On 12/29/05, Doran, Harold <[EMAIL PROTECTED]> wrote: > Uli: > > The graphic in the paper, sometimes called a catepillar plot, must be > created with some programming as there is (as far as I know) not a > built-in function for such plots. As for the contents of bVar you say > the dimensions are 2,2,28 and there are two random effects and 28 > schools. So, from what I know about your model, the third dimension > represents the posterior covariance matrix for each of your 28 schools > as Spencer notes. > > For example, consider the following model > > library(Matrix) > > library(mlmRev) > > fm1 <- lmer(math ~ 1 + (year|schoolid), egsingle) > > Then, get the posterior means (modes for a GLMM) > > [EMAIL PROTECTED] > > These data have 60 schools, so you will see ,,1 through ,,60 and the > elements of each matrix are posterior variances on the diagonals and > covariances in the off-diags (upper triang) corresponding to the > empirical Bayes estimates for each of the 60 schools. > > , , 1 > > [,1] [,2] > [1,] 0.01007129 -0.001272618 > [2,] 0.00000000 0.004588049
I'd have to go back and check but I think that these are the upper triangles of the symmetric matrix (as Spencer suggested) that are the conditional variance-covariance matrices of the two-dimensional random effects for each school up to a scale factor. That is, I think each face needs to be multiplied by s^2 to get the actual variance-covariance matrix. > > > Does this help? > > Harold > > > -----Original Message----- > From: [EMAIL PROTECTED] > [mailto:[EMAIL PROTECTED] On Behalf Of Spencer Graves > Sent: Thursday, December 29, 2005 6:58 AM > To: Ulrich Keller > Cc: r-help > Subject: Re: [R] bVar slot of lmer objects and standard errors > > Have you received a satisfactory reply to this post? I > haven't seen one. Unfortunately, I can't give a definitive answer, but > I can offer an intelligent guess. With luck, this might encourage > someone who knows more than I do to reply. If not, I hope these > comments help you clarify the issue further, e.g., by reading the source > or other references. > > I'm not not sure, but I believe that > [EMAIL PROTECTED],,i] is the upper triangular part of the > covariance matrix of the random effects for the i-th level of schoolid. > The lower triangle appears as 0, though the code (I believe) iterprets > it as equal to the upper triangle. More precisely, I suspect it is > created from something that is stored in a more compact form, i.e., > keeping only a single copy of the off-diagonal elements of symmetric > matrices. I don't seem to have access to your "nlmframe", so I can't > comment further on those specifics. You might be able to clarify this > by reading the source code. I've been sitting on this reply for several > days without finding time to do more with it, so I think I should just > offer what I suspect. > > The specifics of your question suggest to me that you want to > produce something similar to Figure 1.12 in Pinheiro and Bates (2000) > Mixed-Effects Models in S and S-Plus (Springer). That was produced from > an "lmList" object, not an "lme" object, so we won't expect to get their > exact answers. Instead, we would hope to get tighter answers available > from pooling information using "lme"; the function "lmList" consideres > each subject separately with no pooling. With luck, the answers should > be close. > > I started by making a local copy of the data: > > library(nlme) > OrthoFem <- Orthodont[Orthodont$Sex=="Female",] > > Next, I believe to switch to "lme4", we need to quit R > completely and restart. I did that. Then with the following sequence > of commands I produced something that looked roughly similar to the > confidence intervals produced with Figure 1.12: > > library(lme4) > fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) > > fm1.s <- coef(fm1OrthF.)$Subject > fm1.s.var <- [EMAIL PROTECTED] > fm1.s0.s <- sqrt(fm1.s.var[1,1,]) > fm1.s0.a <- sqrt(fm1.s.var[2,2,]) > fm1.s[,1]+outer(fm1.s0.s, c(-2,0,2)) > fm1.s[,2]+outer(fm1.s0.a, c(-2,0,2)) > > hope this helps. > Viel Glueck. > spencer graves > > Ulrich Keller wrote: > > > Hello, > > > > I am looking for a way to obtain standard errors for emprirical Bayes > estimates of a model fitted with lmer (like the ones plotted on page 14 > of the document available at > http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/000000 > 0b/80/2b/b3/94.pdf). > > > Harold Doran mentioned > (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) > that the posterior modes' variances can be found in the bVar slot of > lmer objects. However, when I fit e.g. this model: > > > > lmertest1<-lmer(mathtot~1+(m_escs_c|schoolid),hlmframe) > > > > then [EMAIL PROTECTED] is a three-dimensional array with > dimensions (2,2,28). > The factor schoolid has 28 levels, and there are random effects for the > intercept and m_escs_c, but what does the third dimension correspond to? > In other words, what are the contents of bVar, and how can I use them to > get standard errors? > > > > Thanks in advance for your answers and Merry Christmas, > > > > Uli Keller ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
