#' Summarize Rank-Based Linear Model Fits
#'
#' Provides a summary similar to the traditional least squares fit.
#'
#'
#' @param object an object of class 'rfit', usually, a result of a call to
#' 'rfit'
#' @param \dots additional arguments
#' @author John Kloke
#' @references Hettmansperger, T.P. and McKean J.W. (2011), \emph{Robust
#' Nonparametric Statistical Methods, 2nd ed.}, New York: Chapman-Hall.
#' @examples
#'
#' data(baseball)
#' fit<-rfit(weight~height,data=baseball)
#' summary(fit)
#'
#' @export summary.rfit
summary.rfit <- function (object,overall.test='drop',...) {
tauhat <- object$tauhat
n<-length(object$y)
pp1 <- object$qrx1$rank
est <- object$coef
ses <- sqrt(diag(vcov(object)))
tstat <- est/ses
pval <- 2 * pt(-abs(tstat), n - pp1)
coef <- cbind(est, ses, tstat, pval)
colnames(coef) <- c("Estimate", "Std. Error", "t.value","p.value")
ans <- list(coefficients = coef)
if( overall.test == 'all' || overall.test == 'wald' ) {
wt <- wald.test.overall(object)
ans <- utils::modifyList(ans,list(waldstat = wt$F, waldpval = wt$p.value))
}
if( overall.test == 'all' || overall.test == 'drop') {
dt <- drop.test(object)
R2 <- (dt$df1/dt$df2 * dt$F)/(1 + dt$df1/dt$df2 * dt$F)
ans <- utils::modifyList(ans,list(dropstat = dt$F, droppval = dt$p.value, R2 = R2))
}
ans$overall.test <- overall.test
ans$call <- object$call
class(ans) <- "summary.rfit"
ans
}
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