I re-ran R again (without missing values), and this time, R kept running. I
have 23658 obs with 63 variables. Is my data set out of memory, or there are
something wrong in MCMCglmm()?

On Fri, Jun 3, 2011 at 9:47 AM, Ping Yi <ping.yi@gmail.com> wrote:

> Hi Ben,
>
> I have deleted the missing values, but i still got the same error. What
> else I can do for it?
>
> Thanks for the help.
>
>
>
>
> On Thu, Jun 2, 2011 at 11:33 AM, Ben Bolker <bbolker@gmail.com> wrote:
>
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>> On 06/02/2011 02:19 PM, Ping Yi wrote:
>> > Hi,
>> >
>> > I have posted this on R-help, but I have not got more information so
>> far.
>> > Bbolker<
>> http://r.789695.n4.nabble.com/template/NamlServlet.jtp?macro=user_nodes&user=23641
>> >suggests
>> > that I should re-post it here, and see if I can get further help.
>> >
>> > 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$Grade>=4,])
>> >
>> > *Error in MCMCglmm(FCATprofLevel ~ transferTypeCat + factor(yr >= 2008)
>> +  :
>> >
>> > please use idh(trait):units, us(trait):units or trait:units for error
>> > structures involving catgeorical data with more than 2 categories*
>> >
>> > I also read chapter 5 of the course notes vignette, and I tried codes
>> below:
>> >
>> > library(MCMCglmm)
>> > y <- MCMCglmm(factor(Level) ~ Type+factor(yr>=2006)+Male+Ethnicity+ELL+
>> >
>> >
>> avgTransfers+sdTransfers+agec+Disability+ELL*pctELL+Disability*pctDisability+
>> >
>> >           pctMale+factor(yr>=2008)*factor(Grade), rcov=~us(trait):units,
>> > random=~Schoolid+Teacherid+Studentid,
>> >           family="categorical", data=data[data$Grade>=4,],
>> verbose=FALSE)
>> >
>> > *Error in buildZ(rmodel.terms[r], data = data) : missing values in
>> random
>> > predictors*
>> >
>> > Does anyone know how to fix it, or if there are any other methods that
>> can
>> > run this model?
>>
>>   Try removing missing values (NAs) and see if that helps?  (It's
>> conceivable that the NAs aren't in your data set but have been produced
>> in the process of generating the data structures for multilevel data,
>> but it would definitely be worth trying data =
>> na.omit(subset(data,Grade>=4)) as a first pass ...
>>
>>
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>>
>
>
>
> --
> Best regards,
> Ping
>



-- 
Best regards,
Ping

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