search for: fm1orthf

Hi all, To follow up on an older thread, it was suggested that the following would produce confidence intervals for the estimated BLUPs from a linear mixed effect model: OrthoFem<-Orthodont[Orthodont$Sex=="Female",] fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) fm1.s <- coef(fm1OrthF.)$Subject fm1.s.var <- fm1OrthF. at bVar$Subject 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)) [,1]...

...but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and lme4 loaded at same time!) # OrthoFem<-Orthodont[Orthodont$Sex=="Female",] # http://tolstoy.newcastle.edu.au/R/help/06/03/23758.html library(lme4) fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) lmer(distance~age+(age|Subject), data=OrthoFem)@bVar$Subject[2,2,]* (attr(VarCorr(lmer(distance~age+(age| Subject),data=OrthoFem)),"sc")^2)[1] (attr(VarCorr(fm1OrthF.),"sc")^2)[1] fm1.s <- coef(fm1OrthF.)$Subje...

I can ask my question using and example from Chapter 1 of Pinheiro & Bates. > # 1.4 An Analysis of Covariance Model > > OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] > fm1OrthF <- + lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) > summary( fm1OrthF ) Linear mixed-effects model fit by REML Data: OrthoFem AIC BIC logLik 149.2183 156.169 -70.60916 Random effects: Formula: ~1 | Subject (Intercept) Residual StdDev:...

...----------------- >library(nlme) > names( Orthodont ) [1] "distance" "age" "Subject" "Sex" > levels( Orthodont$Sex ) [1] "Male" "Female" > OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] > > fm1OrthF <- lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) > fm2OrthF <- update( fm1OrthF, random = ~ age | Subject ) > orthLRTsim <- simulate.lme( fm1OrthF, fm2OrthF, nsim = 1000 ) > plot( orthLRTsim, df = c(1, 2) ) # produces Figure 2.3 Error in if ((dfType <- as.do...

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_01/0000000b/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

Dear r-helpers, After successfully running require(nlme) vfr.lmL <- lmList( estimate ~ (slant + respType + visField + hand)^2 | subject, vfr ) pairs(vfr.lmL, id = 0.01, adj = -0.5) # Pinheiro & Bates (p. 141) produces the following error: Error in sprintf(gettext(fmt, domain = domain), ...) : object "form" not found Any guesses as to what I may have done wrong?