# Building predictive maps of response variables in R

I am a novice in spatial statistics in R. So, I am looking for some advises. In particular, I would like to create a predictive map of capture success from a generalized linear mixed-effects model (GLMM). In my GLMM, the dependent variable is the total number of unique individuals captured per 100 trap nights in trapping sites and the independent variables are the proportion of land cover types in trapping sites. I included a random effect for the year. My raster of land cover types has cells of 25 x 25 m. How can I assign predicted values from the GLMM to raster pixels in R? Have I to use a kriging method? As the covariates are proportions of land cover types in trapping sites, is it a problem if a raster pixel represents a single land cover type (and not several land cover types)?

Update:

Here are:

• The structure of my dataset:

• A representation of one trapping site:

So, a trapping site is composed of several land cover types and each raster pixel is assigned to one land cover type. The trapping sites have different sizes. I defined the proportion of a given land cover type as `number of pixels for the land cover type in the trapping site / total number of pixels (thus for all land cover types) in the trapping site`.

• Given that any well-defined trapping site is so small that it can be considered to have only one land cover type, exactly how did you define and measure "proportion of land cover types" at your sites? Did you look at the proportions within a certain distance, for instance? Feb 8, 2016 at 19:54
• Thank you very much whuber for your response. No, I didn't look at the proportions within a certain distance, but within the entire trapping site.
– Nell
Feb 8, 2016 at 22:27
• It looks like you have a change of support problem, especially if the trapping sites are of different sizes. In effect, your data represent properties of regions of relatively large spatial extent, whereas you wish to draw a map in which properties are assigned to points. Something has to give. If most of the trapping sites are approximately the same size, one work-around for this problem is to surround every grid pixel with a hypothetical trapping area of this common size (and shape) and assign the land cover proportions from the hypothetical area to the pixel. Feb 8, 2016 at 22:35
• Thank you very much whuber. Yes, the trapping sites are of different sizes.
– Nell
Feb 8, 2016 at 22:47