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Hi Georges

Many thanks for your suggestions

Regards

L

Hello Both, I hope you are doing well. I wish you a happy new year. I am using 50 imputed data to runmlwin. Due to the size of my database (more than 200000 obs and 85 var), I am using the flong format. I sorted my data according to level3 level2 level1 but I am getting the following error message: ...

Hi, Thank you for the suggestion and sorry to have bothered you. My problem is sorted, a simple mistake in the command and a bug due to the fact that I' am working with more than 300thousand observations . Here are the results with the data I provided *command insheet using "N:\data.xls",c...

I hope that you will be able to use this table. It is a sample of my data. Var1 and var2 had missing values and have been imputed I want to use them in my multilevel model to predict y using for example xi: mi est, cmdok: runmlwin y cons var1 i.var2 , level2(area: cons,) level1(id: cons) nopause Man...

Hi All, I have 2 variables : var1 continuous with missing values (100) var2 unordered categorical with missing values (25) I have imputed the missing values now I am running the following programme: xi: mi est, cmdok: runmlwin y cons var1t i.var2 , level2(area: cons,) level1(indiv: cons) nopause And...

Thank you for your suggestion
I was trying to find an easiest way. But you are right I should to do it step by step with each imputed variable.

Many thanks to both for your help

Cheers

Likestatistic

Many thanks for your help,

With missing data the programme doesn't run, so I need to impute them before. I am now trying to figure-out how to deal with mi, fp and runwlin.
Maybe I should do it in two steps using mi impute or realcom.
Do you have some advice?

Thanks.

- It works and I guess it doesn't matter if I get this message? :D Warning: age_1 has been recast to float, this has reduced precision age_2: 182 values changed Warning: age_2 has been recast to float, this has reduced precision - I have tried with my data and I got the same message + the following ...

clear webuse igg (Immunoglobulin in children) gen cons = 1 gen id = _n fp <age>: noisily runmlwin sqrtigg cons <age>, level1(id:cons) nopause nogroup I am getting these messages age_1 is held to more precision than MLwiN can handle, to reduce the precision use recast float > age_1, force When fittin...

Hi ChrisCharlton,

I changed my do file and restarted my computer but I still got the same message with my data and the example I send to you.

e(sample) changed across estimation runs
r(498);

end of do-file

r(498);