A suggestion for predict function(s)

I must respectfully disagree.  Why carry extra copies of data arround?  This
is probably OK for small to medium sized data, but definitely not for large
data.

Besides, in your example, it may do different things depending on whether
newdata is supplied:  model.matrix is not necessarily the same as the
original data frame.  You need a bit more work to get the right model.matrix
that correspond to the newdata.  It's not clear to me whether you want to
return model matrix or model frame, but in either case it's not sufficient
to just use `newdata'.

Andy

From: Ross Darnell

Maybe a useful addition to the predict functions would be to 
return the 
values of the predictor variables. It just (unless there are 
problems) 
requires an extra line. I have inserted an example below.

"predict.glm" <-
  function (object, newdata = NULL, type = c("link", "response",
                                      "terms"), se.fit = FALSE, 
dispersion = NULL, terms = NULL,
            na.action = na.pass, ...)
{
  type <- match.arg(type)
  na.act <- object$na.action
  object$na.action <- NULL
  if (!se.fit) {
    if (missing(newdata)) {
      pred <- switch(type, link = object$linear.predictors,
                     response = object$fitted, terms = 
predict.lm(object,
                                                 se.fit = 
se.fit, scale 
= 1, type = "terms",
                                                 terms = terms))
      if (!is.null(na.act))
        pred <- napredict(na.act, pred)
    }
    else {
      pred <- predict.lm(object, newdata, se.fit, scale = 1,
                         type = ifelse(type == "link", 
"response", type),
                         terms = terms, na.action = na.action)
      switch(type, response = {
        pred <- family(object)$linkinv(pred)
      }, link = , terms = )
    }
  }
  else {
    if (inherits(object, "survreg"))
      dispersion <- 1
    if (is.null(dispersion) || dispersion == 0)
      dispersion <- summary(object, dispersion = 
dispersion)$dispersion
    residual.scale <- as.vector(sqrt(dispersion))
    pred <- predict.lm(object, newdata, se.fit, scale = 
residual.scale,
                       type = ifelse(type == "link", 
"response", type),
                       terms = terms, na.action = na.action)
    fit <- pred$fit
    se.fit <- pred$se.fit
    switch(type, response = {
      se.fit <- se.fit * abs(family(object)$mu.eta(fit))
      fit <- family(object)$linkinv(fit)
    }, link = , terms = )
    if (missing(newdata) && !is.null(na.act)) {
      fit <- napredict(na.act, fit)
      se.fit <- napredict(na.act, se.fit)
    }
    predictors <- if (missing(newdata)) model.matrix(object) 
else newdata
    pred <- list(predictors=predictors,
                 fit = fit, se.fit = se.fit,
                 residual.scale = residual.scale)
  }
  pred


#______________________ end of R code



Ross Darnell
-- 
School of Health and Rehabilitation Sciences
University of Queensland, Brisbane QLD 4072 AUSTRALIA
Email: <r.darnell at uq.edu.au>
Phone: +61 7 3365 6087     Fax: +61 7 3365 4754  Room:822, 
Therapies Bldg.
http://www.shrs.uq.edu.au/shrs/school_staff/ross_darnell.html

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