EarthModel: Multivariate Adaptive Regression Splines Model

In brian-j-smith/MachineShop: Machine Learning Models and Tools

Multivariate Adaptive Regression Splines Model

Description

Build a regression model using the techniques in Friedman's papers "Multivariate Adaptive Regression Splines" and "Fast MARS".

Usage

EarthModel(
  pmethod = c("backward", "none", "exhaustive", "forward", "seqrep", "cv"),
  trace = 0,
  degree = 1,
  nprune = integer(),
  nfold = 0,
  ncross = 1,
  stratify = TRUE
)

Arguments

pmethod	pruning method.
trace	level of execution information to display.
degree	maximum degree of interaction.
nprune	maximum number of terms (including intercept) in the pruned model.
nfold	number of cross-validation folds.
ncross	number of cross-validations if nfold > 1.
stratify	logical indicating whether to stratify cross-validation samples by the response levels.

Details

Response types:

factor, numeric

Automatic tuning of grid parameters:

nprune, degree*

* excluded from grids by default

Default argument values and further model details can be found in the source See Also link below.

In calls to varimp for EarthModel, argument type may be specified as "nsubsets" (default) for the number of model subsets that include each predictor, as "gcv" for the generalized cross-validation decrease over all subsets that include each predictor, or as "rss" for the residual sums of squares decrease. Variable importance is automatically scaled to range from 0 to 100. To obtain unscaled importance values, set scale = FALSE. See example below.

Value

MLModel class object.

See Also

earth, fit, resample

Examples

## Requires prior installation of suggested package earth to run

model_fit <- fit(Species ~ ., data = iris, model = EarthModel)
varimp(model_fit, method = "model", type = "gcv", scale = FALSE)

brian-j-smith/MachineShop documentation built on June 12, 2025, 3:52 a.m.