st: Re: Convergence not acheived

["Joseph Coveney" <stajc2@gmail.com>]

Florence  Momplaisir wrote: 

I'm using a mixed effect model to assess factors associated with getting an 
elective c-section among HIV + women. I used these commands (listed below) but 
I'm getting an error message saying that convergence is not acheived. My cell 
sizes vary between 3 and 111 and it is difficult for me to collapse things 
further (wouldn't make clinical sense). What should I do in that case?
xtset Stateno
.   xi: xtlogit elective_c i.adequate_pre1 i.delivery_agegrp1 i.r
> ace_cat1 i.illdrug i.C_RPT i.gestage1 if viralsup==2, or pa c(e
> xc)
i.adequate_pre1   _Iadequate__0-2     (naturally coded; _Iadequat
> e__0 omitted)
i.delivery_ag~1   _Idelivery__0-2     (naturally coded; _Ideliver
> y__0 omitted)
i.race_cat1       _Irace_cat1_0-2     (naturally coded; _Irace_ca
> t1_0 omitted)
i.illdrug         _Iilldrug_0-1       (naturally coded; _Iilldrug
> _0 omitted)
i.C_RPT           _IC_RPT_1-2         (_IC_RPT_1 for C_RPT==False
>  omitted)
i.gestage1        _Igestage1_0-1      (naturally coded; _Igestage
> 1_0 omitted)
note: _Iilldrug_1 dropped because of collinearity
note: _Igestage1_1 dropped because of collinearity
estimates diverging (correlation > 1)
r(430);

--------------------------------------------------------------------------------

You could try something like the following, but your model might be more 
ambitious than what your data will support.  

xtgee . . . , i(Stateno) family(binomial) link(log) corr(independent) robust

You seem to have collinearity among your categorical variables, some of which 
are discretized continuous variables, and so also try keeping some or all 
continuous variables as continuous.  If nonlinear functional forms are of 
scientific interest, then you can use polynomials or fractional polynomials of
them.

Joseph Coveney

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