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
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