search for: postvar

...can calculate one from the other, right? Just to make sure I'm not misunderstanding something. metaplot(CoeffVector, StdErrorVector, nn=NULL, labels=Name, conf.level=0.95, xlab=paste(CoeffNames[j], CoefficientName[i]), ylab="Countries", xlim=NULL, summn=PostCoeffs[1,j], sumse=sqrt(PostVars[1,j]), sumnn=1/(PostVars[1,j]), summlabel="Summary", lwd=2, boxsize=1) meta.summaries -------------- What does it mean, when I get the warning message that NaNs were produced in "pchisq(q, df, lower.tail, log.p)"? Is there something wrong with my data (there are no NAs in...

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Dear all, I am trying to get the emperical bayes estimates together with their standard errors out of lme4. Up to now I have used MLwiN to get these estimates. I have fitted the following - very simple - model, just to find out how this works. test<-lmer(y~(1|subject),data,REML=F) ranef(test,postVar=T) str(ranef(test,postVar=T) If I use the formulation of the emperical bayes estimates and their standard errors from Longford (1993), this comes down to using a shrinkage factor based on the level 1 and 2 variance and the sample size of the subject at hand (lambda=var(u)/{var(u)+var(e)/n}). Usin...

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction interval for the individual intercepts, not the conditional modes of the random effects, that is, the fixed effect (overall estimated intercept) + the conditional...

Hi R Users, When I use package lme4 for mixed model analysis, I can't distinguish the significant and insignificant variables from all random independent variables. Here is my data and result: Data: Rice<-data.frame(Yield=c(8,7,4,9,7,6,9,8,8,8,7,5,9,9,5,7,7,8,8,8,4,8,6,4,8,8,9), Variety=rep(rep(c("A1","A2","A3"),each=3),3),

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Hello! I wonder how can one get upper and lower limits of a prediction interval -- exact values? They are shown on caterpillar plot using ranef() with argument postVar=TRUE, but I would like to know them. A while ago, some discussions were opened on "Confidence Intervals for Random Effect BLUP's", but the answer was never clear: http://www.mail-archive.com/r-help at r-project.org/msg04820.html Thanks! PM