Curious - what would be the purpose of this regression? On Mon, Oct 4, 2010 at 4:39 PM, harez...@post.harvard.edu <jarek...@yahoo.com> wrote: > Dear R users, > An equivalence between linear mixed model formulation and penalized > regression > models (including the ridge regression and penalized regression splines) has > proven to be very useful in many aspects. Examples include the use of the > lme() > function in the library(nlme) to fit smooth models including the estimation > of a > smoothing parameter using REML. My question concerns the use of the linear > mixed > model software to fit a ridge regression with the number of columns in the > design matrix X (p) exceeding the number of observations (n). Has anybody in > the > R community implemented the LME-like approach with estimation of the variance > components using REML to find the coefficient estimates (BLUEs) and predictors > (BLUPs) in the ridge regression problem in the "p > n" setting? > > Sample code below summarizes my problem: > #################################################### > version$version.string > # [1] "R version 2.11.1 (2010-05-31)" > > library(nlme) > > # DATA generation: > dim <- 200 > n <- 50 > XX <- matrix(rnorm(dim*n, 0, 0.1), ncol=dim, nrow=n) > beta <- matrix(c(rep(1, 40), rep(2,20), rep(0,140)), ncol=1) > Y <- XX %*% beta + rnorm(n) > > # MODEL fit: > dummyId <- factor(rep(1,n)) > Z.block <- list(dummyId=pdIdent(~-1+XX)) > data.fr <- data.frame(Y,XX) > fit <- lme(Y~1, > data=data.fr, > random=Z.block) > > # ERROR: > Warning message: > In lme.formula(Y ~ 1, data = data.fr, random = Z.block) : > Fewer observations than random effects in all level 1 groups > ############################################################# > > Thank you in advance, > Jarek Harezlak > > > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >
-- Dimitri Liakhovitski Ninah Consulting www.ninah.com ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
