GLMNetModel: GLM Lasso or Elasticnet Model

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

GLM Lasso or Elasticnet Model

Description

Fit a generalized linear model via penalized maximum likelihood.

Usage

GLMNetModel(
  family = NULL,
  alpha = 1,
  lambda = 0,
  standardize = TRUE,
  intercept = logical(),
  penalty.factor = .(rep(1, nvars)),
  standardize.response = FALSE,
  thresh = 1e-07,
  maxit = 1e+05,
  type.gaussian = .(if (nvars < 500) "covariance" else "naive"),
  type.logistic = c("Newton", "modified.Newton"),
  type.multinomial = c("ungrouped", "grouped")
)

Arguments

family	optional response type. Set automatically according to the class type of the response variable.
alpha	elasticnet mixing parameter.
lambda	regularization parameter. The default value lambda = 0 performs no regularization and should be increased to avoid model fitting issues if the number of predictor variables is greater than the number of observations.
standardize	logical flag for predictor variable standardization, prior to model fitting.
intercept	logical indicating whether to fit intercepts.
penalty.factor	vector of penalty factors to be applied to each coefficient.
standardize.response	logical indicating whether to standardize "mgaussian" response variables.
thresh	convergence threshold for coordinate descent.
maxit	maximum number of passes over the data for all lambda values.
type.gaussian	algorithm type for guassian models.
type.logistic	algorithm type for logistic models.
type.multinomial	algorithm type for multinomial models.

Details

Response types:

BinomialVariate, factor, matrix, numeric, PoissonVariate, Surv

Automatic tuning of grid parameters:

lambda, alpha

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

Value

MLModel class object.

See Also

glmnet, fit, resample

Examples

## Requires prior installation of suggested package glmnet to run

fit(sale_amount ~ ., data = ICHomes, model = GLMNetModel(lambda = 0.01))

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