The “glmnet” Function in R

• Package: glmnet

• Purpose: Fit a generalized linear model via penalized maximum likelihood.

• General class: Regression

• Required argument(s):

• x: A matrix of predictors.

• y: A response variable.

• Notable optional arguments:

• family: The response type. Can be “gaussian” (default), “binomial”, “poisson”, “multinomial”, “cox”, “mgaussian”.

• alpha: The elastic net mixing parameter. The default is 1 (lasso).

• lambda: A user-specified sequence of lambda values.

• standardize: Logical value indicating whether to standardize the predictors. The default is TRUE.

• Example:

• # Load the required library
  library(glmnet)
  
  # Generate example data
  set.seed(123)
  x <- matrix(rnorm(100*20), 100, 20)
  y <- rnorm(100)
  
  # Fit a lasso model
  fit <- glmnet(x, y, alpha = 1)
  
  # Print the model
  print(fit)

• In this example, the glmnet function from the glmnet package is used to fit a lasso model to the example data. The matrix x contains the predictors, and the vector y contains the response variable. The alpha parameter is set to 1 to fit a lasso model. The resulting fit object contains the fitted model.