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Cross-classified model with negative binomial distribution
Posted: Tue Jul 09, 2013 3:25 pm
by VBMHealthEcon
Hello,
I'm relatively new to MLwiN and I'm using it through the runmlwin command in Stata
I'm trying to estimate a cross-classified random intercepts model with a negative binomial distribution using MCMC. However, I received an error message saying that "The selected distribution is not available for MCMC". In which case, what is the best way for me to proceed in estimating my model? I have a relatively large dataset with 127,700 level 1 observations so I fear IGLS may not be appropriate. Appreciate your advice.
Thanks, Valerie
Re: Cross-classified model with negative binomial distributi
Posted: Wed Jul 10, 2013 5:55 pm
by ChrisCharlton
Unfortunately as you have seen MLwiN cannot currently fit negative binomial models using MCMC. As an alternative method for introducing over-dispersion you can however add a pseudo level with the identifier set to your level-1 identifier and then add a random effect at this level. As you are using a cross-classified model you will need to ensure that the unit IDs have unique codes.
Re: Cross-classified model with negative binomial distributi
Posted: Tue Jul 30, 2013 2:38 pm
by VBMHealthEcon
Dear Chris,
Thanks very much for your help. I couldn't add a random effect to the level 1 identifier (perhaps I was doing something wrong) but alternatively I tried the poisson distribution with the extra option and this gives similar results to the negbin so I'm happy with that.
Best,
Valerie
Re: Cross-classified model with negative binomial distributi
Posted: Tue Jul 30, 2013 3:01 pm
by GeorgeLeckie
Hi Valerie,
Glad you found a solution which works for you. Nonetheless, this is how you add the psuedo-level...
The data are county-level counts. There are 354 counties. There is one observation (row of data) per county
Single-level Poisson
Code: Select all
. runmlwin obs cons uvbi, level1(county) discrete(distribution(poisson) offset(lne
> xpected)) rigls nopause
MLwiN 2.28 multilevel model Number of obs = 354
Poisson response model
Estimation algorithm: RIGLS, MQL1
Run time (seconds) = 1.28
Number of iterations = 4
------------------------------------------------------------------------------
obs | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | -.0701054 .0110454 -6.35 0.000 -.091754 -.0484569
uvbi | -.0571914 .0026768 -21.37 0.000 -.0624378 -.0519449
------------------------------------------------------------------------------
Here we add a county-level random effect.
Code: Select all
. runmlwin obs cons uvbi, level2(county: cons) level1(county) discrete(distributio
> n(poisson) offset(lnexpected)) rigls nopause
MLwiN 2.28 multilevel model Number of obs = 354
Poisson response model
Estimation algorithm: RIGLS, MQL1
-----------------------------------------------------------
| No. of Observations per Group
Level Variable | Groups Minimum Average Maximum
----------------+------------------------------------------
county | 354 1 1.0 1
-----------------------------------------------------------
Run time (seconds) = 1.58
Number of iterations = 5
------------------------------------------------------------------------------
obs | Coef. Std. Err. z P>|z| [95% Conf. Interval]
-------------+----------------------------------------------------------------
cons | -.091411 .0248586 -3.68 0.000 -.1401329 -.042689
uvbi | -.0479693 .0053335 -8.99 0.000 -.0584227 -.0375159
------------------------------------------------------------------------------
------------------------------------------------------------------------------
Random-effects Parameters | Estimate Std. Err. [95% Conf. Interval]
-----------------------------+------------------------------------------------
Level 2: county |
var(cons) | .1468484 .0157706 .1159385 .1777583
------------------------------------------------------------------------------
I hope that helps
George
Re: Cross-classified model with negative binomial distributi
Posted: Wed Jul 31, 2013 9:17 am
by VBMHealthEcon
Dear George,
Many thanks for your prompt reply and example of how to add a pseudo level - I understand now what I was doing wrong.
Best,
Valerie
Re: Cross-classified model with negative binomial distributi
Posted: Wed Jul 31, 2013 10:06 am
by GeorgeLeckie
Great, you're welcome
George