Multinomial logistic regression with robust standard errors/clustered data

PernilleSL — Mon, 19 Jun 2017 06:29:22 GMT

Hi,

I have to do a multinomial logistic regression on clustedered data. My supervisor ask me to run a logistic regression with robust standard errors in order to take into account dependency between observations in the data set. I have tried to find an appropriate procedure in SAS 9.4 to do so, and my best guess is to use the PROC GLIMMIX in which I put in 'random intercept / subject= id'. However, whwn I do so I get an error in the log saying: 'Nominal model require that the repsonse variable is a group effect on the on RANDOM statements. You need to add 'GROUP=outcome '. When I do so I just get another error saying: 'Model is too large to be fit by PROC GLIMMIX in a resonable amount of time on this system. Consider changing your model.'

Then my question is what to do and which model to use?

I have used the following SAS syntax:

1st error reported above:

PROC GLIMMIX data=c;

CLASS age (ref='3') ID;

MODEL outcome = age / DIST=multinomial LINK=glogit CL ODDSRATIO;

RANDOM intercept / SUBJECT=id;

RUN;

and 2nd error reported above:

PROC GLIMMIX data=c;

CLASS age (ref='3') id;

MODEL outcome = age / DIST=multinomial LINK=glogit CL ODDSRATIO;

RANDOM intercept / SUBJECT=id GROUP=outcome;

RUN;

Thanks in advance

Best,

Pernille

Re: Multinomial logistic regression with robust standard errors/clustered data

Ksharp — Mon, 19 Jun 2017 12:35:34 GMT


Could you try 
 RANDOM _residual_ / SUBJECT=id;

Re: Multinomial logistic regression with robust standard errors/clustered data

PernilleSL — Tue, 20 Jun 2017 05:43:53 GMT

Thanks a lot for your quick repsonse. I have tried what you suggested, but then I get another error: 'R-side random effects are not supported by the multinomial distribution.'. Any other sugesstions?

Thanks in advance.

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Multinomial logistic regression with robust standard errors/clustered data

Re: Multinomial logistic regression with robust standard errors/clustered data

Re: Multinomial logistic regression with robust standard errors/clustered data