GLM with elastic-net

Usage

renoir_glmnet(
  x,
  y,
  weights = NULL,
  offset = NULL,
  clean,
  keep.call,
  alpha = 1,
  relax = F,
  gamma = 1,
  resp.type,
  observations = NULL,
  features = NULL,
  learning.method,
  ...
)

Arguments

x: the input matrix, where rows are observations and columns are variables
y: the response variable. Its number of rows must match the number of rows of x
weights: priors of the observations
offset: used only for GLM methods, it is an a priori known component to be included in the linear predictor during fitting
alpha: The elasticnet mixing parameter, with $0\le\alpha\le 1$. The penalty is defined as $$(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1.$$ alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.
relax: If TRUE then for each active set in the path of solutions, the model is refit without any regularization. See details for more information. This argument is new, and users may experience convergence issues with small datasets, especially with non-gaussian families. Limiting the value of 'maxp' can alleviate these issues in some cases.
gamma: dummy variable, not used in training but set in config
resp.type: the response type
observations: indices of observations to keep
features: indices of predictors to keep
...: further arguments to glmnet

Value

An object of class Trained

Author

Alessandro Barberis