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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