Functions and Data for a Course on Modern Regression and Classification

Functions and data are provided that support a course that emphasizes statistical issues of inference and generalizability. The functions are designed to make it straightforward to illustrate the use of cross-validation, the training/test approach, simulation, and model-based estimates of accuracy. Methods considered are Generalized Additive Modeling, Linear and Quadratic Discriminant Analysis, Tree-based methods, and Random Forests.


              Changes in gamclass version 0.57

Changes in vignettes
o Argument reg.line=NA, in calls to the car functions sp() and spm() has been changed to regLine=TRUE, as required in car versions >=3.0.0 . o An argument diag="boxplot", in figs5.Rnw in a call to car::spm(), becomes diagonal=list(method="boxplot"), as required in car versions >=3.0.0 .

              Changes in gamclass version 0.53

NEW FEATURES o Functions plotFars() an tabFarsDead() do not now have an argument 'data'. Instead, to deal with a "No visible binding" message, data('FARS', package='gamclass', envir=environment()) is used to place the dataset 'FARS' in the function environment, with FARS <- get("FARS", envir=environment()) used to appease the CRAN check. o The functions eventCounts() (counts of events by specified intervals), gamRF() (use repeated sampling to compare interpolation accuracy of GAM model + randomForests fit to residual), addhlines() (plot horizontal lines to show rpart fitted values) and bssBYcut() (between group SS for y, for all possible splits on a value of x) o Vignettes figs7 and figs8 are new. The former vignettes figs7:figs9 have become figs9:figs11 o The dataset airAccs has been added.

Reference manual

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0.62.3 by John Maindonald, a year ago

Browse source code at

Authors: John Maindonald

Documentation:   PDF Manual  

GPL (>= 2) license

Imports rpart, randomForest, lattice, latticeExtra, methods

Suggests leaps, quantreg, sp, diagram, oz, forecast, kernlab, Ecdat, mlbench, DAAGbio, car, mgcv, DAAG, MASS, ape, KernSmooth, knitr, prettydoc, rmarkdown, bookdown

See at CRAN