Harold, I'm confused now. Just for concretness, suppose we have 800 people, 82 food items, and one predictor ("folic", the folic acid concentration) at the food-item level. Then DV will be a vector of length 800, foods is an 800 x 82 matrix, sex is a vector of length 800, age is a vector of length 800, and folic is a vector of length 82. The vector of folic acid concentrations in individual diets is then just foods%*%folic, which I can call folic_indiv.
How would I fit the model in lmer(), then? There's some bit of understading that I'm still missing. Thanks. Andrew Doran, Harold wrote: > Prof Gelman: > > I believe the answer is yes. It sounds as though persons are partially > crossed within food items? > > Assuming a logit link, the syntax might follow along the lines of > > fm1 <- lmer(DV ~ foods + sex + age + (1|food_item), data, family = > binomial(link='logit'), method = "Laplace", control = list(usePQL= > FALSE) ) > > Maybe this gets you partly there. > > Harold > > > > -----Original Message----- > From: [EMAIL PROTECTED] on behalf of Andrew Gelman > Sent: Sat 5/20/2006 5:49 AM > To: r-help@stat.math.ethz.ch > Cc: [EMAIL PROTECTED] > Subject: [R] Can lmer() fit a multilevel model embedded in a > regression? > > I would like to fit a hierarchical regression model from Witte et al. > (1994; see reference below). It's a logistic regression of a health > outcome on quntities of food intake; the linear predictor has the form, > X*beta + W*gamma, > where X is a matrix of consumption of 82 foods (i.e., the rows of X > represent people in the study, the columns represent different foods, > and X_ij is the amount of food j eaten by person i); and W is a matrix > of some other predictors (sex, age, ...). > > The second stage of the model is a regression of X on some food-level > predictors. > > Is it possible to fit this model in (the current version of) lmer()? > The challenge is that the persons are _not_ nested within food items, so > it is not a simple multilevel structure. > > We're planning to write a Gibbs sampler and fit the model directly, but > it would be convenient to be able to flt in lmer() as well to check. > > Andrew > > --- > > Reference: > > Witte, J. S., Greenland, S., Hale, R. W., and Bird, C. L. (1994). > Hierarchical regression analysis applied to a > study of multiple dietary exposures and breast cancer. Epidemiology 5, > 612-621. > > -- > Andrew Gelman > Professor, Department of Statistics > Professor, Department of Political Science > [EMAIL PROTECTED] > www.stat.columbia.edu/~gelman > > Statistics department office: > Social Work Bldg (Amsterdam Ave at 122 St), Room 1016 > 212-851-2142 > Political Science department office: > International Affairs Bldg (Amsterdam Ave at 118 St), Room 731 > 212-854-7075 > > Mailing address: > 1255 Amsterdam Ave, Room 1016 > Columbia University > New York, NY 10027-5904 > 212-851-2142 > (fax) 212-851-2164 > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > > -- Andrew Gelman Professor, Department of Statistics Professor, Department of Political Science [EMAIL PROTECTED] www.stat.columbia.edu/~gelman Statistics department office: Social Work Bldg (Amsterdam Ave at 122 St), Room 1016 212-851-2142 Political Science department office: International Affairs Bldg (Amsterdam Ave at 118 St), Room 731 212-854-7075 Mailing address: 1255 Amsterdam Ave, Room 1016 Columbia University New York, NY 10027-5904 212-851-2142 (fax) 212-851-2164 ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html