rlogit: rlogit

In aucm: AUC Maximization

Description

Robust logistic regression estimator of Bianco and Yohai

Usage

rlogit (formula, dat, const=0.5, kmax=1e3, maxhalf=10, verbose=FALSE)
## S3 method for class 'rlogit'
coef(object,...)
## S3 method for class 'rlogit'
trainauc(fit, training.data=NULL, ...)
## S3 method for class 'rlogit'
predict(object, newdata, ...)
## S3 method for class 'rlogit'
ratio(fit)
logistic.f(eta,h,loss=TRUE)

Arguments

formula	a formula specifying the model to be fit.
dat	a data frame containing the outcome and covariates in the model
const	tuning constant used in the computation of the estimator, defaults to 0.5
kmax	maximum number of iterations before convergence, defaults to 1000
maxhalf	max number of step-halving ,defaults to 10
verbose	logical
object	an object of class 'rlogit'
fit	an object that inherits from class 'auc' such as 'rauc' or 'sauc'
newdata	data at which to predict
training.data	data frame used to compute auc based on a fit obtained by a call to rauc, sauc or sauc.dca
eta,h	logistic.f computes for loss = FALSE expit(eta/h) or expit(-eta/h) for loss = TRUE
loss	a boolean. if TRUE (default) logistic loss is assumed.
...	arguments passed to or from methods

Details

This program computes the estimator of Bianco and Yohai (1996) in logistic regression. By default, an intercept term is included and p parameters are estimated. The outcome is coded as a 0/1 binomial variable.

If initwml == TRUE, a weighted ML estimator is computed with weights derived from the MCD estimator computed on the explanatory variables. If initwml == FALSE, a classical ML fit is perfomed. When the explanatory variables contain binary observations, it is recommended to set initwml to FALSE or to modify the code of the algorithm to compute the weights only on the continuous variables.

Value

A list with the follwoing components:

convergence	logical, was convergence achieved
objective	value of the objective function at the minimum
coef	estimates for the parameters
sterror	standard errors of the parameters (if convergence is TRUE)

Author(s)

Christophe Croux, Gentiane Haesbroeck. Thanks to Kristel Joossens and Valentin Todorov for improving the code.

References

Implementing the Bianco and Yohai estimator for Logistic Regression
Croux, C., and Haesbroeck, G. (2003)
Computational Statistics and Data Analysis, 44, 273-295

Examples

set.seed(1)
x0 <- matrix(rnorm(100,1))
y  <- as.numeric(runif(100)>0.5)        # numeric(runif(100)>0.5)
dat=data.frame(y=y, x=x0)
rlogit(y~x, dat)

aucm documentation built on Jan. 11, 2020, 9:43 a.m.