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#' @author
#' The method for \code{glm} objects is adapted
#' from \href{http://www.win-vector.com/blog/2014/05/trimming-the-fat-from-glm-models-in-r/}{Nina Zumel's post}
#' on Win-Vector Blog.
#'
#' @export
#' @rdname strip
#'
strip_.glm <-
function(object,
keep,
...)
{
keep <- match.arg(tolower(keep),
c("everything", "predict", "print"),
several.ok = TRUE)
cl <- class(object)
ca <- object$call
if ("everything" %in% keep) {
return(object)
}
if ("predict" %in% keep) {
op <- object
op$y <- NULL
op$model <- NULL
op$residuals <- NULL
op$fitted.values <- NULL
op$effects <- NULL
op$qr$qr <- NULL
op$linear.predictors <- NULL
op$weights <- NULL
op$prior.weights <- NULL
op$data <- NULL
op$family$variance <- NULL
op$family$dev.resids <- NULL
op$family$aic <- NULL
op$family$validmu <- NULL
op$family$simulate <- NULL
attr(op$terms,".Environment") <- NULL
attr(op$formula,".Environment") <- NULL
} else {
op <- list(call = ca)
}
if ("print" %in% keep) {
oq <- list(call = ca,
coefficients = object$coefficients,
contrasts = object$contrasts,
df.null = object$df.null,
df.residual = object$df.residual,
na.action = object$na.action,
null.deviance = object$null.deviance,
deviance = object$deviance,
aic = object$aic)
} else {
oq <- list(call = ca)
}
object <- rlist::list.merge(op, oq)
class(object) <- cl
object
}
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