> -----Original Message-----
> From: r-help-boun...@r-project.org
> [mailto:r-help-boun...@r-project.org] On Behalf Of Sam Steingold
>
> plot.glm <- function (x, y, file = NULL, xlab =
> deparse(substitute(x)),
> ylab = deparse(substitute(y)), main = NULL) {
> m <- glm(y ~ x)
> if (!is.null(file))
> pdf(file = file)
> plot(x, y, xlab = xlab, ylab = ylab, main = main)
> lines(x, y = m$fitted.values, col = "green")
> if (!is.null(file))
> dev.off()
> print(m)
> }
>
> is there a better/easier/more general way?
There is no guarantee that x is sorted in ascending order for a glm (or any
other model), so lines(x, fitted()) can give very spiny results. Even if
sorted, non-linear fits with large gaps in x will not give smooth lines.
Better to use something along the lines of the budworm example in the glm help
page, which uses predict() on a new sequence.
If you want something a bit more general, you can use either range(x) to get
the new sequence limits or par("usr")[1:2] to gets the current plot x limits.
S Ellison
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