Thank you Peter. Actually 3 people from mixed model mailing list tried my code using lmer(). They got the same results as what I got from lme4(). So they couldn't replicate my lmer() results:
Random effects: Groups Name Variance Std.Dev. eye:id (Intercept) 3.59531 1.89613 id (Intercept) 3.51025 1.87357 Residual 0.01875 0.13693 Number of obs: 640, groups: eye:id, 160; id, 80 The only difference they can think of is they are using Mac and FreeBSD while mine is PC. I can't imagine this can be the reason. I re-install lme4 package, but still got weird results with lmer(). Per your suggestion, here is the results for aov() summary(aov(score~trt+Error(id/eye), data=dat)) Error: id Df Sum Sq Mean Sq F value Pr(>F) trt 7 1353.6 193.378 4.552 0.0002991 *** Residuals 72 3058.7 42.482 --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Error: id:eye Df Sum Sq Mean Sq F value Pr(>F) Residuals 80 1152 14.4 Error: Within Df Sum Sq Mean Sq F value Pr(>F) Residuals 480 9 0.01875 As can be seen, thr within subject variance estimate (0.01875) is the same between aov, lmer and lme. But I am not sure how to relate results between aov and lmer/lme for the other 2 variance components (id and eye%in%id). Thanks John ----- Original Message ---- From: Peter Dalgaard <pda...@gmail.com> To: array chip <arrayprof...@yahoo.com> Cc: r-help@r-project.org Sent: Fri, September 17, 2010 1:05:27 PM Subject: Re: [R] lmer() vs. lme() gave different variance component estimates On 09/17/2010 09:14 PM, array chip wrote: > Hi, I asked this on mixed model mailing list, but that list is not very > active, > > so I'd like to try the general R mailing list. Sorry if anyone receives the > double post. > > > Hi, I have a dataset of animals receiving some eye treatments. There are 8 > > treatments, each animal's right and left eye was measured with some scores > (ranging from 0 to 7) 4 times after treatment. So there are nesting groups > eyes > > within animal. Dataset attached > >> dat<-read.table("dat.txt",sep='\t',header=T,row.names=1) >> dat$id<-factor(dat$id) >> str(dat) > 'data.frame': 640 obs. of 5 variables: > $ score: int 0 2 0 7 4 7 0 2 0 7 ... > $ id : Factor w/ 80 levels "1","3","6","10",..: 7 48 66 54 18 26 38 52 39 > 63 > ... > $ rep : int 1 1 1 1 1 1 1 1 1 1 ... > $ eye : Factor w/ 2 levels "L","R": 2 2 2 2 2 2 2 2 2 2 ... > $ trt : Factor w/ 8 levels "A","B","C","Control",..: 1 1 1 1 1 1 1 1 1 1 ... > > I fit a mixed model using both lmer() from lme4 package and lme() from nlme > package: > >> lmer(score~trt+(1|id/eye),dat) > > Linear mixed model fit by REML > Formula: score ~ trt + (1 | id/eye) > Data: dat > AIC BIC logLik deviance REMLdev > 446.7 495.8 -212.4 430.9 424.7 > Random effects: > Groups Name Variance Std.Dev. > eye:id (Intercept) 6.9208e+00 2.630742315798 > id (Intercept) 1.4471e-16 0.000000012030 > Residual 1.8750e-02 0.136930641909 > Number of obs: 640, groups: eye:id, 160; id, 80 > >> summary(lme(score~trt, random=(~1|id/eye), dat)) > > Linear mixed-effects model fit by REML > Data: dat > AIC BIC logLik > 425.1569 474.0947 -201.5785 > > Random effects: > Formula: ~1 | id > (Intercept) > StdDev: 1.873576 > > Formula: ~1 | eye %in% id > (Intercept) Residual > StdDev: 1.896126 0.1369306 > > As you can see, the variance components estimates of random effects are quite > different between the 2 model fits. From the data, I know that the variance > component for "id" can't be near 0, which is what lmer() fit produced, so I > think the lme() fit is correct while lmer() fit is off. This can also be seen > from AIC, BIC etc. lme() fit has better values than lmer() fit. > > > I guess this might be due to lmer() didn't converge very well, is there > anyway > to adjust to make lmer() converge better to get similar results as lme()? That's your guess... I'd be more careful about jumping to conclusions. If this is a balanced data set, perhaps you could supply the result of summary(aov(score~trt+Error(id/eye), data=dat)) The correct estimates should be computable from the ANOVA table. -- Peter Dalgaard Center for Statistics, Copenhagen Business School Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.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.