View source: R/ML_GLMNetModel.R
GLMNetModel | R Documentation |
Fit a generalized linear model via penalized maximum likelihood.
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")
)
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 argument values and further model details can be found in the source See Also link below.
MLModel
class object.
glmnet
, fit
,
resample
## Requires prior installation of suggested package glmnet to run
fit(sale_amount ~ ., data = ICHomes, model = GLMNetModel(lambda = 0.01))
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