# 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` ```r 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 ```r ## 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.

rlassologit function