mvdalab: Multivariate Data Analysis Laboratory

An open-source project 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.

AuthorNelson Lee Afanador, Thanh Tran, and Lionel Blanchet
Date of publication2016-07-09 10:45:38
MaintainerNelson Lee Afanador <nelson.afanador@mvdalab.com>
LicenseGPL-3
Version1.1

View on CRAN

Man pages

acfplot: Plot of Auto-correlation Funcion

ap.plot: Actual versus Predicted Plot and Residuals versus Predicted

bca.cis: Bias-corrected and Accelerated Confidence Intervals

bidiagpls.fit: Bidiag2 PLS

BiPlot: Generates a biplot from the output of an 'mvdareg' and...

boot.plots: Plots of the Output of a Bootstrap Simulation for an...

coefficients: Extract Summary Information Pertaining to the Coefficients...

coefficients.boots: BCa Summaries for the coefficient of an mvdareg object

coefficientsplot2D: 2-Dimensionsl Graphical Summary Information Pertaining to the...

coef.mvdareg: Extract Information From a plsFit Model

coefsplot: Graphical Summary Information Pertaining to the Regression...

College: Data for College Level Examination Program and the College...

contr.niets: Cell Means Contrast Matrix

ellipse.mvdalab: Ellipses, Data Ellipses, and Confidence Ellipses

imputeBasic: Naive imputation of missing values.

imputeEM: Expectation Maximization (EM) for imputation of missing...

imputeQs: Quartile Naive Imputation of Missing Values

imputeRough: Naive Imputation of Missing Values for Dummy Variable Model...

introNAs: Introduce NA's into a Dataframe

jk.after.boot: Jackknife After Bootstrap

loadings: Summary Information Pertaining to the Bootstrapped Loadings

loadings.boots: BCa Summaries for the loadings of an mvdareg object

loadingsplot: Graphical Summary Information Pertaining to the Loadings

loadingsplot2D: 2-Dimensionsl Graphical Summary Information Pertaining to the...

mewma: Generates a Hotelling's T2 Graph of the Multivariate...

model.matrix.mvdalab: 'model.matrix' creates a design (or model) matrix.

MVcis: Calculate Hotelling's T2 Confidence Intervals

MVComp: Traditional Multivariate Mean Vector Comparison

mvdaboot: Bootstrapping routine for 'mvdareg' objects

mvdalab-package-title: Multivariate Data Analysis Laboratory (mvdalab)

mvdaloo: Leave-one-out routine for 'mvdareg' objects

mvrnorm.svd: Simulate from a Multivariate Normal, Poisson, Exponential, or...

my.dummy.df: Create a Design Matrix with the Desired Constrasts

no.intercept: Delete Intercept from Model Matrix

pcaFit: Principal Component Analysis

pca.nipals: PCA with the NIPALS algorithm

PE: Percent Explained Variation of X

Penta: Penta data set

perc.cis: Percentile Bootstrap Confidence Intervals

plot.cp: Plotting Function for Score Contributions.

plot.mvcomp: Plot of Multivariate Mean Vector Comparison

plot.mvdareg: General plotting function for 'mvdareg' and 'mvdapaca'...

plot.plusminus: 2D Graph of the PCA scores associated with a plusminusFit

plot.R2s: Plot of R2

plot.smc: Plotting function for Significant Multivariate Correlation

plot.sr: Plotting function for Selectivity Ratio.

plot.vip: Plotting function for Variable Importance in the Projection

plsFit: Partial Least Squares Regression

plusMinusDat: plusMinusDat data set

plusminus.fit: PlusMinus (Mas-o-Menos)

plusminusFit: Plus-Minus (Mas-o-Menos) Classifier

plusminus.loo: Leave-one-out routine for 'plusminus' objects

predict.mvdareg: Model Predictions From a plsFit Model

print.mvdalab: Print Methods for mvdalab Objects

print.plusminus: Print Methods for plusminus Objects

proCrustes: Comparison of n-point Configurations vis Procrustes Analysis

R2s: Cross-validated R2, R2 for X, and R2 for Y for PLS models

ScoreContrib: Generates a score contribution plot

scoresplot: 2D Graph of the scores

SeqimputeEM: Sequential Expectation Maximization (EM) for imputation of...

smc: Significant Multivariate Correlation

smc.acfTest: Test of the Residual Significant Multivariate Correlation...

sr: Selectivity Ratio

T2: Generates a Hotelling's T2 Graph

vip: Variable Importance in the Projection

weight.boots: BCa Summaries for the weights of an mvdareg object

weights: Extract Summary Information Pertaining to the Bootstrapped...

weightsplot: Extract Graphical Summary Information Pertaining to the...

weightsplot2D: Extract a 2-Dimensional Graphical Summary Information...

Xresids: Generates a Graph of the X-residuals

XresidualContrib: Generates the squared prediction error contributions and...

y.loadings: Extract Summary Information Pertaining to the y-loadings

y.loadings.boots: Extract Summary Information Pertaining to the y-loadings

Files in this package

mvdalab
mvdalab/NAMESPACE
mvdalab/NEWS.md
mvdalab/data
mvdalab/data/Penta.rda
mvdalab/data/plusMinusDat.rda
mvdalab/data/College.rda
mvdalab/R
mvdalab/R/acfplot.R mvdalab/R/coef.mvdareg.R mvdalab/R/plot.mvcomp.R mvdalab/R/model.matrix.mvdareg.R mvdalab/R/imputeBasic.R mvdalab/R/XresidualContrib.R mvdalab/R/loadings.mvdareg.R mvdalab/R/loadingsplot.R mvdalab/R/vip.R mvdalab/R/Xresids.R mvdalab/R/print.npca.R mvdalab/R/weightsplot.R mvdalab/R/plot.plusminus.R mvdalab/R/T2.R mvdalab/R/weights.mvdareg.R mvdalab/R/coefficients.mvdareg.R mvdalab/R/MVcis.R mvdalab/R/print.empca.R mvdalab/R/loadingsplot2D.R mvdalab/R/print.seqem.R mvdalab/R/print.proC.R mvdalab/R/summary.plusminus.R mvdalab/R/plot.R2s.R mvdalab/R/my.dummy.df.R mvdalab/R/print.mvdareg.R mvdalab/R/print.mvdapca.R mvdalab/R/imputeRough.R mvdalab/R/bidiagpls.fit.R mvdalab/R/coefficients.boots.R mvdalab/R/smc.R mvdalab/R/mewma.R mvdalab/R/no.intercept.R mvdalab/R/proCrustes.R mvdalab/R/R2s.R mvdalab/R/pcaFit.R mvdalab/R/sr.R mvdalab/R/pca.nipals.R mvdalab/R/plusminus.loo.R mvdalab/R/smc.acfTest.R mvdalab/R/weight.boots.R mvdalab/R/summary.mvdareg.R mvdalab/R/mvdaboot.R mvdalab/R/imputeQs.R mvdalab/R/introNAs.R mvdalab/R/SeqimputeEM.R mvdalab/R/boot.plots.R mvdalab/R/ap.plot.R mvdalab/R/weightsplot2D.R mvdalab/R/BiPlot.R mvdalab/R/y.loadings.R mvdalab/R/jk.after.boot.R mvdalab/R/PE.R mvdalab/R/MVComp.R mvdalab/R/imputeEM.R mvdalab/R/predict.mvdareg.R mvdalab/R/plot.mvdapca.R mvdalab/R/mvrnorm.svd.R mvdalab/R/plot.mvdareg.R mvdalab/R/bca.cis.R mvdalab/R/perc.cis.R mvdalab/R/plusminusFit.R mvdalab/R/loadings.boots.R mvdalab/R/coefsplot.R mvdalab/R/scoresplot.R mvdalab/R/contr.niets.R mvdalab/R/plot.cp.R mvdalab/R/ScoreContrib.R mvdalab/R/mvdalab.R mvdalab/R/plsFit.R mvdalab/R/print.plusminus.R mvdalab/R/y.loadings.boots.R mvdalab/R/mvrnormBase.svd.R mvdalab/R/ellipse.mvdalab.R mvdalab/R/plusminus.fit.R mvdalab/R/plot.vip.R mvdalab/R/coefficientsplot2D.R mvdalab/R/mvdaloo.R
mvdalab/README.md
mvdalab/MD5
mvdalab/DESCRIPTION
mvdalab/man
mvdalab/man/loadingsplot.Rd mvdalab/man/MVcis.Rd mvdalab/man/imputeBasic.Rd mvdalab/man/MVComp.Rd mvdalab/man/proCrustes.Rd mvdalab/man/my.dummy.df.Rd mvdalab/man/plot.mvcomp.Rd mvdalab/man/coefficients.Rd mvdalab/man/SeqimputeEM.Rd mvdalab/man/vip.Rd mvdalab/man/coef.mvdareg.Rd mvdalab/man/bidiagpls.fit.Rd mvdalab/man/print.mvdalab.Rd mvdalab/man/introNAs.Rd mvdalab/man/plot.cp.Rd mvdalab/man/weightsplot.Rd mvdalab/man/BiPlot.Rd mvdalab/man/imputeRough.Rd mvdalab/man/mvdaloo.Rd mvdalab/man/XresidualContrib.Rd mvdalab/man/predict.mvdareg.Rd mvdalab/man/y.loadings.Rd mvdalab/man/plot.vip.Rd mvdalab/man/mvdaboot.Rd mvdalab/man/no.intercept.Rd mvdalab/man/sr.Rd mvdalab/man/jk.after.boot.Rd mvdalab/man/print.plusminus.Rd mvdalab/man/ScoreContrib.Rd mvdalab/man/plot.R2s.Rd mvdalab/man/coefsplot.Rd mvdalab/man/plot.sr.Rd mvdalab/man/contr.niets.Rd mvdalab/man/plot.mvdareg.Rd mvdalab/man/T2.Rd mvdalab/man/smc.Rd mvdalab/man/College.Rd mvdalab/man/coefficientsplot2D.Rd mvdalab/man/PE.Rd mvdalab/man/mvdalab-package-title.Rd mvdalab/man/plusMinusDat.Rd mvdalab/man/weight.boots.Rd mvdalab/man/plot.smc.Rd mvdalab/man/loadings.boots.Rd mvdalab/man/loadings.Rd mvdalab/man/scoresplot.Rd mvdalab/man/model.matrix.mvdalab.Rd mvdalab/man/mvrnorm.svd.Rd mvdalab/man/imputeEM.Rd mvdalab/man/ap.plot.Rd mvdalab/man/boot.plots.Rd mvdalab/man/acfplot.Rd mvdalab/man/Xresids.Rd mvdalab/man/plot.plusminus.Rd mvdalab/man/weights.Rd mvdalab/man/plusminus.loo.Rd mvdalab/man/plusminusFit.Rd mvdalab/man/smc.acfTest.Rd mvdalab/man/imputeQs.Rd mvdalab/man/Penta.Rd mvdalab/man/R2s.Rd mvdalab/man/plusminus.fit.Rd mvdalab/man/plsFit.Rd mvdalab/man/perc.cis.Rd mvdalab/man/weightsplot2D.Rd mvdalab/man/y.loadings.boots.Rd mvdalab/man/bca.cis.Rd mvdalab/man/mewma.Rd mvdalab/man/loadingsplot2D.Rd mvdalab/man/pca.nipals.Rd mvdalab/man/coefficients.boots.Rd mvdalab/man/pcaFit.Rd mvdalab/man/ellipse.mvdalab.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.