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#' GWRM Predictions
#'
#' Obtains predictions from a fitted GWRM object.
#'
#' @param object a fitted object of class inheriting from \code{"gw"}.
#' @param newdata optionally, a data frame in which to look for variables with which to predict. If omitted, the fitted linear predictors are used.
#' @param ... further arguments passed to or from other methods.
#'
#' @return A data frame with newdata and their fitted means.
#'
#' @importFrom stats delete.response .checkMFClasses
#'
#' @examples
#' data(goals)
#' fit <- gw(goals ~ position, data = goals)
#' predict(fit)
#'
#' @export
predict.gw <- function(object = NULL, newdata = NULL, ...){
tt <- terms(object)
if (!inherits(object, "gw"))
warning("calling predict.gw(<fake-gw-object>) ...")
if (missing(newdata) || is.null(newdata)) {
mm <- X <- model.matrix(object)
mmDone <- TRUE
offset <- object$offset
}
else {
Terms <- delete.response(tt)
m <- model.frame(Terms, newdata, xlev = object$xlevels)
if (!is.null(cl <- attr(Terms, "dataClasses")))
.checkMFClasses(cl, m)
nobs<-nrow(as.matrix(m))
X <- model.matrix(Terms, m, offset <- rep(0, nobs))
if (!is.null(off.num <- attr(tt, "offset")))
for (i in off.num) offset <- offset + eval(attr(tt, "variables")[[i + 1]], newdata)
if (!is.null(object$call$offset))
offset <- offset + eval(object$call$offset, newdata)
mmDone <- FALSE
}
ncovars <- ncol(X)
beta <- object$coefficients[1:(ncovars)]
if(!object$kBool){
k <- object$betaIIpars[1]
ro <- object$betaIIpars[2]
}
else{
k <- object$k
ro <- object$betaIIpars[2]
}
if (is.null(offset))
fits <- exp(X %*% beta)
else
fits <- exp(offset + X %*% beta)
predictor <- cbind(fits)
colnames(predictor) <- c("fit")
ans <- data.frame(predictor)
return(ans)
}
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