Multivariate Data Analysis Laboratory
Actual versus Predicted Plot and Residuals versus Predicted
Bias-corrected and Accelerated Confidence Intervals
Bidiag2 PLS
Generates a biplot from the output of an 'mvdareg' and 'mvdapca' objec...
Plot of Auto-correlation Funcion
Plots of the Output of a Bootstrap Simulation for an mvdareg Object
Extract Information From a plsFit Model
BCa Summaries for the coefficient of an mvdareg object
Extract Summary Information Pertaining to the Coefficients resulting f...
2-Dimensionsl Graphical Summary Information Pertaining to the Coeffici...
Graphical Summary Information Pertaining to the Regression Coefficient...
Cell Means Contrast Matrix
Ellipses, Data Ellipses, and Confidence Ellipses
Naive imputation of missing values.
Expectation Maximization (EM) for imputation of missing values.
Quartile Naive Imputation of Missing Values
Naive Imputation of Missing Values for Dummy Variable Model Matrix
Introduce NA's into a Dataframe
Jackknife After Bootstrap
BCa Summaries for the loadings of an mvdareg object
Summary Information Pertaining to the Bootstrapped Loadings
Graphical Summary Information Pertaining to the Loadings
2-Dimensionsl Graphical Summary Information Pertaining to the Loadings...
Generates a Hotelling's T2 Graph of the Multivariate Exponentially Wei...
model.matrix creates a design (or model) matrix.
Principal Component Based Multivariate Process Capability Indices
Calculate Hotelling's T2 Confidence Intervals
Traditional Multivariate Mean Vector Comparison
Bootstrapping routine for mvdareg objects
Multivariate Data Analysis Laboratory (mvdalab)
Leave-one-out routine for mvdareg objects
Simulate from a Multivariate Normal, Poisson, Exponential, or Skewed D...
Create a Design Matrix with the Desired Constrasts
Delete Intercept from Model Matrix
PCA with the NIPALS algorithm
Principal Component Analysis
Percent Explained Variation of X
Percentile Bootstrap Confidence Intervals
Plotting Function for Score Contributions.
Plot of Multivariate Mean Vector Comparison
General plotting function for mvdareg and mvdapaca objects.
2D Graph of the PCA scores associated with a plusminusFit
Plot of R2
Plotting function for Significant Multivariate Correlation
Plotting function for Selectivity Ratio.
Plots of the Output of a Permutation Distribution for an mvdareg Obj...
Partial Least Squares Regression
PlusMinus (Mas-o-Menos)
Leave-one-out routine for plusminus objects
Plus-Minus (Mas-o-Menos) Classifier
Model Predictions From a plsFit Model
Print Methods for mvdalab Objects
Print Methods for plusminus Objects
Comparison of n-point Configurations vis Procrustes Analysis
Cross-validated R2, R2 for X, and R2 for Y for PLS models
Generates a score contribution plot
2D Graph of the scores
Sequential Expectation Maximization (EM) for imputation of missing val...
Test of the Residual Significant Multivariate Correlation Matrix for t...
Significant Multivariate Correlation
Selectivity Ratio
Generates a Hotelling's T2 Graph
BCa Summaries for the weights of an mvdareg object
Extract Summary Information Pertaining to the Bootstrapped weights
Extract Graphical Summary Information Pertaining to the Weights
Extract a 2-Dimensional Graphical Summary Information Pertaining to th...
Weight Randomization Test PLS
Generates a Graph of the X-residuals
Generates the squared prediction error contributions and contribution ...
Extract Summary Information Pertaining to the y-loadings
Extract Summary Information Pertaining to the y-loadings
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.