mosaicModel: 'mosaicModel' package

In mosaicModel: An Interface to Statistical Modeling Independent of Model Architecture

Description

Functions for teaching about modeling.

Details

The package offers a handful of high-level functions for evaluating, displaying, and interpreting models that work in a consistent way across model architectures, e.g. lm, glm, rpart, randomForest, knn3, caret-train, and so on.

• mod_eval() – evaluate a model, that is, turn inputs into model values. For many model architectures, you can also get prediction or confidence intervals on the outputs.

• mod_plot() – produce a graphical display of the "shape" of a model. There can be as many as 4 input variables shown, along with the output.

• mod_effect() – calculate effect sizes, that is, how a change in an input variable changes the output

• mod_error() – find the mean square prediction error (or the log likelihood)

• mod_ensemble() – create an ensemble of bootstrap replications of the model, that is, models fit to resampled data from the original model.

• mod_cv() – carry out cross validation on one or more models.

• mod_fun() – extract a function from a model that implements the inputs-to-output relationship. mosaicModel stays out of the business of training models. You do that using functions, e.g.

• the familiar lm or glm provided by the stats package

• train from the caret package for machine learning

• rpart, randomForest, rlm, and other functions provided by other packages

mosaicModel documentation built on May 2, 2019, 7:59 a.m.