[R-sig-ME] questions on MCMCglmm

[YA]

Dear list,


I am using MCMCglmm package in R for my multilevel multinomial logistic regression model. I have a three category level1 unordered multinomial outcome 'nomial', which was coded as 0,1,2, a level1 continuous predictor 'IT1', and a level2 continuous predictor 'age', the ID variable is 'ID'. My questions are:


1. The regression coeffecient of IT1 is 0.120220, but which category of 'nomial' does the coefficient concern? how to interpret it? 

2.  Although I have got the model run properly, I dont quite understand  what the 'rcov = ~us(trait):units' do in the model, only that the model  cant run without this part. I have read the coursenote on the website  several times, but still have not figured it out. 

3. What  does 'us','idh', 'ihv' stands for (the author uses them to refer to  covariance structure, are they initials of some terminologies)? 

4. I tried to make predictions with IT1=3 and age=23, I got error suggesting "object 'nomial' not found", but 'nomial' is exactly what I am trying to predict. How to understand the logic behind this?



 So the model fit and prediction are as below:


> m10=MCMCglmm(as.factor(nomial)~IT1+age,random=~ID,data=dat,rcov = ~us(trait):units,family='categorical') 
> summary(m10)

 Iterations = 3001:12991
 Thinning interval  = 10
 Sample size  = 1000 

 DIC: 358.2906 

 G-structure:  ~ID

   post.mean l-95% CI u-95% CI eff.samp
ID     28.75    18.64    38.81    767.4

 R-structure:  ~us(trait):units

                                  post.mean l-95% CI u-95% CI eff.samp
traitnomial.1:traitnomial.1.units   0.04237  0.01597  0.13210    10.77
traitnomial.2:traitnomial.1.units  -0.21941 -0.39132 -0.07699     6.04
traitnomial.1:traitnomial.2.units  -0.21941 -0.39132 -0.07699     6.04
traitnomial.2:traitnomial.2.units   1.69265  1.21517  2.21141    23.81

 Location effects: as.factor(nomial) ~ IT1 + age 

             post.mean   l-95% CI   u-95% CI eff.samp pMCMC   
(Intercept)  34.541008 -15.319976  78.613352  931.637 0.120   
IT1           0.120220   0.009121   0.343275    6.204 0.004 **
age          -1.404272  -3.267662   0.659993  931.605 0.130   
---
Signif. codes:  0 ��***�� 0.001 ��**�� 0.01 ��*�� 0.05 ��.�� 0.1 �� �� 1


> predict.MCMCglmm(m10,data.frame(IT1=3,age=23),type='response')
Error in eval(inp, data, env) : object 'nomial' not found


Thank you very much.


Best regards,


YA
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