On Nov 20, 2010, at 4:27 AM, Sonja Klein wrote:


I'm very new to R and modeling but need some help with visualization of glms.

I'd like to make a graph of my glms to visualize the different effects of
different parameters.
I've got a binary response variable (bird sightings) and use binomial glms. The 'main' response variable is a measure of distance to a track and the
parameters I'm testing for are vegetation parameters that effect the
response in terms of distance.
My glm is: glm(Response~NEdist+I(NEdist^2)+Distance+I(Distance^2) which is the basic model and where I add interactions to, like for exampls Visibility
as an interaction to Distance
(glm(Response~NEdist+I(NEdist^2)+Distance*Visibility+I(Distance^2)))

I'd now like to make a graph which has the response variable on the y-axis (obviously). But the x-axis should have distance on it. The NEdist is a vector that is just co-influencing the curve and has to stay in the model
but doesn't have any interactions with any other vectors.
I'd then like to put in curves/lines for the different models to see if for
example visibility effects the distance of the track to the first bird
sighting.

Is there a way to produce a graph in R that has these features?

Of course. Modeling would be of little value without such capability. In R, regression functions produce an object with a particular class ("glm" in this case) and there is generally have predict method for each class. There is also a vector of fitted values within the object that may be accessed with the fitted or fitted values functions.

The predict.glm help page has a worked example.

--

David Winsemius, MD
West Hartford, CT

______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.

Reply via email to