R help - Jun 2011 - How to write random effect in MCMCglmm

Hi All,

The data set that I have is a cluster data, and I want to run a HLM mixed
model with multi-level response. Here is my data set:
response:
   - Level (num:  1, 2, 3, 4, 5 - 5 levels)
Covariates:
   - Type (Factor: A, B, C - 3 levels)
   - yr (num: 2006, 2007, ...)
   - Male (num: 0=not Male, 1=Male - 2 levels)
   - Ethnicity (Factor: A, B, H, ..., - 7 levels)
   - ELL (num: 0, 1, - 2 levels)
   - Disability (num: 0, 1, - 2levels)
   - avgTransfers (num with missing values)
   - sdTransfers (num with missing values)
   - agec (num with missing values)
   - PctELL (num)
   - PctDisability (num)
   - PctMale (num)
Random Effects:
   - Schoolid, Teacherid, Studentid

library(MCMCglmm)
y <- MCMCglmm(factor(Level) ~ Type+factor(yr>=2006)+Male+Ethnicity+ELL+
         
avgTransfers+sdTransfers+agec+Disability+ELL*pctELL+Disability*pctDisability+
          pctMale+factor(yr>=2008)*factor(Grade),
random=~Schoolid+Teacherid+Studentid, 
          family="categorical", data=data[data$Grade>=4,])

Error in MCMCglmm(FCATprofLevel ~ transferTypeCat + factor(yr >= 2008) +  : 
please use idh(trait):units, us(trait):units or trait:units for error
structures involving catgeorical data with more than 2 categories

Does anyone know how I can fix it?

Thanks


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Belle <ping.yi <at> gmail.com> writes:
> 
> Hi All,
> 
> The data set that I have is a cluster data, and I want to run a HLM mixed
> model with multi-level response. Here is my data set:
> response:
 [snip]
> library(MCMCglmm)
> y <- MCMCglmm(factor(Level) ~
Type+factor(yr>=2006)+Male+Ethnicity+ELL+
>
avgTransfers+sdTransfers+agec+Disability+ELL*pctELL+Disability*pctDisability+
>           pctMale+factor(yr>=2008)*factor(Grade),
> random=~Schoolid+Teacherid+Studentid, 
>           family="categorical", data=data[data$Grade>=4,])
> 
> Error in MCMCglmm(FCATprofLevel ~ transferTypeCat + factor(yr >= 2008) +
:
> please use idh(trait):units, us(trait):units or trait:units for error
> structures involving catgeorical data with more than 2 categories
> 
> Does anyone know how I can fix it?
> 
  I would suggest (1) reading chapter 5 of the course notes vignette
carefully, if you haven't already [library("MCMCglmm"); 
vignette("CourseNotes")].  Especially see section 5.2 (which
begins "Multinomial models are difficult - both to fit and interpret. 
This is particularly true when each unit of observation only has a
single realisation from the multinomial.")
(2) posting this to the r-sig-mixed-models mailing list
(say that you tried here first so no-one accuses you of cross-posting).