Dear all,

I have constructed a multinomial mixed model using the package MCMCglmm
that has three potential values of the response variable (let's call them
A, B, and C). Although the model is running smoothly, I am unclear of how
to interpret the output of the predict function. I would like to
cross-validate the model, so I need to know the model prediction for each
data point (A, B, or C). I have 634 data points and 1268 rows in the output
of the predict function, which makes me think there is both a probability B
is more likely than A and a probability that C is more likely than A, but
because they are all in one column and don't have an index, I don't know
what point each row corresponds to.

Here is my model:

mt<-MCMCglmm(pcl2~-1+trait + trait:ngtr1 + trait:bin1 + trait:night1,

random = ~ us(1+night1):lID,

rcov=~us(trait):units,

prior = list(

R = list(V=IJ,fix=1),

G = list(

G1 = list(V = diag(2),n = 0.002))),

burnin = 30000,

nitt = 200000,

family = "categorical",

data = killsdf,

verbose=FALSE,

DIC=TRUE,

pr=TRUE,

thin=200)

And the basic predict function:

m.pred<-predict(mt,type="response",interval="prediction")

My output from "predict" looks like this (again, n = 634):

         fit                lwr                upr
1 0.07529940 0.0009159809 0.2450521
2 0.26639933 0.0006481384 0.8171763
3 0.08842384 0.0011288143 0.2682600
4 0.28736411 0.0001511912 0.8286539
5 0.11389115 0.0013791012 0.3841322
6 0.21789723 0.0058586078 0.5767750
…
1268 0.24447799 5.553336e-03 0.6738022

I apologize if this is clearly discussed in the Course Notes or other
documents, but I just cannot seem to find it. I would be very grateful to
anyone who can provide insight on how to interpret the predict function
output.

Many thanks,
Justine

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