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:Nelson Lee Afanador, Thanh Tran, Lionel Blanchet, and Richard Baumgartner

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 = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:
  • 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.

2.56 score 72 scripts 344 downloads 1 mentions 88 exports 67 dependencies

Last updated 2 years agofrom:21e9983ecc. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 11 2024
R-4.5-linuxOKOct 11 2024

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:abindbackportsbootbroomcarcarDataclicolorspacecowplotcpp11DerivdoBydplyrfansifarverFormulagenericsggplot2gluegtableisobandlabelinglatticelifecyclelme4magrittrMASSMatrixMatrixModelsmgcvmicrobenchmarkminqamnormtmodelrmomentsmunsellnlmenloptrnnetnumDerivpbkrtestpenalizedpillarpkgconfigplyrpurrrquantregR6RColorBrewerRcppRcppArmadilloRcppEigenreshape2rlangscalessnSparseMstringistringrsurvivaltibbletidyrtidyselectutf8vctrsviridisLitewithr

Readme and manuals

Help Manual

Help pageTopics
Multivariate Data Analysis Laboratory (mvdalab)mvdalab-package mvdalab
Plot of Auto-correlation Funcionacfplot
Actual versus Predicted Plot and Residuals versus Predictedap.plot
Bias-corrected and Accelerated Confidence Intervalsbca.cis
Bidiag2 PLSbidiagpls.fit
Generates a biplot from the output of an 'mvdareg' and 'mvdapca' objectBiPlot
Plots of the Output of a Bootstrap Simulation for an 'mvdareg' Objectboot.plots
Extract Information From a plsFit Modelcoef.mvdareg
BCa Summaries for the coefficient of an mvdareg objectcoefficients.boots
Extract Summary Information Pertaining to the Coefficients resulting from a PLS modelcoefficients.mvdareg
2-Dimensionsl Graphical Summary Information Pertaining to the Coefficients of a PLScoefficientsplot2D
Graphical Summary Information Pertaining to the Regression Coefficientscoefsplot
Data for College Level Examination Program and the College Qualification TestCollege
Cell Means Contrast Matrixcontr.niets
Ellipses, Data Ellipses, and Confidence Ellipsesellipse.mvdalab
Naive imputation of missing values.imputeBasic
Expectation Maximization (EM) for imputation of missing values.imputeEM print.empca
Quartile Naive Imputation of Missing ValuesimputeQs
Naive Imputation of Missing Values for Dummy Variable Model MatriximputeRough print.roughImputation
Introduce NA's into a DataframeintroNAs
Jackknife After Bootstrapjk.after.boot
Summary Information Pertaining to the Bootstrapped Loadingsloadings.mvdareg
BCa Summaries for the loadings of an mvdareg objectloadings.boots
Graphical Summary Information Pertaining to the Loadingsloadingsplot
2-Dimensionsl Graphical Summary Information Pertaining to the Loadings of a PLS or PCA Analysisloadingsplot2D
Generates a Hotelling's T2 Graph of the Multivariate Exponentially Weighted Averagemewma
'model.matrix' creates a design (or model) matrix.model.matrix.mvdareg
Principal Component Based Multivariate Process Capability IndicesMultCapability
Calculate Hotelling's T2 Confidence IntervalsMVcis
Traditional Multivariate Mean Vector ComparisonMVComp print.mvcomp
Bootstrapping routine for 'mvdareg' objectsmvdaboot
Leave-one-out routine for 'mvdareg' objectsmvdaloo
Simulate from a Multivariate Normal, Poisson, Exponential, or Skewed Distributionmvrnorm.svd mvrnormBase.svd
Create a Design Matrix with the Desired Constrastsmy.dummy.df
Delete Intercept from Model Matrixno.intercept
PCA with the NIPALS algorithmpca.nipals print.npca
Principal Component AnalysispcaFit plot.mvdapca print.mvdapca
Percent Explained Variation of XPE
Penta data setPenta
Percentile Bootstrap Confidence Intervalsperc.cis
Plotting Function for Score Contributions.plot.cp
Plot of Multivariate Mean Vector Comparisonplot.mvcomp
General plotting function for 'mvdareg' and 'mvdapaca' objects.plot.mvdareg
2D Graph of the PCA scores associated with a plusminusFitplot.plusminus
Plot of R2plot.R2s
Plotting function for Significant Multivariate Correlationplot.smc
Plotting function for Selectivity Ratio.plot.sr
Plots of the Output of a Permutation Distribution for an 'mvdareg' Object with 'method = "bidiagpls"'plot.wrtpls
Partial Least Squares Regressionmvdareg plsFit summary.mvdareg summary.mvdareg.default
PlusMinus (Mas-o-Menos)plusminus.fit
Leave-one-out routine for 'plusminus' objectsplusminus.loo
plusMinusDat data setplusMinusDat
Plus-Minus (Mas-o-Menos) ClassifierplusminusFit summary.plusminus summary.plusminus.default
Model Predictions From a plsFit Modelpredict.mvdareg
Print Methods for mvdalab Objectsprint.mvdareg
Print Methods for plusminus Objectsprint.plusminus
Comparison of n-point Configurations vis Procrustes Analysisprint.proC proCrustes
Cross-validated R2, R2 for X, and R2 for Y for PLS modelsprint.R2s R2s
Generates a score contribution plotScoreContrib
2D Graph of the scoresscoresplot
Sequential Expectation Maximization (EM) for imputation of missing values.print.seqem SeqimputeEM
Significant Multivariate Correlationprint.smc smc smc.error smc.modeled
Test of the Residual Significant Multivariate Correlation Matrix for the presence of Autocorrelationsmc.acfTest
Selectivity Ratioprint.sr sr sr.error sr.modeled
Generates a Hotelling's T2 GraphT2
Bivariate process data.Wang_Chen
Simulated process data from a plastics manufacturer.Wang_Chen_Sim
BCa Summaries for the weights of an mvdareg objectweight.boots
Extract Summary Information Pertaining to the Bootstrapped weightsweights.mvdareg
Extract Graphical Summary Information Pertaining to the Weightsweightsplot
Extract a 2-Dimensional Graphical Summary Information Pertaining to the weights of a PLS Analysisweightsplot2D
Weight Randomization Test PLSwrtpls.fit
Generates a Graph of the X-residualsXresids
Generates the squared prediction error contributions and contribution plotXresidualContrib
Extract Summary Information Pertaining to the y-loadingsy.loadings
Extract Summary Information Pertaining to the y-loadingsy.loadings.boots