I have 10 different species with presence/absences data, as well as 6 different covariates relating to the design of marinas, including 3 continuous (lengths of walls, pontoons and groynes) and 3 factor variables (distance from freshwater, type of entrance and marina location). I have made binomial GLMs to assess which design elements of a marina which most influence the presence of each species.
However, when I run variance inflation factor (VIF) values to assess if there is collinearity among the covariates, the covariate ‘location’ only comes up as collinear in some of my models, even tho they all contain the same covariates... why is this? Should I just exclude 'location' from all my models (I can see why it would be collinear)? Or should I include/exclude it from different models based on the models VIF?
The following are two of my 10 models, the remainder follow the same format but with different response variables (all binary presence/absence data)
bin.fit1<- glm(S_clava ~ as.factor(fresh) + wall + groyne + pontoon + mooring + as.factor(entrance) + as.factor(location), family= binomial, data= data1) bin.fit2<- glm(C_mutica ~ as.factor(fresh) + wall + groyne + pontoon + mooring + as.factor(entrance) + as.factor(location), family= binomial, data= data1)