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? -- View this message in context: http://r.789695.n4.nabble.com/How-to-produce-glm-graph-tp3051367p3051367.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
