Hello STATALIST group


Can anyone tell me why I am getting the following error while running Latent Class Analysis

_gsem_eval_mix__wrk(): 3900 unable to allocate real <tmp>[30445,264]
_gsem_eval_mix(): - function returned error
mopt__calluser_v(): - function returned error
opt__eval_nr_v2(): - function returned error
opt__eval(): - function returned error
opt__looputil_iter0_common(): - function returned error
opt__looputil_iter0_nr(): - function returned error
opt__loop_nr(): - function returned error
opt__loop(): - function returned error
_moptimize(): - function returned error
Mopt_maxmin(): - function returned error
<istmt>: - function returned error
The following syntax I used to run the LCA
Code:
gsem ( hyp diab asthma chr_heart stroke arthritis cataract glucoma teeth angina depr
> ession obesity<-,logit) (C<- age gender martal resid living_arnmnt mpce educ caste a
> dl iadl), lclass (C 3) iterate(100)
The following is my datset

Code:
* Example generated by -dataex-. To install: ssc install dataex
clear
input byte(hyp diab asthma chr_heart stroke arthritis cataract glucoma teeth angina depression) float obesity int age byte(gender martal resid living_arnmnt mpce caste educ hospital)
0 0 0 0 0 0 . . 1 1 0 0 6 0 1 0 3 0 3 . .
0 0 0 0 0 0 0 0 1 0 1 0 5 1 0 1 2 1 3 1 0
0 0 1 0 0 0 . . 1 0 0 0 1 1 0 0 2 3 2 3 1
0 1 0 0 0 0 0 0 1 0 0 0 4 1 1 1 4 1 3 3 0
0 0 0 0 0 0 . . 1 1 0 0 5 0 0 0 2 2 1 1 1
0 0 0 0 0 0 0 0 1 0 0 0 4 1 0 0 2 2 . 1 0
0 0 0 0 0 0 0 0 0 0 0 0 6 0 1 0 3 3 1 . 0
0 0 0 0 0 1 0 0 0 1 0 0 6 0 1 0 3 1 3 . 1
1 0 0 0 0 0 0 0 1 1 0 0 6 0 1 0 3 1 2 . 0
0 0 1 0 0 0 . . 1 1 0 0 2 0 0 0 2 1 3 . 0
0 0 0 0 0 0 . . 1 0 1 0 4 1 2 1 4 0 4 1 0
0 0 1 0 0 0 . . 1 0 0 0 6 0 0 0 2 1 2 . 0
0 0 0 0 0 0 . . 0 0 0 0 1 1 0 1 2 0 1 1 .
0 0 0 0 0 0 0 0 1 1 0 0 6 1 0 0 1 2 2 1 0
0 0 0 0 0 0 1 0 1 0 0 0 6 0 1 0 3 4 1 . 0
0 0 0 0 0 1 1 0 1 0 1 0 3 0 1 0 4 1 4 . 0
0 0 0 0 0 0 0 0 1 0 1 0 5 0 1 0 4 3 1 . 0
0 0 1 1 0 1 . . 0 1 0 0 5 1 0 0 2 2 3 1 .
1 0 0 0 0 0 . . 0 1 1 0 4 0 1 0 4 1 1 . 0
1 0 0 0 0 0 . . 1 1 0 0 5 0 1 0 3 1 3 . .
0 0 0 0 0 0 0 0 1 0 0 0 5 0 0 0 2 0 3 . 1
0 0 0 0 0 0 . . 1 0 1 0 4 0 0 0 1 0 3 . 1
0 0 0 0 0 0 1 0 1 1 0 0 6 0 1 0 3 1 1 . 0
0 0 1 0 0 0 1 0 1 1 0 0 6 1 0 1 2 1 1 4 0
0 0 0 0 0 0 . . 1 1 1 0 4 0 1 0 3 0 3 . 0
0 0 0 0 0 0 . . 0 0 0 0 4 0 0 0 2 2 3 . .
0 0 0 0 0 0 0 0 1 0 0 0 6 0 1 0 3 2 3 . 0
0 0 1 0 0 0 . . 1 0 0 0 1 0 0 0 2 0 2 2 0
0 0 0 0 0 0 1 1 1 0 0 0 2 0 0 0 2 1 1 . .
0 0 1 0 0 0 1 0 1 1 0 0 4 0 1 0 3 4 2 . .
0 0 0 0 0 1 0 0 1 0 0 0 6 0 1 0 4 1 1 . 1
0 0 0 0 0 0 1 0 1 0 0 0 5 0 1 0 3 3 3 . .
0 0 0 0 0 0 . . 1 1 0 0 5 0 1 0 3 4 4 . 0
0 0 1 0 0 1 1 0 0 1 0 0 4 1 1 0 0 1 3 . 0
0 0 0 0 0 0 . . 1 0 0 0 6 1 0 0 2 2 3 2 .
0 0 0 0 0 0 1 0 1 0 0 0 5 1 0 1 2 0 3 1 .
0 0 0 0 0 0 1 0 1 0 0 0 6 1 0 0 2 0 4 1 0
1 0 0 0 0 1 . . 1 0 1 0 6 0 0 0 2 2 3 . 0
0 0 0 0 0 0 . . 1 0 1 0 4 1 0 0 2 0 4 4 0
0 0 0 0 0 0 . . 1 0 0 0 4 0 1 0 3 3 4 . .
0 0 0 0 0 0 . . 1 0 1 0 3 0 0 0 2 0 1 . .
0 0 0 0 0 0 . . 1 0 0 0 6 0 0 0 2 0 2 1 0
0 0 0 0 0 0 0 0 1 1 1 0 4 0 0 0 2 1 1 . 0
0 0 0 0 0 0 . . 0 0 0 0 6 0 0 0 2 4 3 . .
0 0 0 0 0 0 . . 1 0 0 0 4 1 0 0 2 2 2 . 0
0 0 1 0 0 1 . . 1 0 0 0 3 0 0 0 1 0 3 . 0
0 0 0 0 0 0 . . 0 0 0 0 4 0 0 0 2 1 1 . 0
0 0 0 0 0 0 1 0 1 0 0 0 6 0 1 1 4 3 4 1 .
0 0 1 0 0 0 . . 1 0 0 0 6 1 0 0 2 3 3 . 0
0 0 0 0 0 0 . . 1 1 1 0 6 0 1 0 0 0 3 . 0
1 0 0 0 0 0 0 0 1 0 0 0 6 1 0 0 2 0 3 1 0
0 0 0 0 0 0 . . 1 0 0 0 6 0 1 0 4 0 4 . 0
0 0 0 0 0 0 . . 1 0 0 0 6 0 1 1 4 0 1 . .
0 0 0 1 0 0 0 0 1 1 0 0 5 1 2 1 4 1 4 2 0
0 0 0 0 0 0 0 0 1 0 0 0 5 0 0 0 1 4 2 . 0
0 0 1 0 0 0 . . 0 1 0 0 3 0 1 0 0 1 . . 0
0 0 0 0 0 0 . . 1 0 0 0 6 0 1 0 3 1 2 . .
0 0 0 0 0 0 0 0 1 1 0 0 6 0 1 0 4 3 3 . .
1 0 0 0 0 0 0 0 1 0 0 0 6 0 1 0 3 1 1 . .
0 0 0 0 0 0 0 0 1 0 1 0 6 0 1 0 4 0 3 . 1
0 0 0 0 0 0 . . 1 0 0 0 2 0 1 0 3 1 2 1 0
0 0 0 0 0 0 . . 1 0 0 0 1 0 0 0 1 2 2 . .
0 0 0 0 0 0 0 0 1 1 1 0 6 0 0 0 1 3 3 . 0
0 0 0 0 0 0 1 0 1 0 0 0 6 1 1 1 3 0 4 2 0
0 0 1 0 0 0 1 0 1 0 0 0 5 0 1 0 3 2 2 . 0
0 0 0 0 0 0 . . 1 0 0 0 1 0 0 1 2 2 2 1 0
1 1 1 0 0 0 1 0 1 1 0 0 6 1 0 0 2 2 1 3 0
1 0 0 0 0 0 . . 1 0 0 0 5 0 1 1 3 1 1 . .
0 0 1 0 0 0 . . 1 1 0 0 6 1 0 0 1 0 3 . 0
0 0 0 0 0 0 0 0 1 1 0 0 5 1 0 0 1 1 1 . 0
0 0 1 0 0 0 0 0 1 0 1 0 6 1 0 0 1 2 4 . 0
0 0 1 0 0 0 . . 1 1 0 0 6 0 1 0 3 2 2 . .
0 0 0 0 0 0 . . 1 0 0 0 5 0 0 0 1 2 3 . 0
1 0 0 0 0 0 . . 1 0 0 0 5 1 0 0 2 0 1 1 1
0 0 0 0 0 0 . . 1 0 0 0 5 0 0 0 2 2 2 . .
0 0 0 0 0 0 . . 1 0 0 0 5 0 0 0 2 2 1 . 0
0 0 0 0 0 1 0 0 1 0 0 0 6 0 0 1 1 3 4 4 1
0 0 0 0 0 0 . . 1 0 0 0 5 0 1 0 3 1 2 . .
0 0 0 0 0 0 0 1 0 0 0 0 6 1 1 0 3 3 2 . .
0 0 0 0 0 0 0 0 1 0 0 0 4 0 1 0 3 3 1 . 1
1 0 0 0 0 0 . . 1 1 0 0 6 1 0 0 1 2 3 1 .
0 0 0 0 0 0 . . 1 0 0 0 6 1 0 1 2 1 3 1 0
0 0 0 0 0 0 1 0 1 0 0 0 6 0 1 1 3 1 . 1 0
1 0 0 0 0 1 . . 1 0 0 0 6 1 0 0 2 0 3 1 0
1 0 0 0 0 0 . . 1 0 0 0 2 0 0 0 1 3 3 . .
0 0 0 0 0 0 . . 1 0 0 0 4 0 0 0 1 0 3 . .
0 0 1 0 0 0 . . 1 0 0 0 6 1 1 0 3 1 3 . 0
0 0 1 0 0 1 0 0 1 1 1 0 6 0 1 0 3 1 4 . .
0 0 0 0 0 0 1 0 1 0 0 0 6 0 1 1 4 0 1 . 0
0 0 0 0 0 0 . . 1 0 0 0 4 0 0 0 1 3 1 . 1
0 0 0 0 0 0 . . 1 0 0 0 4 0 1 0 0 0 2 . 0
0 0 1 0 0 0 0 1 0 0 0 0 1 0 0 0 2 1 1 . 0
0 1 0 0 0 0 . . 1 1 0 0 2 1 0 0 1 0 4 2 .
0 0 0 0 0 0 0 0 1 0 0 0 6 0 1 1 3 3 1 . 0
1 0 0 0 0 0 . . 1 0 0 0 5 0 0 0 1 0 1 . 0
0 0 0 0 0 0 . . 1 0 0 0 6 0 1 0 3 0 3 . 0
0 0 0 0 0 0 . . 0 0 0 0 2 0 0 0 1 0 1 . 0
0 0 0 0 0 0 . . 1 0 0 0 6 0 1 1 3 0 2 . 0
0 0 0 0 0 1 . . 1 0 0 0 5 0 1 1 0 0 1 . .
0 0 0 0 0 0 . . 1 0 0 0 5 1 0 0 1 1 3 . 0
end
label values obesity obesity
label def obesity 0 "non obese", modify
label values age age
label def age 1 "45-49", modify
label def age 2 "50-54", modify
label def age 3 "55-59", modify
label def age 4 "60-64", modify
label def age 5 "65-69", modify
label def age 6 "70-116", modify
label values educ educ
label def educ 1 "primary or below", modify
label def educ 2 "upper prim", modify
label def educ 3 "secondary", modify
label def educ 4 "above secd", modify
label values hospital hospital
label def hospital 0 "no hospitalization", modify
label def hospital 1 "hospitaalized", modify