GLMM: measure for significance of random variable?

1.  To evalute the significance of "the random variable" (a random 
effect?) using 'lmer', have you considered fitting models with and 
without that effect, as in the example with 'example(lmer)'?

	  2.  Regarding 'predict.lmer', I tried the following:
 > predict(fm1)
Error in predict(fm1) : no applicable method for "predict"
 > predict.glm(fm1)
NULL

	  However, ' RSiteSearch("predict lmer")' produced 9 hits for me, the 
first of which indicated that glmmPQL in library(MASS) had a predict 
method (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/62139.html).

	  3.  I can't tell you why the "Laplace" method didn't work with all 
your models, but I can guess:  Do you know if the model is even 
estimable?  As a partial test for that, have you tried estimating the 
same fixed effects with "glm", something like the following:

model4b0 <- glm(RESPONSE~ D_TO_FORAL +
+ I((DIST_GREEN-300)*(DIST_GREEN<300))+
+ I((DIST_WATER-200)*(DIST_WATER<200)) +
+ I((DIST_VILL-900)*(DIST_VILL<900)) +
+ I((DIST_HOUSE-200)*(DIST_HOUSE<200)), family=binomial)

[or 'family=quasibinomial']

	  If this fails to give you an answer, it says there is something in 
the model that is not estimable.  I might further try the same thing in 
"lm":

model4b00 <- lm(RESPONSE~ D_TO_FORAL +
+ I((DIST_GREEN-300)*(DIST_GREEN<300))+
+ I((DIST_WATER-200)*(DIST_WATER<200)) +
+ I((DIST_VILL-900)*(DIST_VILL<900)) +
+ I((DIST_HOUSE-200)*(DIST_HOUSE<200)))

	  If this fails also, you can at least add 'singular.ok=TRUE' to find 
out what "lm" will estimate.

  	  If this doesn't answer the question, I suggest you work to develop 
this simplest, self-contained example you can think of that will 
replicate the problem, then send that to this listserve, as suggested in 
the posting guide! 'www.R-project.org/posting-guide.html'.  It's much 
easier for someone else to diagnose a problem if they can replicate it 
on their own computer in a matter of seconds.

	  hope this helps.
	  spencer graves