MachineShop (version 2.0.0)

GLMNetModel: 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 = NULL,
  penalty.factor = .(rep(1, nvars)),
  standardize.response = FALSE,
  thresh = 1e-07,
  maxit = 1e+05,
  type.gaussian = .(ifelse(nvars < 500, "covariance", "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.

Value

MLModel class object.

Details

Response Types:

factor, matrix, numeric, Surv

Automatic Tuning of Grid Parameters:

lambda, alpha

Default values for the NULL arguments and further model details can be found in the source link below.

See Also

glmnet, fit, resample

Examples

# NOT RUN {
fit(sale_amount ~ ., data = ICHomes, model = GLMNetModel(lambda = 0.01))

# }