I imitated predict.glm, my thing worked, now I need to revise.  It would
help me very much if someone would explain predict.glm line 28, which says

    object$na.action <- NULL # kill this for predict.lm calls

I want to know

1) why does it set the object$na.action to NULL
2) what does the comment after mean?

Maybe I need a pass by value lesson too, because I can't see how changing
that in object would have any effect on calculations done elsewhere.

pj


Here's the context from predict.glm, to save you trouble of looking it up:

predict.glm <-
  function(object, newdata = NULL, type = c("link", "response", "terms"),
           se.fit = FALSE, dispersion = NULL, terms = NULL,
           na.action = na.pass, ...)
{
    ## 1998/06/23 KH:  predict.lm() now merged with the version in lm.R

    type <- match.arg(type)
    na.act <- object$na.action
    object$na.action <- NULL # kill this for predict.lm calls
    if (!se.fit) {
    ## No standard errors
    if(missing(newdata)) {
        pred <- switch(type,
               link = object$linear.predictors,
               response = object$fitted.values,
                           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)$linkinv(pred)},
           link = , terms = )
          }
    } else {
    ## summary.survreg has no ... argument.
    if(inherits(object, "survreg")) dispersion <- 1.
    if(is.null(dispersion) || dispersion == 0)
        dispersion <- summary(object, dispersion=dispersion)$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)
        }
    pred <- list(fit = fit, se.fit = se.fit, residual.scale =
residual.scale)
    }
    pred
}



-- 
Paul E. Johnson
Professor, Political Science      Assoc. Director
1541 Lilac Lane, Room 504      Center for Research Methods
University of Kansas                 University of Kansas
http://pj.freefaculty.org               http://quant.ku.edu

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