Dear whom it may concern,

Hi. My name is Sang Won Han, a Ph.D. student in Sociology at Columbia, and
I am writing to you because I would like to ask you some questions
regarding the multilevel analysis. I am working on a research project that
utilizes the New York City Community Health Survey (CHS) (
http://www.nyc.gov/html/doh/html/data/survey.shtml), a repeated
cross-sectional survey, for looking into individual- and neighborhood-level
determinants for multiple health outcomes including general health status,
diabetes, obesity, asthma, and so on by fitting a multilevel model for each
year from 2003 to 2012, and I have some difficulty properly fitting the
models. The questions that I have are as follows:

1) I am wondering how to incorporate survey weights into the multilevel
model in 'lmer' or 'glmer' in R. For every year of survey, one kind of
survey weights is provided for each respondent, but I heard that for
multilevel analysis, it needs to incorporate two types of survey weights,
like level-1 weights for individuals and level-2 weights for groups, and in
Stata, when 'xtmixed' is used, there are options for incorporating those
two types of survey weights under the names of 'pw' for individual-level
weights and 'pweight' for group-level weights. But I am using lmer or glmer
in 'lme4' package in R, and in the manual there is an option 'weights',
saying 'an optional vector of ‘prior weights’ to be used in the ﬁtting
process. Should be NULL or a numeric vector' but I am not sure this is the
right option to incorporate the survey weights, and as I mentioned, in the
survey that I am working on, there exists just one type of survey weights
for respondents. For your information, I tried to fit the model using
xtmixed command in Stata by setting probability 1.0 for all neighborhoods
as the level-2 survey weights because level-2 units in the survey encompass
all the neighborhoods in New York City, but the model doesn't converge
unless using a scale option in xtmixed but when using the scale option,
then the results are different from the ones that are obtained from lmer or
glmer in R with incorporating the weights into 'weights' option.

2) I would like to ask you how to run 'multivariate multilevel analysis' in
R. As I mentioned, we are interested in what determinants predict multiple
health outcomes simultaneously and want to estimate individual- and
neighborhood-level correlation among health outcomes after controlling for
relevant covariates. After reading some related papers (e.g.
http://jech.bmj.com/content/59/8/664.short), I've just found that MLwiN
could run that analysis, and also there exists a package that allows to run
MLwiN in and transport the results into R environment ('R2MLwiN'), but I am
wondering if it is possible to run the same model in R with what packages.

Your answers to my questions will be greatly appreciated, and look forward
to hearing from you soon. Thank you.


Sincerely,

Sang Won

-- 
Sang Won Han
Department of Sociology
Columbia University
501 Knox Hall - MC 9649
606 West 122nd Street
New York, NY 10027
E-mail sh3083@columbia.edu

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