[R] How to produce glm graph

[David Winsemius]

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