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