% Generated by roxygen2: do not edit by hand % Please edit documentation in R/KW.R name{rcbr.fit.KW1} alias{rcbr.fit.KW1} title{NPMLE fitting for random coefficient binary response model} usage{ rcbr.fit.KW1(x, y, control) } description{ Exact NPMLE fitting requires that the code{uv} argument contain a matrix
whose rows represent points in the interior of the locally maximal polytopes determined by the hyperplane arrangement of the observations. If it is not provided it will be computed afresh here; since this can be somewhat time consuming, \code{uv} is included in the returned object so that it can be reused if desired. Approximate NPMLE fitting can be achieved by specifying an equally spaced grid of points at which the NPMLE can assign mass using the arguments \code{u} and \code{v}. If the design matrix \code{X} contains only 2 columns, so we have the Cosslett, aka current status, model then the polygons in the prior description collapse to intervals and the default method computes the locally maximal count intervals and passes their interior points to the optimizer of the log likelihood. Alternatively, as in the bivariate case one can specify a grid to obtain an approximate solution.
} details{ @param X the design matrix expected to have an intercept column of
ones as the first column, the last column is presumed to contain values of the covariate that is designated to have coefficient one. @param y the binary response. @param u grid values for the intercept dimension of the random coefficients @param v grid values for the intercept dimension of the random coefficients @param uv evaluation points for the mass to be assigned by the NPMLE,typically the witness points of the locally maximal polytopes,as described in Gu and Koenker (2018). @param control is a list of parameters for the fitting, see \code{KW.control} for further details. @return a list with components: \itemize{ \item uv evaluation points for the fitted distribution \item W estimated mass associated with the \code{uv} points \item logLik the loglikelihood value of the fit \item status mosek solution status } @author Jiaying Gu and Roger Koenker @references Gu, J. and R. Koenker (2018) Nonparametric maximum likelihood estimation of the random coefficients binary choice model, preprint. @keywords nonparametrics importFrom Matrix Rmosek @export
}