R: Multivariate Data Analysis Laboratory

# Multivariate Data Analysis Laboratory

## Documentation for package ‘mvdalab’ version 1.7

- DESCRIPTION file
- Package NEWS
- Package README

## 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