Dear list,

It's the first time I use glmm and still I'm learning how to use it. and I'm
trying to fit it with the MCMCglmm package. I have read the great
documentation from the package and several other ones.. and there are things
I do understand but some other that simply cannot get it that I hope can
clarify here. There might be very silly.
I'm trying to fit a glmm to analyse the variation in auc values across
methods(3 different ones), the distribution of the species (4 catergories)
and the range size that they ocupy. So as fixed effect I chose Method,
distribution and range size and fit as random effect species and the
interaction between methods and distribution and method and range size, to
see how is the performance of the methods across the distribution and
different ranges.


My dataset looks like this
     speci Tribe Distribution range_size Method sample size AUC_train
AUC_test auc1_trans auc2_trans  Agr_goda Smerinthini mixed 200.987424 Maxent
15 0.997 0.536 1.493317282 0.56569183  Agr_god Smerinthini mixed 200.987424
GLM 15 0.889 0.666 1.09515667 0.72883359  Agr_god Smerinthini mixed
200.987424 RF 15 0.981 0.759 1.375550464 0.86177585  Agn_orn Acherontiini
temp 8.92546411 Maxent 4 0.978 0.88 1.360648078 1.0758622  Agn_orn
Acherontiini temp 8.92546411 GLM 4 0.999 0.996 1.52607124 1.48132377
Agn_orn Acherontiini temp 8.92546411 RF 4 0.998 0.998 1.507540228 1.50754023
Agr_convol Acherontiini trop 15355.3536 Maxent 2121 0.901 0.878 1.122069134
1.07166773  Agr_convol
Acherontiini trop 15355.3536 GLM 2121 0.998 0.997 1.507540228 1.49331728
Agr_convol
Acherontiini trop 15355.3536 RF 2121 1 0.998 1.570796327 1.50754023  Akb_dav
Ambulycini arid 494.348982 Maxent 47 0.994 0.984 1.461196969 1.39167151
Akb_dav Ambulycini arid 494.348982 GLM 47 1 0.991 1.570796327 1.43653142
Akb_dav Ambulycini arid 494.348982 RF 47 1 0.989 1.570796327 1.42233606

Since I don't have any apriori expectation for the variances I chose to use
an uninformative prior. and here is my code.

prior2 <- list(R= list(V=1,nu=0.002),G=list(G1=list(V =1,nu=0.002),G2=list(V
=1,nu=0.002),G3=list(V =1,nu=0.002)))
aucfit2 <- MCMCglmm(auc2_trans ~ Method + Distribution+range_size ,random= ~
speci + Method:Distribution, Method:range_size,data=mcomp,family="gaussian",
prior=prior2,nitt=120000,thin=40,burnin=40000, pr=TRUE, pl=TRUE,
verbose=FALSE, DIC=TRUE, saveX=TRUE, saveZ=TRUE, saveXL=TRUE, ginverse=NULL)


With these I keep on getting negative means, modes and DIC. Is there
something that I'm doing wrong? Though not with all the species and all the
methods, but with most of them. And actually how could I interpret the
results the function shows?

> aucfit4$DIC
[1] -1014.270
> posterior.mode(aucfit4$Sol[,"MethodMaxent"])
       var1
-0.02492543


In one of the papers I read (Elith et al 2006) says that comparing wether
the percentage of runs for a Method was greater of smaller that other it was
possible to assess performance of the model. This question might be very
silly, but how do I obtain the percentage of runs? Would be from the object
Sol? and simply to "count" the times it is greater of smaller?


I would very much appreciate any advice or comment you can provide me with,

Once more I'm sorry for the silly questions.


Lilana




-- 
Liliana Ballesteros

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