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