heplots - Visualizing Hypothesis Tests in Multivariate Linear Models
Provides HE plot and other functions for visualizing hypothesis tests in multivariate linear models. HE plots represent sums-of-squares-and-products matrices for linear hypotheses and for error using ellipses (in two dimensions) and ellipsoids (in three dimensions). The related 'candisc' package provides visualizations in a reduced-rank canonical discriminant space when there are more than a few response variables.
Last updated 4 months ago
linear-hypothesesmatricesmultivariate-linear-modelsplotrepeated-measure-designsvisualizing-hypothesis-tests
9 stars 4.94 score 82 dependencies 28 dependentscandisc - Visualizing Generalized Canonical Discriminant and Canonical Correlation Analysis
Functions for computing and visualizing generalized canonical discriminant analyses and canonical correlation analysis for a multivariate linear model. Traditional canonical discriminant analysis is restricted to a one-way 'MANOVA' design and is equivalent to canonical correlation analysis between a set of quantitative response variables and a set of dummy variables coded from the factor variable. The 'candisc' package generalizes this to higher-way 'MANOVA' designs for all factors in a multivariate linear model, computing canonical scores and vectors for each term. The graphic functions provide low-rank (1D, 2D, 3D) visualizations of terms in an 'mlm' via the 'plot.candisc' and 'heplot.candisc' methods. Related plots are now provided for canonical correlation analysis when all predictors are quantitative.
Last updated 4 months ago
dimension-reductionmultivariate-linear-modelsvisualization
15 stars 4.78 score 83 dependencies 26 dependentsmatlib - Matrix Functions for Teaching and Learning Linear Algebra and Multivariate Statistics
A collection of matrix functions for teaching and learning matrix linear algebra as used in multivariate statistical methods. These functions are mainly for tutorial purposes in learning matrix algebra ideas using R. In some cases, functions are provided for concepts available elsewhere in R, but where the function call or name is not obvious. In other cases, functions are provided to show or demonstrate an algorithm. In addition, a collection of functions are provided for drawing vector diagrams in 2D and 3D.
Last updated 3 days ago
diagramslinear-equationsmatrixmatrix-functionsmatrix-visualizervectorvignette
65 stars 4.42 score 84 dependencies 10 dependentsHistData - Data Sets from the History of Statistics and Data Visualization
The 'HistData' package provides a collection of small data sets that are interesting and important in the history of statistics and data visualization. The goal of the package is to make these available, both for instructional use and for historical research. Some of these present interesting challenges for graphics or analysis in R.
Last updated 4 months ago
graphicshistorical-data
63 stars 3.76 score 0 dependencies 3 dependentsvcdExtra - 'vcd' Extensions and Additions
Provides additional data sets, methods and documentation to complement the 'vcd' package for Visualizing Categorical Data and the 'gnm' package for Generalized Nonlinear Models. In particular, 'vcdExtra' extends mosaic, assoc and sieve plots from 'vcd' to handle 'glm()' and 'gnm()' models and adds a 3D version in 'mosaic3d'. Additionally, methods are provided for comparing and visualizing lists of 'glm' and 'loglm' objects. This package is now a support package for the book, "Discrete Data Analysis with R" by Michael Friendly and David Meyer.
Last updated 1 years ago
categorical-data-visualizationgeneralized-linear-modelsmosaic-plots
24 stars 3.22 score 42 dependencies 3 dependentsggbiplot - A Grammar of Graphics Implementation of Biplots
A 'ggplot2' based implementation of biplots, giving a representation of a dataset in a two dimensional space accounting for the greatest variance, together with variable vectors showing how the data variables relate to this space. It provides a replacement for stats::biplot(), but with many enhancements to control the analysis and graphical display. It implements biplot and scree plot methods which can be used with the results of prcomp(), princomp(), FactoMineR::PCA(), ade4::dudi.pca() or MASS::lda() and can be customized using 'ggplot2' techniques.
Last updated 5 months ago
biplotdata-visualizationdimension-reductionprincipal-component-analysis
10 stars 2.33 score 28 dependenciesnestedLogit - Nested Dichotomy Logistic Regression Models
Provides functions for specifying and fitting nested dichotomy logistic regression models for a multi-category response and methods for summarising and plotting those models. Nested dichotomies are statistically independent, and hence provide an additive decomposition of tests for the overall 'polytomous' response. When the dichotomies make sense substantively, this method can be a simpler alternative to the standard 'multinomial' logistic model which compares response categories to a reference level. See: J. Fox (2016), "Applied Regression Analysis and Generalized Linear Models", 3rd Ed., ISBN 1452205663.
Last updated 3 months ago
logistic-regressionmultinomial-logistic-regressionpolytomous-variables
10 stars 1.67 score 66 dependenciesgenridge - Generalized Ridge Trace Plots for Ridge Regression
The genridge package introduces generalizations of the standard univariate ridge trace plot used in ridge regression and related methods. These graphical methods show both bias (actually, shrinkage) and precision, by plotting the covariance ellipsoids of the estimated coefficients, rather than just the estimates themselves. 2D and 3D plotting methods are provided, both in the space of the predictor variables and in the transformed space of the PCA/SVD of the predictors.
Last updated 7 months ago
bias-variancegraphicsprincipal-component-analysisregression-modelsridge-regressionsingular-value-decomposition
2 stars 0.94 score 82 dependenciesVisCollin - Visualizing Collinearity Diagnostics
Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009), "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>. These include better tabular presentation of collinearity diagnostics that highlight the important numbers, a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient, and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.
Last updated 7 months ago
biplotscollinearity-diagnosticsgraphicsregression-models
1 stars 0.92 score 0 dependenciesmvinfluence - Influence Measures and Diagnostic Plots for Multivariate Linear Models
Computes regression deletion diagnostics for multivariate linear models and provides some associated diagnostic plots. The diagnostic measures include hat-values (leverages), generalized Cook's distance, and generalized squared 'studentized' residuals. Several types of plots to detect influential observations are provided.
Last updated 2 years ago
multivariate-analysismultivariate-linear-regressionstatisticsvisualization
2 stars 0.89 score 83 dependencies