GLM Lasso or Elasticnet Model
Fit a generalized linear model via penalized maximum likelihood.
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")
)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 |
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
|
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. |
BinomialVariate, factor,
matrix, numeric, PoissonVariate, Surv
lambda, alpha
Default values for the NULL arguments and further model details can be
found in the source link below.
MLModel class object.
## Requires prior installation of suggested package glmnet to run fit(sale_amount ~ ., data = ICHomes, model = GLMNetModel(lambda = 0.01))
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