I would like to take into account the sampling effort in a logistic regression (in R) to give more or less weight to observations, but I don’t know how to do it. I have read many topics on the “weigths” parameter in glm but this seems not suitable in my case. I have a dataset with presence or absence at different location and the sampling effort vary between locations. I have a raster map that estimate the sampling effort at each location (depending on the number of potential observers in each area) but I don´t have precise information for each site such as the exact number of visit and the result of each visit (presence or absence). I would like to build a species distribution model based on different environmental predictors but taking into account the differences in sampling effort. Is someone have an idea on how to specify this in a glm?
Since you want to consider the sampling effort, the
svyglm function should be a good option.
svyglm computes the robust standard errors which consider the loss of precision introduced by sampling weights.
svyglm, you might specify your survey design with the