Search found 21 matches
- Thu Dec 10, 2020 8:50 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
Re: getting credible intervals around postestimations
got it to work, thank you so much for your help!
- Thu Dec 10, 2020 3:01 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
Re: getting credible intervals around postestimations
Actually, I can get credible intervals around a group-specific effect using this code:
bysort group (iteration): egen p_mean = mean(p)
bysort group (iteration): egen p_lo = pctile(p), p(2.5)
bysort group (iteration): egen p_hi = pctile(p), p(97.5)
where p is a variable for the group specific ...
bysort group (iteration): egen p_mean = mean(p)
bysort group (iteration): egen p_lo = pctile(p), p(2.5)
bysort group (iteration): egen p_hi = pctile(p), p(97.5)
where p is a variable for the group specific ...
- Thu Dec 10, 2020 2:48 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
Re: getting credible intervals around postestimations
thank you so much! so that makes sense, although it looks like I can only use mcmcsum (with the variables option) if I use all the chains; when I try to use it on a subset (e.g., on the residual values for one group) I get an output that looks like this:
. mcmcsum u if group==1, variables ...
. mcmcsum u if group==1, variables ...
- Wed Dec 09, 2020 10:44 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
Re: getting credible intervals around postestimations
To give you some more context: After I fit the models and pull residuals:
runmlwin outcome cons , ///
level2(group: cons) ///
level1(id:) ///
discrete(distribution(binomial) link(logit) denominator(denominator) pql2) ///
rigls maxiterations(100) ///
nopause
runmlwin outcome cons ...
runmlwin outcome cons , ///
level2(group: cons) ///
level1(id:) ///
discrete(distribution(binomial) link(logit) denominator(denominator) pql2) ///
rigls maxiterations(100) ///
nopause
runmlwin outcome cons ...
- Wed Dec 09, 2020 10:22 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
Re: getting credible intervals around postestimations
Thank you for your reply! I have tried mcmcsum, and I get the following error:
Estimate results must come from runmlwin
Furthermore, when I load the .dta file where the parameter chains I stored, the only variables included are: iteration, deviance, FP1_cons, RP2_var_cons_, and OD_bcons_1; I ...
Estimate results must come from runmlwin
Furthermore, when I load the .dta file where the parameter chains I stored, the only variables included are: iteration, deviance, FP1_cons, RP2_var_cons_, and OD_bcons_1; I ...
- Tue Dec 08, 2020 10:38 pm
- Forum: runmlwin user forum
- Topic: getting credible intervals around postestimations
- Replies: 8
- Views: 17913
getting credible intervals around postestimations
Hello! I am fitting a null two-level multilevel logistic model using the following example code:
runmlwin outcome cons , ///
level2(group: cons) ///
level1(id:) ///
discrete(distribution(binomial) link(logit) denominator(denominator) pql2) ///
rigls maxiterations(100) ///
nopause ...
runmlwin outcome cons , ///
level2(group: cons) ///
level1(id:) ///
discrete(distribution(binomial) link(logit) denominator(denominator) pql2) ///
rigls maxiterations(100) ///
nopause ...
- Fri Sep 11, 2020 12:00 pm
- Forum: runmlwin user forum
- Topic: using runmlwin with survey weights
- Replies: 3
- Views: 15588
using runmlwin with survey weights
Hello,
I am trying to fit a two-level multilevel logistic model in runmlwin using survey weights. The model runs fine using PQL2 estimation methods, however, I get the following error when trying to run the same model using MCMC methods: "Weights are only valid for univariate models estimated ...
I am trying to fit a two-level multilevel logistic model in runmlwin using survey weights. The model runs fine using PQL2 estimation methods, however, I get the following error when trying to run the same model using MCMC methods: "Weights are only valid for univariate models estimated ...
- Tue Jan 07, 2020 11:33 am
- Forum: runmlwin user forum
- Topic: obtaining level 2 residuals from multilevel ordinal model
- Replies: 2
- Views: 5772
Re: obtaining level 2 residuals from multilevel ordinal model
yes, thank you so much!! it's always the comma placement that gets me



- Mon Jan 06, 2020 10:47 pm
- Forum: runmlwin user forum
- Topic: obtaining level 2 residuals from multilevel ordinal model
- Replies: 2
- Views: 5772
obtaining level 2 residuals from multilevel ordinal model
Hi all,
I am trying to obtain the level 2 residuals from a null two-level ordinal model using runmlwin.
These models work fine:
* Fit model using IGLS MQL1
runmlwin hiv cons, ///
level2(group: (cons, contrast(1/2))) ///
level1(idcode:) ///
discrete(dist(multinomial) link(ologit) denom(cons ...
I am trying to obtain the level 2 residuals from a null two-level ordinal model using runmlwin.
These models work fine:
* Fit model using IGLS MQL1
runmlwin hiv cons, ///
level2(group: (cons, contrast(1/2))) ///
level1(idcode:) ///
discrete(dist(multinomial) link(ologit) denom(cons ...
- Thu Dec 05, 2019 2:42 pm
- Forum: runmlwin user forum
- Topic: using runmlwin in batch mode on Unix
- Replies: 19
- Views: 29478
Re: using runmlwin in batch mode on Unix
Hi Chris,
Oh yes, I see now where I can attach a file. Thanks!
I created the script as you suggested, and got the following error:
./mlnscript: error while loading shared libraries: libmln.so: cannot open shared object file: No such file or directory
However, that file (libmln.so) is in ...
Oh yes, I see now where I can attach a file. Thanks!
I created the script as you suggested, and got the following error:
./mlnscript: error while loading shared libraries: libmln.so: cannot open shared object file: No such file or directory
However, that file (libmln.so) is in ...