Hi all,
I am modeling infection in individuals as a binary outcome "Submicro" using a logistic regression. As individuals are clustered in households I have included a household level random effect (intercept). I have managed to model this using lmer from the lme4 package which has been working well, however, when I look at the values of the random intercepts, there appears to be residual spatial-autocorrelation (using Moran's I and semivariograms). I would therefore like to account for the spatial autocorrelation between clusters. Previously I have managed (with help!) to do this using WinBUGS via Bayesian inference, however, I'm wondering whether there is a frequentist solution. Having done some scouring on the web it looks like glmmPQL command in the MASS package might be useful, however, when I run the following  
SpatialMod<-glmmPQL(Submicro ~ agegrp, random= ~ 1|houseid, correlation = corExp(form = ~ lon + lat),         family = binomial(link = "logit"), data = D)
it gives me the error message
Error in getCovariate.corSpatial(object, data = data) :   cannot have zero distances in "corSpatial"
Having looked around at similar examples using gls, I thought specifying the grouping level within the correlation structure might help, i.e.
SpatialMod<-glmmPQL(Submicro ~ agegrp, random= ~ 1|houseid, correlation = corExp(form = ~ lon + lat | houseid),         family = binomial(link = "logit"), data = D)
But I get the same error. I'm guessing this is because it is trying to fit a spatial correlation structure between observations rather than households. But I'm wondering whether it is possible to model the spatial autocorrelation at the household level and if so how to define this in the model.
Thanks very much!Hugh

 		 	   		  
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