Hello all,

I run a fixed effects logistic regression of self rated health and local employment change over three waves, as follows (clustering is baseline geographical location):

However, convergence is not achieved. Reviewing the Statalist archives I removed variables one by one until I identified the two culprits, respondents age and geographical location, no longer included below:

Some information on the troublemakers:

Having identified the trouble-makers, what can I do to fix this? I tried changing the data types and rounding the variables, as below, but that didn't change anything. Similarly banding age into categories was not useful as it changed my number of observations to 176.

I would like to keep these variables in my analysis so what can I do?

Thanks for any and all advice,

John

I run a fixed effects logistic regression of self rated health and local employment change over three waves, as follows (clustering is baseline geographical location):

Code:

. clogit binary_health_y psum_unemployed_total_cont_y i.yrlycurrent_county_y1 i.year age_y i.maritalstatus_ > y if has_y0_questionnaire==1 & has_y5_questionnaire==1 | has_y0_questionnaire==1 & has_y10_questionnaire= > =1 | has_y0_questionnaire==1 & has_y5_questionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire= > =1 & cbmi_y5 !=. & has_y5_questionnaire==0 | has_y0_questionnaire==1 & cbmi_y10 !=. & has_y10_questionnai > re==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==0 & cbmi_y10 !=. & has_y10_question > naire==0 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 | has_y0_questionnaire==1 & cb > mi_y10 !=. & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 & > cbmi_y10 !=. & has_y10_questionnaire==1, group(id) cluster (current_county_y1) robust iterate(500) nolog note: multiple positive outcomes within groups encountered. note: 447 groups (1,057 obs) dropped because of all positive or all negative outcomes. note: 3.yrlycurrent_county_y1 omitted because of no within-group variance. note: 6.yrlycurrent_county_y1 omitted because of no within-group variance. note: 7.yrlycurrent_county_y1 omitted because of no within-group variance. note: 13.yrlycurrent_county_y1 omitted because of no within-group variance. note: 15.yrlycurrent_county_y1 omitted because of no within-group variance. note: 16.yrlycurrent_county_y1 omitted because of no within-group variance. note: 17.yrlycurrent_county_y1 omitted because of no within-group variance. note: 18.yrlycurrent_county_y1 omitted because of no within-group variance. note: 19.yrlycurrent_county_y1 omitted because of no within-group variance. note: 20.yrlycurrent_county_y1 omitted because of no within-group variance. note: 23.yrlycurrent_county_y1 omitted because of no within-group variance. note: 24.yrlycurrent_county_y1 omitted because of no within-group variance. note: 25.yrlycurrent_county_y1 omitted because of no within-group variance. note: 26.yrlycurrent_county_y1 omitted because of no within-group variance. note: 28.yrlycurrent_county_y1 omitted because of no within-group variance. note: 29.yrlycurrent_county_y1 omitted because of no within-group variance. note: 32.yrlycurrent_county_y1 omitted because of no within-group variance. convergence not achieved Conditional (fixed-effects) logistic regression Number of obs = 524 Wald chi2(16) = . Prob > chi2 = . Log pseudolikelihood = -178.37962 Pseudo R2 = 0.0592 (Std. Err. adjusted for 20 clusters in current_county_y1) ---------------------------------------------------------------------------------------------- | Robust binary_health_y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- psum_unemployed_total_cont_y | -.0720918 .0388134 -1.86 0.063 -.1481648 .0039811 | yrlycurrent_county_y1 | Cavan | 0 (empty) Clare | 3.508484 1.437552 2.44 0.015 .6909335 6.326035 Cork | -701.8535 . . . . . Donegal | 0 (empty) Dublin 16 | 0 (empty) Dublin City | 5.209175 1.927489 2.70 0.007 1.431367 8.986984 DĂșn Laoghaire-Rathdown | 5.798566 1.803271 3.22 0.001 2.264218 9.332913 Fingal | 5.31021 2.008511 2.64 0.008 1.373601 9.246819 Galway | .7730753 1.945584 0.40 0.691 -3.040199 4.58635 Galway City | .7289299 . . . . . Kerry | 0 (empty) Kildare | .5227628 1.379929 0.38 0.705 -2.181848 3.227374 Kilkenny | 0 (omitted) Laois | 0 (omitted) Leitrim | 0 (empty) Limerick | 0 (empty) Longford | 0 (empty) Louth | 0 (empty) Mayo | -37.39307 . . . . . Meath | 5.840481 1.762588 3.31 0.001 2.385872 9.295089 Monaghan | 0 (empty) Offaly | 0 (omitted) Roscommon | 0 (omitted) Sligo | 0 (empty) South Dublin | 5.426697 1.908334 2.84 0.004 1.686432 9.166962 Tipperary | 0 (empty) Tipperary North | 0 (empty) Waterford | -559.1875 . . . . . Westmeath | 3.329881 1.503996 2.21 0.027 .382103 6.277658 Wexford | 0 (omitted) Wicklow | -97.28333 . . . . . | year | 5 | -.4421024 .2411563 -1.83 0.067 -.9147601 .0305553 10 | .2906156 . . . . . | age_y | .0180895 .030657 0.59 0.555 -.0419972 .0781762 | maritalstatus_y | Cohabiting | .2093029 .1423515 1.47 0.141 -.069701 .4883068 Separated | -.5001542 1.496891 -0.33 0.738 -3.434007 2.433699 Divorced | -1.252438 .6516711 -1.92 0.055 -2.529689 .0248143 Widowed | .5818359 1.804183 0.32 0.747 -2.954298 4.117969 Single/Never married | -.025371 .4241849 -0.06 0.952 -.8567581 .8060161 ---------------------------------------------------------------------------------------------- Warning: convergence not achieved . margins, dydx(psum_unemployed_total_cont_y) post Average marginal effects Number of obs = 524 Model VCE : Robust Expression : Pr(binary_health_y|fixed effect is 0), predict(pu0) dy/dx w.r.t. : psum_unemployed_total_cont_y ---------------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- psum_unemployed_total_cont_y | -.0083921 .0045424 -1.85 0.065 -.0172951 .0005108 ----------------------------------------------------------------------------------------------

However, convergence is not achieved. Reviewing the Statalist archives I removed variables one by one until I identified the two culprits, respondents age and geographical location, no longer included below:

Code:

. clogit binary_health_y psum_unemployed_total_cont_y i.year i.maritalstatus_y if has_y0_questionnaire==1 & > has_y5_questionnaire==1 | has_y0_questionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & > has_y5_questionnaire==1 & has_y10_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_ques > tionnaire==0 | has_y0_questionnaire==1 & cbmi_y10 !=. & has_y10_questionnaire==0 | has_y0_questionnaire== > 1 & cbmi_y5 !=. & has_y5_questionnaire==0 & cbmi_y10 !=. & has_y10_questionnaire==0 | has_y0_questionnair > e==1 & cbmi_y5 !=. & has_y5_questionnaire==1 | has_y0_questionnaire==1 & cbmi_y10 !=. & has_y10_questionn > aire==1 | has_y0_questionnaire==1 & cbmi_y5 !=. & has_y5_questionnaire==1 & cbmi_y10 !=. & has_y10_questi > onnaire==1, group(id) cluster (current_county_y1) robust nolog note: multiple positive outcomes within groups encountered. note: 469 groups (1,106 obs) dropped because of all positive or all negative outcomes. Conditional (fixed-effects) logistic regression Number of obs = 547 Wald chi2(8) = 86.50 Prob > chi2 = 0.0000 Log pseudolikelihood = -194.59969 Pseudo R2 = 0.0172 (Std. Err. adjusted for 20 clusters in current_county_y1) ---------------------------------------------------------------------------------------------- | Robust binary_health_y | Coef. Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- psum_unemployed_total_cont_y | -.0708697 .0309798 -2.29 0.022 -.1315889 -.0101504 | year | 5 | -.3027965 .1272918 -2.38 0.017 -.5522839 -.053309 10 | .4799054 .2673404 1.80 0.073 -.0440723 1.003883 | maritalstatus_y | Cohabiting | .3540121 .2454215 1.44 0.149 -.1270052 .8350295 Separated | -.5968626 1.514624 -0.39 0.694 -3.565471 2.371746 Divorced | -1.213241 .6442842 -1.88 0.060 -2.476015 .0495326 Widowed | -.0610695 1.433941 -0.04 0.966 -2.871542 2.749404 Single/Never married | .080062 .3159233 0.25 0.800 -.5391362 .6992603 ---------------------------------------------------------------------------------------------- . margins, dydx(psum_unemployed_total_cont_y) post Average marginal effects Number of obs = 547 Model VCE : Robust Expression : Pr(binary_health_y|fixed effect is 0), predict(pu0) dy/dx w.r.t. : psum_unemployed_total_cont_y ---------------------------------------------------------------------------------------------- | Delta-method | dy/dx Std. Err. z P>|z| [95% Conf. Interval] -----------------------------+---------------------------------------------------------------- psum_unemployed_total_cont_y | -.0157168 .0055284 -2.84 0.004 -.0265523 -.0048812 ---------------------------------------------------------------------------------------------- . estimates store logitmod . estimates table logitmod, star stats(N r2 r2_a) ------------------------------ Variable | logitmod -------------+---------------- psum_unemp~y | -.01571676** -------------+---------------- N | 547 r2 | r2_a | ------------------------------ legend: * p<0.05; ** p<0.01; *** p<0.001

Code:

. des yrlycurrent_county_y storage display value variable name type format label variable label ----------------------------------------------------------------------------------------------------------- yrlycurrent_c~y str23 %23s . sum yrlycurrent_county_y Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- yrlycurren~y | 0

Code:

. sum age_y Variable | Obs Mean Std. Dev. Min Max -------------+--------------------------------------------------------- age_y | 3,123 35.06398 7.199812 15.1 53.7 . des age_y storage display value variable name type format label variable label ----------------------------------------------------------------------------------------------------------- age_y float %9.0g .

I would like to keep these variables in my analysis so what can I do?

Code:

gen int rage_y = round(age_y) gen float fyrlycurrent_county_y1 = yrlycurrent_county_y1 replace age_y=round(age_y, 1) gen age_bands_y=. replace age_bands_y = 1 if age_y>=15 & age_y<= 25 replace age_bands_y = 2 if age_y>=25 & age_y<= 35 replace age_bands_y = 3 if age_y>=45 & age_y<= 55

Thanks for any and all advice,

John

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