error message: no imputations to compute between-imputation variance

[Lorena Estrada]

Hi. I'm attempting to run a mixed regression analysis using pweights with the Add Health data set. I used the recommended scaling techniques pwigls and mpml (user-written commands), but when I run it I get the error message "no imputations to compute between-imputation variance".


SCALING SYNTAX.


*ALL Latinos (Only)
mi passive: gen latinowgtwvs = .00000001
mi xeq: replace latinowgtwvs = gswgt4 if latino==1

mi passive: gen latinowgttemp = .00000001
mi xeq: replace latinowgttemp = w4_wc if latino==1

mpml_wt, psu_id(psuscid) fsu_id(aid) psu_wt(schwt1) fsu_wt(latinowgttemp) mpml_wta(alllatwgt)

pwigls, psu_id(psuscid) fsu_id(aid) psu_wt(schwt1) fsu_wt(latinowgttemp) psu_m1wt(m1alllat) fsu_m1wt(pw1r_alllat) psu_m2wt(m2alllat) fsu_m2wt(alllatwgt2)
mi passive: generate mlalllatwt2=m2alllat
mi passive: generate mlalllatwt1=alllatwgt2




I've tried the approach of mi svyset, as well as embedding the weights in the mixed syntax without any luck. below the full syntax and output.


ATTEMPT #1
mi estimate, errorok: mixed depress wvage16 wvage16sq wvage16cub || psuscid:, pweight(mlalllatwt2) || aid:, pweight(mlalllatwt1) cov(un) variance mle
no imputations to compute between-imputation variance
r(2000);

ATTEMPT #2
mi estimate, errorok: mixed depress wvage16 wvage16sq wvage16cub || psuscid: || aid: cov(un) variance mle
no imputations to compute between-imputation variance
r(2000);

ATTEMPT #3
mi svyset psuscid [pweight=alllatwgt], strata(region)
pweight: alllatwgt
VCE: linearized
Single unit: missing
Strata 1: region
SU 1: psuscid
FPC 1: <zero>

mi estimate, errorok: svy: mixed depress wvage16 wvage16sq wvage16cub
no imputations to compute between-imputation variance
r(2000);


Any thoughts on what I'm doing wrong??

[Tista Kundu]

I am having exactly the same issue. And to my utter surprise, sometimes this error disappears when use the 'noi' option and sometimes it also disappeares with 'dots' option. I don't understand if this is some sort of a bug or not!!! Because the very same code sometimes produce this r(2000) error and sometimes runs smoothly!!!

[daniel klein]

We (or tech-support) would really need to see a reproducible example to be able to say more. What do you get when you type

mi query

immediately after receiving the error message?

Also, why do you need the errorok option? My guess is that the model fails for all but one imputed dataset, which you allow by specifying the errorok option; with only one valid set of results you cannot compute any between imputation variance.

Best
Daniel