Functions for teaching about modeling.
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