Hi everyone,
I am new Stata (v15.1) user. Currently, I am having difficulty in formulating the code for multi-level mixed effect Poisson regression model using DHS (https://dhsprogram.com/Data/) dataset. In the DHS data subjects are nested within households, and households are nested within clusters. My outcome of interest is a count variable and I have several categorical predicts. Usually for survey data we use “SVY” command. However, Stata documentation (https://www.stata.com/manuals/memepoisson.pdf) shows below command for “mepoisson” multi-level mixed effect Poisson regression which is very confusing to me.
svyset psu, weight(wvar3) || ssu, weight(wvar2) || _n, weight(wvar1)
svy: mepoisson y x || psu: || ssu:
How do I incorporate weight (v005), primary sampling unit (v021) and strata (v022) variable in the above code? Also how to estimate values for ICC, PCV, AIC, and BIC after model fitting.
I was hoping someone could help me with this or if anyone has done similar analysis and shares the code that will be much appreciated.
Thank you,
Iqbal
I am new Stata (v15.1) user. Currently, I am having difficulty in formulating the code for multi-level mixed effect Poisson regression model using DHS (https://dhsprogram.com/Data/) dataset. In the DHS data subjects are nested within households, and households are nested within clusters. My outcome of interest is a count variable and I have several categorical predicts. Usually for survey data we use “SVY” command. However, Stata documentation (https://www.stata.com/manuals/memepoisson.pdf) shows below command for “mepoisson” multi-level mixed effect Poisson regression which is very confusing to me.
svyset psu, weight(wvar3) || ssu, weight(wvar2) || _n, weight(wvar1)
svy: mepoisson y x || psu: || ssu:
How do I incorporate weight (v005), primary sampling unit (v021) and strata (v022) variable in the above code? Also how to estimate values for ICC, PCV, AIC, and BIC after model fitting.
I was hoping someone could help me with this or if anyone has done similar analysis and shares the code that will be much appreciated.
Thank you,
Iqbal
