Re: [R] Can lmer() fit a multilevel model embedded in a regression?

From: Doran, Harold <HDoran_at_air.org>
Date: Sun 21 May 2006 - 22:55:25 EST


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:	r-help-bounces@stat.math.ethz.ch on behalf of Andrew Gelman
Sent:	Sat 5/20/2006 5:49 AM
To:	r-help@stat.math.ethz.ch
Cc:	reg26@columbia.edu
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
gelman@stat.columbia.edu
www.stat.columbia.edu/~gelman

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