mosaicModel {mosaicModel} | R Documentation |
mosaicModel
package
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
orglm
provided by thestats
package-
train
from thecaret
package for machine learning -
rpart
,randomForest
,rlm
, and other functions provided by other packages