

   PPrreeddiicctt MMeetthhoodd ffoorr GGLLMM FFiittss

        predict.glm(object, newdata=NULL, type=c("link", "response"),
                    se.fit=FALSE, dispersion=NULL, ...)

   AArrgguummeennttss::

     object: A fitted object of class inheriting from `"glm"'.

    newdata: Optionally, a new data frame from which to make
             the predictions. If omitted, the fitted linear
             predictors are used.

       type: The type of prediction required. The default is on
             the scale of the linear predictors; the alterna-
             tive is on the scale of the response variable.
             Thus for a default binomial model the default pre-
             dictions are of log-odds (probabilities on logit
             scale) and `type = "response"' gives the predicted
             probabilities.

             The value of this argument can be abbreviated.

     se.fit: A switch indicating if standard errors are
             required.

   dispersion: The dispersion of the GLM fit to be assumed in
             computing the standard errors. If omitted, that
             returned by `summary' applied to the object is
             used.

   DDeessccrriippttiioonn::

        Obtains predictions and optionally estimates standard
        errors of those predictions from a fitted generalized
        linear model object.

   VVaalluuee::

        If `se = FALSE', a vector of predictions. If `se =
        TRUE', a list with components

        fit: Predictions

     se.fit: Estimated standard errors

   residual.scale: A scalar giving the square root of the dis-
             persion used in computing the standard errors.

   NNoottee::

        This method is also currently used for objects of class
        `"survreg"' (parametric survival fits from package
        `survival4') and possibly others. The assumptions made
        by `predict.glm' may not always be right for such
        objects.

   AAuutthhoorr((ss))::

        B.D. Ripley

   SSeeee AAllssoo::

        `glm'

   EExxaammpplleess::

        ## example from Venables and Ripley (1997, pp. 231-3.)
        ldose <- rep(0:5, 2)
        numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
        sex <- factor(rep(c("M", "F"), c(6, 6)))
        SF <- cbind(numdead, numalive=20-numdead)
        budworm.lg <- glm(SF ~ sex*ldose, family=binomial)
        summary(budworm.lg)

        plot(c(1,32), c(0,1), type="n", xlab="dose",
           ylab="prob", log="x")
        text(2^ldose, numdead/20,as.character(sex))
        ld <- seq(0, 5, 0.1)
        lines(2^ld, predict(budworm.lg, data.frame(ldose=ld,
           sex=factor(rep("M", length(ld)), levels=levels(sex))),
           type="response"))
        lines(2^ld, predict(budworm.lg, data.frame(ldose=ld,
           sex=factor(rep("F", length(ld)), levels=levels(sex))),
           type="response"))

