Fits a logistic regression/RSF using glmnet

Function with similar name

Usage

net_logit(
  f,
  data,
  alpha = 1,
  penalty.factor = NULL,
  type.measure = "deviance",
  standardize = TRUE,
  na.action = "na.pass",
  func = c("glmnet", "cv.glmnet")[1],
  ...
)

net_rsf(
  f,
  data,
  alpha = 1,
  penalty.factor = NULL,
  type.measure = "deviance",
  standardize = TRUE,
  na.action = "na.pass",
  func = c("glmnet", "cv.glmnet")[1],
  ...
)

Arguments

f

[formula]
Formula of the model to be fitted, with all possible candidate terms.

data

[data.frame,tibble]
Complete data set to be analyzed.#' @param alpha Default is L1-regularization (Lasso regression), with alpha = 1. L2-regularization (Ridge regression) is done with alpha = 0, and elastic-net regression is performed for any alpha value between 0 and 1. For more details, see the glmnet::glmnet() documentation. For Adaptive and Decay Adaptive Lasso, keep alpha = 1.

penalty.factor

[numeric,vector=NULL]
Vector of penalty factors to be used for Adaptive Lasso fitting. The vector might have the same length as the the number of columns given by the model matrix, model.matrix(f, data). Default is NULL, in case the same penalty is applied to all variables.

type.measure

[character(1)="deviance"]
Type of measure to evaluate the model internally in glmnet::glmnet(). For logistic and conditional logistic regression, it is by default "deviance".

standardize

[logical(1)=TRUE]
Whether the call to glmnet should include internal standardization of variables or not. Default is TRUE. It should be set to FALSE if the variables are already standardized.

na.action

[character(1)="na.pass"]
Default is "na.pass", i.e. rows with NAs are not automatically removed from the model.matrix used for fitting.

func

[character(1)="glmnet"]{"glmnet", "cv.glmnet"}
The function to be used for fitting. Default is glmnet::glmnet(). The second option is glmnet::cv.glmnet() which already performs the cross-validation and might include the variable selection/callibration within.

Check option parallel = TRUE from glmnet.