Multivariate Data Analysis Laboratory

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Documentation for package ‘mvdalab’ version 1.7

• DESCRIPTION file.
• Package NEWS.

Help Pages

mvdalab-package	Multivariate Data Analysis Laboratory (mvdalab)
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 'mvdapca' object
boot.plots	Plots of the Output of a Bootstrap Simulation for an 'mvdareg' Object
coef.mvdareg	Extract Information From a plsFit Model
coefficients.boots	BCa Summaries for the coefficient of an mvdareg object
coefficients.mvdareg	Extract Summary Information Pertaining to the Coefficients resulting from a PLS model
coefficientsplot2D	2-Dimensionsl Graphical Summary Information Pertaining to the Coefficients of a PLS
coefsplot	Graphical Summary Information Pertaining to the Regression Coefficients
College	Data for College Level Examination Program and the College Qualification Test
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 values.
imputeQs	Quartile Naive Imputation of Missing Values
imputeRough	Naive Imputation of Missing Values for Dummy Variable Model Matrix
introNAs	Introduce NA's into a Dataframe
jk.after.boot	Jackknife After Bootstrap
loadings.boots	BCa Summaries for the loadings of an mvdareg object
loadings.mvdareg	Summary Information Pertaining to the Bootstrapped Loadings
loadingsplot	Graphical Summary Information Pertaining to the Loadings
loadingsplot2D	2-Dimensionsl Graphical Summary Information Pertaining to the Loadings of a PLS or PCA Analysis
mewma	Generates a Hotelling's T2 Graph of the Multivariate Exponentially Weighted Average
model.matrix.mvdareg	'model.matrix' creates a design (or model) matrix.
MultCapability	Principal Component Based Multivariate Process Capability Indices
MVcis	Calculate Hotelling's T2 Confidence Intervals
MVComp	Traditional Multivariate Mean Vector Comparison
mvdaboot	Bootstrapping routine for 'mvdareg' objects
mvdalab	Multivariate Data Analysis Laboratory (mvdalab)
mvdaloo	Leave-one-out routine for 'mvdareg' objects
mvdareg	Partial Least Squares Regression
mvrnorm.svd	Simulate from a Multivariate Normal, Poisson, Exponential, or Skewed Distribution
mvrnormBase.svd	Simulate from a Multivariate Normal, Poisson, Exponential, or Skewed Distribution
my.dummy.df	Create a Design Matrix with the Desired Constrasts
no.intercept	Delete Intercept from Model Matrix
pca.nipals	PCA with the NIPALS algorithm
pcaFit	Principal Component Analysis
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.mvdapca	Principal Component Analysis
plot.mvdareg	General plotting function for 'mvdareg' and 'mvdapaca' objects.
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.wrtpls	Plots of the Output of a Permutation Distribution for an 'mvdareg' Object with 'method = "bidiagpls"'
plsFit	Partial Least Squares Regression
plusminus.fit	PlusMinus (Mas-o-Menos)
plusminus.loo	Leave-one-out routine for 'plusminus' objects
plusMinusDat	plusMinusDat data set
plusminusFit	Plus-Minus (Mas-o-Menos) Classifier
predict.mvdareg	Model Predictions From a plsFit Model
print.empca	Expectation Maximization (EM) for imputation of missing values.
print.mvcomp	Traditional Multivariate Mean Vector Comparison
print.mvdapca	Principal Component Analysis
print.mvdareg	Print Methods for mvdalab Objects
print.npca	PCA with the NIPALS algorithm
print.plusminus	Print Methods for plusminus Objects
print.proC	Comparison of n-point Configurations vis Procrustes Analysis
print.R2s	Cross-validated R2, R2 for X, and R2 for Y for PLS models
print.roughImputation	Naive Imputation of Missing Values for Dummy Variable Model Matrix
print.seqem	Sequential Expectation Maximization (EM) for imputation of missing values.
print.smc	Significant Multivariate Correlation
print.sr	Selectivity Ratio
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 missing values.
smc	Significant Multivariate Correlation
smc.acfTest	Test of the Residual Significant Multivariate Correlation Matrix for the presence of Autocorrelation
smc.error	Significant Multivariate Correlation
smc.modeled	Significant Multivariate Correlation
sr	Selectivity Ratio
sr.error	Selectivity Ratio
sr.modeled	Selectivity Ratio
summary.mvdareg	Partial Least Squares Regression
summary.mvdareg.default	Partial Least Squares Regression
summary.plusminus	Plus-Minus (Mas-o-Menos) Classifier
summary.plusminus.default	Plus-Minus (Mas-o-Menos) Classifier
T2	Generates a Hotelling's T2 Graph
Wang_Chen	Bivariate process data.
Wang_Chen_Sim	Simulated process data from a plastics manufacturer.
weight.boots	BCa Summaries for the weights of an mvdareg object
weights.mvdareg	Extract Summary Information Pertaining to the Bootstrapped weights
weightsplot	Extract Graphical Summary Information Pertaining to the Weights
weightsplot2D	Extract a 2-Dimensional Graphical Summary Information Pertaining to the weights of a PLS Analysis
wrtpls.fit	Weight Randomization Test PLS
Xresids	Generates a Graph of the X-residuals
XresidualContrib	Generates the squared prediction error contributions and contribution plot
y.loadings	Extract Summary Information Pertaining to the y-loadings
y.loadings.boots	Extract Summary Information Pertaining to the y-loadings