I'm trying to figure out how to use lmer to fit models with factors that have some nesting and some non-nested groupings. For example, in this paper: http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf we have a logistic regression of survey respondents' political preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, state (51 states within 5 regions), 4 age categories, and 4 education categories. I'd like to include states (nested within regions), and also age, education, and age x education. (That is, 5 batches of varying coefs: 50 states, 5 regions, 4 age categories, 4 education categories, and 16 age x education categories.) The age x education factor is kinda tricky because it's connected both to age and to education. I'm thinking of a model like this: lmer (y ~ black*female + (1 | state) + (1 | region) + (1 | age) + (1 | edu) + (1 | age.edu), family=binomial(link="logit")) (Here, I'm thinking of age.edu as a variable with 16 levels.) Anyway, it blows up when i try to put in these nested things. I read Doug Bates's article in R-news and there seems to be a way of doing nested groupings (unfortunately, I can't quite figure out how to do it), but I don't see any references to situations such as age, edu, and age*edu . For the article, we used Bugs, which is fine, but I'd like to see how far I can take it using lmer. I could kludge it by, for example, including age, edu, and region as unmodeled factors: lmer (y ~ black*female + (1 | state) + factor(region) + factor(age) + factor(edu) + (1 | age.edu), family=binomial(link="logit")) but I'd like to do the full multilevel version. Thanks! Andrew -- Andrew Gelman Professor, Department of Statistics Professor, Department of Political Science gelman at stat.columbia.edu www.stat.columbia.edu/~gelman Tues, Wed, Thurs: Social Work Bldg (Amsterdam Ave at 122 St), Room 1016 212-851-2142 Mon, Fri: International Affairs Bldg (Amsterdam Ave at 118 St), Room 711 212-854-7075 Mailing address: 1255 Amsterdam Ave, Room 1016 Columbia University New York, NY 10027-5904 212-851-2142 (fax) 212-851-2164
RSiteSearch("lmer nested") produced 85 hits, the first of which looks to me like it would answer your question (http://finzi.psych.upenn.edu/R/Rhelp02a/archive/61571.html): Have you tried replacing "state" with "region:state" something like the following: > lmer (y ~ black*female + (1 | region:state) + (1 | region) + (1 | age) + (1 | edu) + (1 | age.edu), family=binomial(link="logit")) But please read the earlier post, as makes cites other documents, e.g., the "MlmSoftRev" vignette, and contains useful comments from Doug Bates, among others. hope this helps. spencer graves Andrew Gelman wrote:> I'm trying to figure out how to use lmer to fit models with factors that > have some nesting and some non-nested groupings. For example, in this > paper: > http://www.stat.columbia.edu/~gelman/research/published/parkgelmanbafumi.pdf > we have a logistic regression of survey respondents' political > preferences (1=Republican, 0=Democrat), regressing on sex, ethnicity, > state (51 states within 5 regions), 4 age categories, and 4 education > categories. I'd like to include states (nested within regions), and > also age, education, and age x education. (That is, 5 batches of > varying coefs: 50 states, 5 regions, 4 age categories, 4 education > categories, and 16 age x education categories.) The age x education > factor is kinda tricky because it's connected both to age and to education. > > I'm thinking of a model like this: > > lmer (y ~ black*female + (1 | state) + (1 | region) + (1 | age) + (1 | > edu) + (1 | age.edu), family=binomial(link="logit")) > > (Here, I'm thinking of age.edu as a variable with 16 levels.) > > Anyway, it blows up when i try to put in these nested things. I read > Doug Bates's article in R-news and there seems to be a way of doing > nested groupings (unfortunately, I can't quite figure out how to do it), > but I don't see any references to situations such as age, edu, and age*edu . > > For the article, we used Bugs, which is fine, but I'd like to see how > far I can take it using lmer. I could kludge it by, for example, > including age, edu, and region as unmodeled factors: > > lmer (y ~ black*female + (1 | state) + factor(region) + factor(age) + > factor(edu) + (1 | age.edu), family=binomial(link="logit")) > > but I'd like to do the full multilevel version. > > Thanks! > Andrew >
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