rlassologit function

rlassologit: Function for logistic Lasso estimation

rlassologit: Function for logistic Lasso estimation

The function estimates the coefficients of a logistic Lasso regression with data-driven penalty. The method of the data-driven penalty can be chosen. The object which is returned is of the S3 class rlassologit

rlassologit(x, ...) ## S3 method for class 'formula' rlassologit( formula, data = NULL, post = TRUE, intercept = TRUE, model = TRUE, penalty = list(lambda = NULL, c = 1.1, gamma = 0.1/log(n)), control = list(threshold = NULL), ... ) ## S3 method for class 'character' rlassologit( x, data = NULL, post = TRUE, intercept = TRUE, model = TRUE, penalty = list(lambda = NULL, c = 1.1, gamma = 0.1/log(n)), control = list(threshold = NULL), ... ) ## Default S3 method: rlassologit( x, y, post = TRUE, intercept = TRUE, model = TRUE, penalty = list(lambda = NULL, c = 1.1, gamma = 0.1/log(n)), control = list(threshold = NULL), ... )

Arguments

  • x: regressors (matrix)
  • ...: further parameters passed to glmnet
  • formula: an object of class 'formula' (or one that can be coerced to that class): a symbolic description of the model to be fitted in the form y~x.
  • data: an optional data frame, list or environment.
  • post: logical. If TRUE, post-lasso estimation is conducted.
  • intercept: logical. If TRUE, intercept is included which is not penalized.
  • model: logical. If TRUE (default), model matrix is returned.
  • penalty: list with options for the calculation of the penalty. c and gamma constants for the penalty.
  • control: list with control values. threshold is applied to the final estimated lasso coefficients. Absolute values below the threshold are set to zero.
  • y: dependent variable (vector or matrix)

Returns

rlassologit returns an object of class rlassologit. An object of class rlassologit is a list containing at least the following components: - coefficients: parameter estimates - beta: parameter estimates (without intercept) - intercept: value of intercept - index: index of selected variables (logicals)

  • lambda: penalty term

  • residuals: residuals

  • sigma: root of the variance of the residuals

  • call: function call

  • options: options

Details

The function estimates the coefficients of a Logistic Lasso regression with data-driven penalty. The option post=TRUE conducts post-lasso estimation, i.e. a refit of the model with the selected variables.

Examples

## Not run: library(hdm) ## DGP set.seed(2) n <- 250 p <- 100 px <- 10 X <- matrix(rnorm(n*p), ncol=p) beta <- c(rep(2,px), rep(0,p-px)) intercept <- 1 P <- exp(intercept + X %*% beta)/(1+exp(intercept + X %*% beta)) y <- rbinom(length(y), size=1, prob=P) ## fit rlassologit object rlassologit.reg <- rlassologit(y~X) ## methods summary(rlassologit.reg, all=F) print(rlassologit.reg) predict(rlassologit.reg, type='response') X3 <- matrix(rnorm(n*p), ncol=p) predict(rlassologit.reg, newdata=X3) ## End(Not run)

References

Belloni, A., Chernozhukov and Y. Wei (2013). Honest confidence regions for logistic regression with a large number of controls. arXiv preprint arXiv:1304.3969.

  • Maintainer: Martin Spindler
  • License: MIT + file LICENSE
  • Last published: 2024-02-14

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