Hello,
I am using multilevel mixed-effects logistic regression -melogit- and I want to present the results using predicted probabilities. However, I am getting very confused about whether or not I want to use the -fixedonly- option when predicting the probabilities or not. I have found very useful information about how to estimate the different probabilities - for example from here:
http://www.ats.ucla.edu/stat/stata/f..._xtmelogit.htm
However, I haven't really seen an explanation of what effect this has on interpretation. How does the interpretation of the predicted probability differ if you use -predict, mu fixedonly- instead of -predict, mu- ?
For example, say I have multiple 0/1 (failure/success) results on a number of subjects and I think that the test result depends on the location of the testing (each person was tested 3 times at each location), I would use the command:
melogit result i.location || subject:
There's a statistically significant difference between location, and I want to present this as predicted probabilities. What I really want is the probably of someone, anyone (i.e. not necessarily in the study), having a success. So, do I use -predict, mu- or -predict, mu fixed only- ? I am thinking that in order to generalise outside of the study population I need to include the random effects, so use -predict, mu-. Is that correct?
Another example... Say that I am now only interested in people who have had a failure at some stage - i.e. I want to know the probabilty of sucess, given that they have had a failure at some stage. So a similar model, but restricted to those who have ever had a failure:
melogit result i.location if ever_fail==1 || subject:
This time, I would like the probably of a success for a person given that they have had a failure at some stage, so I think that the between subject variability is not of relevance. So, in this instance, I am thinking that I would want -predict, mu fixedonly-. Would you agree?
I would really appreciate any help you can offer on this. I'm managing to get myself very muddled.
Many thanks,
Gillian
I am using multilevel mixed-effects logistic regression -melogit- and I want to present the results using predicted probabilities. However, I am getting very confused about whether or not I want to use the -fixedonly- option when predicting the probabilities or not. I have found very useful information about how to estimate the different probabilities - for example from here:
http://www.ats.ucla.edu/stat/stata/f..._xtmelogit.htm
However, I haven't really seen an explanation of what effect this has on interpretation. How does the interpretation of the predicted probability differ if you use -predict, mu fixedonly- instead of -predict, mu- ?
For example, say I have multiple 0/1 (failure/success) results on a number of subjects and I think that the test result depends on the location of the testing (each person was tested 3 times at each location), I would use the command:
melogit result i.location || subject:
There's a statistically significant difference between location, and I want to present this as predicted probabilities. What I really want is the probably of someone, anyone (i.e. not necessarily in the study), having a success. So, do I use -predict, mu- or -predict, mu fixed only- ? I am thinking that in order to generalise outside of the study population I need to include the random effects, so use -predict, mu-. Is that correct?
Another example... Say that I am now only interested in people who have had a failure at some stage - i.e. I want to know the probabilty of sucess, given that they have had a failure at some stage. So a similar model, but restricted to those who have ever had a failure:
melogit result i.location if ever_fail==1 || subject:
This time, I would like the probably of a success for a person given that they have had a failure at some stage, so I think that the between subject variability is not of relevance. So, in this instance, I am thinking that I would want -predict, mu fixedonly-. Would you agree?
I would really appreciate any help you can offer on this. I'm managing to get myself very muddled.
Many thanks,
Gillian
Comment