[R-sig-ME] Help: making predictions for applied problems using glmm

[Oliver, M. K.]

Dear R users,

My query concerns how best to make inferences for applied problems from the outcomes of a mixed modelling approach. I am an ecologist and have been modelling the presence or absence of a species of vole in patches of suitable habitat in upland areas in relation to a number of biologically meaningful covariates. My sampling design includes 310 vole habitat patches within 9 river subcatchments and I have included subcatchment as a random effect in order to incorporate this structure in the data. I have used the information-theoretic approach with model weighting and averaging to make inferences about model selection and the consistency of parameter estimates. However, I am faced with one outstanding issue: as my question is an applied one, it is highly desirable to predict the probability of a vole habitat patch being occupied given a particular set of covariate values. This is easily done in glm using the predict() function, but I am aware that no such function exist for glmm.

However, would it be sound to take a similar approach to the coefficients and se for glmm i.e by back-transforming and applying to a range of x?

Or, would such an approach fail to take into account the model structure based on the random effect?

If so, would a reasonable approach be to estimate coefficients from re-samples of the data whilst maintaining the model structure? Predicted values could then be presented with confidence intervals that reflected variation in the structure of the data.

If not, what (if there is such a thing) would be the appropriate way to make predictions from glmm for applied problems such as this?

I hope I have been reasonably clear and am not being too ignorant. Any advice or comments would be much appreciated.

Many thanks

Matt

Dr Matthew Oliver
Research Fellow
School of Biological Sciences
University of Aberdeen
Zoology building
Tillydrone Avenue
Aberdeen AB24 2TZ
UK
tel + 44(0)1224 272789


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