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 = 1e07, 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 values and further model details can be found in the source 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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