One approach to this is generating a representative sequence of your
x-variable(s) with seq() or expand.grid(). Next use the predict()
function to make predictions from your glm object along the sequence.
Finally, plot the predictions vs. the new sequence. Putting everything
into a dataframe helps.
# generate some data:
x <- rnorm(10)
y <- x * 2 + rnorm(10, sd=0.5)
# plot
plot(y ~ x)
# generate linear model:
l <- lm(y ~ x)
# generate sequence along x-var
# and predictions
d <- data.frame(x.new=seq(from=-1.5, to=1.2, by=0.1))
d$y.new <- predict(l, data.frame(x=d$x.new))
# add to plot:
lines(y.new ~ x.new, data=d, lwd=2)
Cheers,
Dylan
On Sun, Jun 7, 2009 at 8:36 PM, Jo Stringer<jstrin...@bses.org.au> wrote:
> Hi!
>
>
> I have fitted two glms assuming a poisson distribution which are:
>
> fit1 <- glm(Aids ~ Year, data=aids, family=poisson())
>
> fit2 <- glm(Aids ~ Year+I(Year^2), data=aids, family=poisson())
>
>
> I am trying to work out how to represent the fitted regression curves of fit1
> and fit2 on the one graph. I have tried:
>
> graphics.off()
>
> plot(Aids ~ Year, data = aids)
>
>
> line(glm(Aids ~ Year, data=aids, family=poisson()))
> line(glm(Aids ~ Year+I(Year^2), data=aids, family=poisson()))
>
> but this does not work.
>
> Can anyone help me?
>
> Thanks
>
> Jo
>
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