LARSModel: Least Angle Regression, Lasso and Infinitesimal Forward...

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

Least Angle Regression, Lasso and Infinitesimal Forward Stagewise Models

Description

Fit variants of Lasso, and provide the entire sequence of coefficients and fits, starting from zero to the least squares fit.

Usage

LARSModel(
  type = c("lasso", "lar", "forward.stagewise", "stepwise"),
  trace = FALSE,
  normalize = TRUE,
  intercept = TRUE,
  step = numeric(),
  use.Gram = TRUE
)

Arguments

type	model type.
trace	logical indicating whether status information is printed during the fitting process.
normalize	whether to standardize each variable to have unit L2 norm.
intercept	whether to include an intercept in the model.
step	algorithm step number to use for prediction. May be a decimal number indicating a fractional distance between steps. If specified, the maximum number of algorithm steps will be ceiling(step); otherwise, step will be set equal to the source package default maximum [default: max.steps].
use.Gram	whether to precompute the Gram matrix.

Details

Response types:

numeric

Automatic tuning of grid parameter:

step

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

Value

MLModel class object.

See Also

lars, fit, resample

Examples

## Requires prior installation of suggested package lars to run

fit(sale_amount ~ ., data = ICHomes, model = LARSModel)

brian-j-smith/MachineShop documentation built on Sept. 22, 2023, 10:01 p.m.