rlassologit {hdm} | R Documentation |
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), ...)
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
|
data |
an optional data frame, list or environment. |
post |
logical. If |
intercept |
logical. If |
model |
logical. If |
penalty |
list with options for the calculation of the penalty. |
control |
list with control values.
|
y |
dependent variable (vector or matrix) |
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.
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 |
Belloni, A., Chernozhukov and Y. Wei (2013). Honest confidence regions for logistic regression with a large number of controls. arXiv preprint arXiv:1304.3969.
## 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)