Hello all: I am interested in computing what the multilevel modeling literature calls a multiple membership model. More specifically, I am working with a data set involving clients and providers. The clients are the lower-level units who are nested within providers (higher-level). However, this is not nesting in the usual sense, as clients can belong to multple providers, which I understand makes this a "multiple membership model." Right now, I would like to keep this simple, using only a continuous dependent variable, but would like to also extend this to a repeated measures design. This doesn't seem to be possible with the lme package. Is there something else I could consider? Thanks, Brian NIMH Training Fellow GWB School of Social Work, PhD Program Washington University in St. Louis One Brookings Drive St. Louis, MO 63130 [[alternative HTML version deleted]]
Souds like a model with cross-classified random effects. Lme4 can handle this easily. Shige On 1/3/06, Brian Perron <beperron@wustl.edu> wrote:> > Hello all: > > I am interested in computing what the multilevel modeling literature calls > a multiple membership model. More specifically, I am working with a data > set involving clients and providers. The clients are the lower-level units > who are nested within providers (higher-level). However, this is not > nesting in the usual sense, as clients can belong to multple providers, > which I understand makes this a "multiple membership model." Right now, I > would like to keep this simple, using only a continuous dependent variable, > but would like to also extend this to a repeated measures design. This > doesn't seem to be possible with the lme package. Is there something else I > could consider? > Thanks, > Brian > > > NIMH Training Fellow > GWB School of Social Work, PhD Program > Washington University in St. Louis > One Brookings Drive > St. Louis, MO 63130 > > [[alternative HTML version deleted]] > > ______________________________________________ > 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 >[[alternative HTML version deleted]]
On Tue, 3 Jan 2006, Brian Perron wrote:> Hello all: > > I am interested in computing what the multilevel modeling literature > calls a multiple membership model. More specifically, I am working with > a data set involving clients and providers. The clients are the > lower-level units who are nested within providers (higher-level). > However, this is not nesting in the usual sense, as clients can belong > to multple providers, which I understand makes this a "multiple > membership model." Right now, I would like to keep this simple, using > only a continuous dependent variable, but would like to also extend this > to a repeated measures design. This doesn't seem to be possible with > the lme package. Is there something else I could consider? Thanks,I think you want lmer() in the lme4 & Matrix packages. It allows crossed random effects. -thomas
"Brian Perron" <beperron at wustl.edu> writes:> Hello all: > > I am interested in computing what the multilevel modeling literature > calls a multiple membership model. More specifically, I am working > with a data set involving clients and providers. The clients are the > lower-level units who are nested within providers (higher-level). > However, this is not nesting in the usual sense, as clients can > belong to multple providers, which I understand makes this a > "multiple membership model." Right now, I would like to keep this > simple, using only a continuous dependent variable, but would like > to also extend this to a repeated measures design. This doesn't seem > to be possible with the lme package. Is there something else I could > consider? Thanks, BrianYou could take a look at the lmer() function in the lme4/Matrix packages - see the Rnews 2005/1 article. One potential problem is that for repeated measurements, it is not (currently?) as strong on correlation structure as lme(). You can actually deal with crossed random effects in lme() too, it just gets a little more complicated, involving things like library(nlme) data(Assay) as1 <- lme(logDens~sample*dilut, data=Assay, random=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1), pdIdent(~dilut-1)))) as2 <- lme(logDens~sample*dilut, data=Assay, random=list(Block=pdBlocked(list( pdIdent(~1), pdIdent(~sample-1))),dilut=~1)) as3 <- lme(logDens~sample*dilut, data=Assay, random=list(Block=~1, Block=pdIdent(~sample-1), dilut=~1)) which all fit the same model (but get the DF wrong in three different ways...) This is slightly different from your example because the crossed factors are nested in "Block", but you can always fake a nesting using one <- rep(1, length(logDens)) #or whatever lme(...., random=list(one=~....)) -- O__ ---- Peter Dalgaard ??ster Farimagsgade 5, Entr.B c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907