% Generated by roxygen2: do not edit by hand % Please edit documentation in R/rcbr.R name{rcbr} alias{rcbr} title{Estimation of Random Coefficient Binary Response Models} usage{ rcbr(formula, data, subset, offset, method = “GK”, …) } description{ Two methods are implemented for estimating binary response models
with random coefficients: A nonparametric maximum likelihood method proposed by Cosslett (1986) and extended by Ichimura and Thompson (1998), and a (hemispherical) deconvolution method proposed by Gautier and and Kitamura (2013). The former is closely related to the NPMLE for mixture models of Kiefer and Wolfowitz (1956). The latter is an R translation of the matlab implementation of Gautier and Kitamura.
} details{ The code{predict} method produces estimates of the probability of a “success”
(y = 1) for a particular vector, \code{(z,v)}, when aggregated over the estimated distribution of random coefficients.
The code{logLik} produces an evaluation of the log likelihood value
associated with a fitted model.
@param formula an expression of the generic form code{y ~ z + v} where
\code{y} is the observed binary response, \code{z} is an observed covariate with a random coefficient, and \code{v} is an observed covariate with coefficient normalize to be one. If \code{z} is not present then the model has only a random "intercept" coefficient and thus corresponds to the basic model of Cosslett (1983); this model is also referred to as the current status model in the biostatistics literature, see Groeneboom and Hendrikx (2016). When \code{z} is present there are random coefficients associated with both the intercept and \code{z}. @param data is a \code{data.frame} containing the data referenced in the formula. @param subset specifies a subsample of the data used for fitting the model @param offset specifies a fixed shift in \code{v} representing the potential effect of other covariates having fixed coefficients that may be useful for profile likelihood computations. (Should be vector of the same length as \code{v}. @param method controls whether the Gautier and Kitamura, "GK", or Kiefer and Wolfowitz, "KW" methods are used. @param ... miscellaneous other arguments to control fitting. See \code{GK.control} and \code{KW.control} for further details.
@return of object of class code{GK}, code{KW1}, with components described in
further detail in the respective fitting functions.
@author Jiaying Gu and Roger Koenker
@references Kiefer, J. and J. Wolfowitz (1956) Consistency of the Maximum
Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters, \emph{Ann. Math. Statist}, 27, 887-906.
Cosslett, S. (1983) Distribution Free Maximum Likelihood Estimator of the
Binary Choice Model, \emph{Econometrica}, 51, 765-782.
Gautier, E. and Y. Kitamura (2013) Nonparametric estimation in random coefficients
binary choice models, \emph{Ecoonmetrica}, 81, 581-607.
Groeneboom, P. and K. Hendrickx (2016) Current Status Linear Regression,
preprint available from \url{https://arxiv.org/abs/1601.00202}.
Ichimuma, H. and T. S. Thompson, (1998) Maximum likelihood estimation of a binary
choice model with random coefficients of unknown distribution," \emph{Journal of Econometrics}, 86, 269-295.
} keyword{nonparametric}