Hi,
I ran the Logit mixed effects model using the Zelig implementation as follows:
#child-month logit random effect household level
z.out <- zelig(y ~ Tr + sex + prox1 + prox2 + prop1 + prop2 + prop3
+ inc2 + inc3 + inc4 + inc5 + age183 + age365 + age730 + age1095
+ age1460 + year2 + mage30p + tag(1 | hh.no), data=X, model="logit.mixed")
I repeated the exercise in STATA, which yielded a larger estimate of
the treatment effect as well as a difference in the standard error. Is
there any reason why R and STATA should disagree ?
Best Regards,
Andy Stokes
Institute for Health Metrics and Evaluation
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Dear Colleagues:
I am using Zelig to estimate a population model and would like to do
some counterfactual simulation based on the results. Both the
dependent (whether have a newly born boy) and the main independent
variable of interest (whether had a prior abortion) are binary ones.
Zelig can handle questions like: what is the difference in probability
of having a boy between those who had prior abortion and those who had
not. I want to go a step further and ask: give the estimated model, if
50% of the total women had abortion, what the percentage of boys will
be in all the newly born babies? What if only 25% of the women had
abortion? And what about 75% of women had abortion? etc. Is there an
easy way to do this? Thanks.
Best,
Shige
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Hi there,
I am having problems obtaining the most recent version of Zelig,
namely version 3.1.0. The newest I have managed to get following the
guidelines for updating the software is 3.0.1. I was told that mixed
effects models including for logit are available in the newest
version.
Thanks!
Andy Stokes
Institute for Health Metrics and Evaluation
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This depends on each model. So, I suggest you look at the docs for
each model.
Good luck,
Kosuke
> On Feb 3, 2008, at 2:49 PM, å®æ¶æ wrote:
>
>> Dear All,
>>
>> I am puzzled by the difference between "unconditional prediction"
>> and "conditional prediction". The option "fn = NULL" has done the
>> job conditioning on the observed variables when making prediction,
>> what else does "cond = TRUE" bring to this process? It will be most
>> helpful if somebody can point me to some literature on this. Thanks.
>>
>> Best,
>> Shige
>
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Dear All,
I am puzzled by the difference between "unconditional prediction" and
"conditional prediction". The option "fn = NULL" has done the job
conditioning on the observed variables when making prediction, what else
does "cond = TRUE" bring to this process? It will be most helpful if
somebody can point me to some literature on this. Thanks.
Best,
Shige
Dear Colleagues,
I am studying women's fertility behaviours in China. In my data, each women
can have up to nine births. A multilevel model with children nested within
women seems to be an ideal solution. In order to reduce model dependence, I
preprocessed the data using nearest neighbour method. Sine the number of the
"treated" case is relatively small (10%), even with the ratio set to 2, the
sample size of the matched data reduced drastically and damaged the
multilevel structure. Now most of women in the matched sample has only one
child left. My question is: does it still make sense to estimate a
multilevel or mixed model instead of a plain OLS regression or logistic
regression? Further, are there good references on propensity score matching
in a multilevel situation?
Best,
Shige
