Bianco-Yohai Estimator for Robust Logistic Regression
Computation of the estimator of Bianco and Yohai (1996) in logistic regression. Now provides both the weighted and regular (unweighted) BY-estimator.
By default, an intercept term is included and p parameters are estimated. For more details, see the reference.
Note: This function is for “back-compatibility” with the
BYlogreg()
code web-published at KU Leuven, Belgium,
and also available as file ‘FunctionsRob/BYlogreg.ssc’ from
https://www.wiley.com/legacy/wileychi/robust_statistics/robust.html.
BYlogreg(x0, y, initwml = TRUE, addIntercept = TRUE, const = 0.5, kmax = 1000, maxhalf = 10, sigma.min = 1e-4, trace.lev = 0)
x0 |
a numeric n * (p-1) matrix containing the explanatory variables. |
y |
numeric n-vector of binomial (0 - 1) responses. |
initwml |
logical for selecting one of the two possible methods for computing the initial value of the optimization process. If If |
addIntercept |
logical indicating that a column of |
const |
tuning constant used in the computation of the estimator (default=0.5). |
kmax |
maximum number of iterations before convergence (default=1000). |
maxhalf |
max number of step-halving (default=10). |
sigma.min |
smallest value of the scale parameter before implosion (and hence non-convergence) is assumed. |
trace.lev |
logical (or integer) indicating if intermediate results
should be printed; defaults to |
a list with components
convergence |
logical indicating if convergence was achieved |
objective |
the value of the objective function at the minimum |
coefficients |
vector of parameter estimates |
vcov |
variance-covariance matrix of the coefficients (if convergence is TRUE). |
sterror |
standard errors, i.e., simply |
Originally, Christophe Croux and Gentiane Haesbroeck, with thanks to Kristel Joossens and Valentin Todorov for improvements.
Speedup, tweaks, more “control” arguments: Martin Maechler.
Croux, C., and Haesbroeck, G. (2003) Implementing the Bianco and Yohai estimator for Logistic Regression, Computational Statistics and Data Analysis 44, 273–295.
Ana M. Bianco and Víctor J. Yohai (1996) Robust estimation in the logistic regression model. In Helmut Rieder, Robust Statistics, Data Analysis, and Computer Intensive Methods, Lecture Notes in Statistics 109, pages 17–34.
set.seed(17) x0 <- matrix(rnorm(100,1)) y <- rbinom(100, size=1, prob= 0.5) # ~= as.numeric(runif(100) > 0.5) BY <- BYlogreg(x0,y) BY <- BYlogreg(x0,y, trace.lev=TRUE) ## The "Vaso Constriction" aka "skin" data: data(vaso) vX <- model.matrix( ~ log(Volume) + log(Rate), data=vaso) vY <- vaso[,"Y"] head(cbind(vX, vY))# 'X' does include the intercept vWBY <- BYlogreg(x0 = vX, y = vY, addIntercept=FALSE) # as 'vX' has it already v.BY <- BYlogreg(x0 = vX, y = vY, addIntercept=FALSE, initwml=FALSE) ## they are relatively close: stopifnot( all.equal(vWBY, v.BY, tolerance = 2e-4) )
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