | OK | Mar 10 2025 |
| R-4.4-linux | OK | Mar 10 2025 |
Exports:acfplotap.plotbca.cisbidiagpls.fitBiPlotboot.plotscoef.mvdaregcoefficients.bootscoefficients.mvdaregcoefficientsplot2Dcoefsplotcontr.nietsellipse.mvdalabimputeBasicimputeEMimputeQsimputeRoughintroNAsjk.after.bootloadings.bootsloadings.mvdaregloadingsplotloadingsplot2Dmewmamodel.matrix.mvdaregMultCapabilityMVcisMVCompmvdabootmvdaloomvrnorm.svdmvrnormBase.svdmy.dummy.dfno.interceptpca.nipalspcaFitPEperc.cisplot.cpplot.mvcompplot.mvdapcaplot.mvdaregplot.plusminusplot.R2splot.smcplot.srplot.wrtplsplsFitplusminus.fitplusminus.looplusminusFitpredict.mvdaregprint.empcaprint.mvcompprint.mvdapcaprint.mvdaregprint.npcaprint.plusminusprint.proCprint.R2sprint.roughImputationprint.seqemprint.smcprint.srproCrustesR2sScoreContribscoresplotSeqimputeEMsmcsmc.acfTestsmc.errorsmc.modeledsrsr.errorsr.modeledsummary.mvdaregsummary.plusminusT2weight.bootsweights.mvdaregweightsplotweightsplot2Dwrtpls.fitXresidsXresidualContriby.loadingsy.loadings.boots
Dependencies:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamnormtmodelrmomentsmunsellnlmenloptrnnetnumDerivpbkrtestpenalizedpillarpkgconfigplyrpurrrquantregR6rbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasreshape2rlangscalessnSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr
Citation
To cite package ‘mvdalab’ in publications use:
Afanador NL, Tran T, Blanchet L, Baumgartner R (2022). mvdalab: Multivariate Data Analysis Laboratory. R package version 1.7, https://CRAN.R-project.org/package=mvdalab.
ATTENTION: This citation information has been auto-generated from the package DESCRIPTION file and may need manual editing, see ‘help("citation")’.
Corresponding BibTeX entry:
@Manual{,
title = {mvdalab: Multivariate Data Analysis Laboratory},
author = {Nelson Lee Afanador and Thanh Tran and Lionel Blanchet
and Richard Baumgartner},
year = {2022},
note = {R package version 1.7},
url = {https://CRAN.R-project.org/package=mvdalab},
}
Readme and manuals
NOTE: This R package focuses on the implementation of latent variable methods and multivariate modeling tools. The focus is on exploratory analyses using dimensionality reduction methods and classical multivariate statistical tools.
Quick demo
Fitting a PLS model:
library(mvdalab)
data(Penta)
mod1 <- plsFit(log.RAI ~., scale = TRUE, data = Penta[, -1],
ncomp = 3, method = "bidiagpls",
validation = "oob")
summary(mod1)
#> Call:
#>
#> plsFit(formula = log.RAI ~ ., ncomp = 3, data = Penta[, -1],
#> method = "bidiagpls", scale = TRUE,
#> validation = "oob")
#>
#> Coefficients:
#> Estimate Bootstrap Error 't value' bias 'bias t value'
#> L3 0.438131130 0.07813105 5.60764388 -0.056287765 -0.72042756
#> S3 -0.339839340 0.08368706 -4.06083517 0.044477379 0.53147262
#> P1 -0.210181974 0.06071820 -3.46159762 0.057955814 0.95450482
#> S1 -0.135884870 0.06997010 -1.94204192 0.019907955 0.28452089
#> P3 0.111249534 0.06854336 1.62305337 0.034862926 0.50862586
#> S2 0.089752422 0.04701730 1.90892350 0.006892843 0.14660228
#> L2 0.071367951 0.04526160 1.57678793 -0.019359275 -0.42771960
#> L4 0.069951677 0.07232330 0.96720806 -0.004522244 -0.06252818
#> L5 0.035696148 0.04552429 0.78411217 0.008591990 0.18873419
#> P4 -0.028238597 0.05462905 -0.51691543 -0.020000349 -0.36611199
#> P2 -0.025167765 0.06283351 -0.40054683 -0.012729431 -0.20258984
#> S4 0.020226747 0.06658432 0.30377644 -0.037592328 -0.56458231
#> L1 0.017465764 0.06489465 0.26914025 -0.006988723 -0.10769335
#> S5 0.010701880 0.04456740 0.24012801 -0.004024037 -0.09029106
#> P5 -0.002811084 0.04681625 -0.06004504 0.003525576 0.07530667
#>
#> Fit Summary:
#>
#> Number of objects = 30
#> Number of predictor variables = 15
#> Method: bidiagpls
#> No. of bootstrap samples = 1000
#> Number of components considered
#> in above parameter estimates = 3
#> R2X = 0.228 0.389 0.485
#> R2Y = 0.691 0.824 0.874
#> Out-of-Bag R2 (per component) = 0.446 0.458 0.354
#> Out-of-Bag PRESS (per component) = 4.335 3.902 4.455
#> Out-of-Bag MSPRESS.632 (per component) = 0.335 0.263 0.286
#> Out-of-Bag RMSPRESS.632 (per component) = 0.578 0.512 0.535
PCA via NIPALS.
library(mvdalab)
my.nipals <- pca.nipals(iris[, 1:4], ncomps = 4, tol = 1e-08)
names(my.nipals)
#> [1] "Loadings" "Scores" "Loading.Space" "Score.Space"
my.nipals$Loadings
#> [,1] [,2] [,3] [,4]
#> Sepal.Length 0.36138514 0.65659919 -0.58203416 0.3154592
#> Sepal.Width -0.08452411 0.73015136 0.59793829 -0.3196944
#> Petal.Length 0.85667099 -0.17337204 0.07625627 -0.4798353
#> Petal.Width 0.35828937 -0.07548926 0.54579393 0.7536837
svd(scale(iris[, 1:4], scale = FALSE))$v
#> [,1] [,2] [,3] [,4]
#> [1,] 0.36138659 -0.65658877 0.58202985 0.3154872
#> [2,] -0.08452251 -0.73016143 -0.59791083 -0.3197231
#> [3,] 0.85667061 0.17337266 -0.07623608 -0.4798390
#> [4,] 0.35828920 0.07548102 -0.54583143 0.7536574
Traditional Multivariate Mean Vector Comparison.
library(mvdalab)
data(College)
dat1 <- College
#Generate a 'fake' difference of 15 units
dat2 <- College + matrix(rnorm(nrow(dat1) * ncol(dat1), mean = 15),
nrow = nrow(dat1), ncol = ncol(dat1))
Comparison <- MVComp(dat1, dat2, level = .95)
Comparison
#> lower 95 % confidence upper 95 % confidence Significance
#> 1 -47.66009 17.95686 Not Significant
#> 2 -19.69886 -10.07013 Significant
#> 3 -16.88621 -12.75947 Significant