Hi Bert,
Thanks for the suggestions. I tried lme with different
control parameters, and also tried using "ML", instaed
of "REML", but still got the same answers.
Yes, I hope some gurus on this list could give me some
hints.
Thanks
--- "Gunter, Bert" <bert_gunter at merck.com>
wrote:> But they are close. This is almost certainly a
> numeric issue -- if you set
> your control parameters in lme so that you run it
> longer (make the stopping
> criteria tighter), I'll bet you converge to the same
> results.
>
> ... or it might be some some similar abstruse
> problem in aov.
>
> Interesting, though. I'll be interested in hearing
> what the "gurus" have to
> say (of which I am NOT one)
>
> Cheers,
>
> Bert Gunter
> Biometrics Research RY 33-300
> Merck & Company
> P.O. Box 2000
> Rahway, NJ 07065-0900
> Phone: (732) 594-7765
> mailto: bert_gunter at merck.com
>
> "The business of the statistician is to catalyze the
> scientific learning
> process." -- George E.P. Box
>
>
>
> -----Original Message-----
> From: array chip [mailto:arrayprofile at yahoo.com]
> Sent: Thursday, October 02, 2003 12:42 PM
> To: s-news at wubios.wustl.edu
> Cc: R-help at stat.math.ethz.ch
> Subject: [S] lme vs. aov with Error term
>
>
> Hi,
>
> I have a question about using "lme" and "aov" for
> the
> following dataset. If I understand correctly, using
> "aov" with an Error term in the formula is
> equivalent
> to using "lme" with default settings, i.e. both
> assume
> compound symmetry correlation structure. And I have
> found that equivalency in the past. However, with
> the
> follwing dataset, I got different answers with using
> "lme" and using "aov", can anyone explain what
> happened here? I have 2 differnt response variables
> "x" and "y" in the following dataset, they are
> actually slightly different (only 3 values of them
> are
> different). With "y", I achieved the equivalency
> between "lme" and "aov"; but with "x", I got
> different
> p values for the ANOVA table.
>
> -------
>
>
x<-c(-0.0649,-0.0923,-0.0623,0.1809,0.0719,0.1017,0.0144,-0.1727,-0.1332,0.0>
986,0.304,-0.4093,0.2054,0.251,-0.1062,0.3833,0.0649,0.2908,0.1073,0.0919,0.> 1167,0.2369,0.306,0.1379)
>
>
y<-c(-0.0649,-0.0923,0.32,0.08,0.0719,0.1017,0.05,-0.1727,-0.1332,0.15,0.304>
,-0.4093,0.2054,0.251,-0.1062,0.3833,0.0649,0.2908,0.1073,0.0919,0.1167,0.23> 69,0.306,0.1379)
>
>
treat<-as.factor(c(1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2))>
time<-as.factor(c(1,1,1,1,2,2,2,2,3,3,3,3,1,1,1,1,2,2,2,2,3,3,3,3))>
sex<-as.factor(c('F','F','M','M','F','F','M','M','F','F','M','M','F','F','M'>
,'M','F','F','M','M','F','F','M','M'))
> subject<-as.factor(c(rep(1:4,3),rep(5:8,3)))
>
xx<-cbind(x=data.frame(x),y=y,treat=treat,time=time,sex=sex,subject=subject)>
> ######## using x as dependable variable
>
> xx.lme<-lme(x~treat*sex*time,random=~1|subject,xx)
> xx.aov<-aov(x~treat*sex*time+Error(subject),xx)
>
> summary(xx.aov)
>
> Error: subject
> Df Sum Sq Mean Sq F value Pr(>F)
> treat 1 0.210769 0.210769 6.8933 0.05846 .
> sex 1 0.005775 0.005775 0.1889 0.68627
> treat:sex 1 0.000587 0.000587 0.0192 0.89649
> Residuals 4 0.122304 0.030576
> ---
> Signif. codes: 0 `***' 0.001 `**' 0.01 `*' 0.05 `.'
> 0.1 ` ' 1
>
> Error: Within
> Df Sum Sq Mean Sq F value Pr(>F)
> time 2 0.00102 0.00051 0.0109 0.9891
> treat:time 2 0.00998 0.00499 0.1066 0.9002
> sex:time 2 0.02525 0.01263 0.2696 0.7704
> treat:sex:time 2 0.03239 0.01619 0.3458 0.7178
> Residuals 8 0.37469 0.04684
>
> anova(xx.lme)
> numDF denDF F-value p-value
> (Intercept) 1 8 3.719117 0.0899
> treat 1 4 5.089022 0.0871
> sex 1 4 0.139445 0.7278
> time 2 8 0.012365 0.9877
> treat:sex 1 4 0.014175 0.9110
> treat:time 2 8 0.120538 0.8880
> sex:time 2 8 0.304878 0.7454
> treat:sex:time 2 8 0.391012 0.6886
>
> #### using y as dependable variable
>
> xx.lme2<-lme(y~treat*sex*time,random=~1|subject,xx)
> xx.aov2<-aov(y~treat*sex*time+Error(subject),xx)
>
> summary(xx.aov2)
>
> Error: subject
> Df Sum Sq Mean Sq F value Pr(>F)
> treat 1 0.147376 0.147376 2.0665 0.2239
> sex 1 0.000474 0.000474 0.0067 0.9389
> treat:sex 1 0.006154 0.006154 0.0863 0.7836
> Residuals 4 0.285268 0.071317
>
> Error: Within
> Df Sum Sq Mean Sq F value Pr(>F)
> time 2 0.009140 0.004570 0.1579 0.8565
> treat:time 2 0.012598 0.006299 0.2177 0.8090
> sex:time 2 0.043132 0.021566 0.7453 0.5049
> treat:sex:time 2 0.069733 0.034866 1.2050 0.3488
> Residuals 8 0.231480 0.028935
>
> anova(xx.lme2)
> numDF denDF F-value p-value
> (Intercept) 1 8 3.0667809 0.1180
> treat 1 4 2.0664919 0.2239
> sex 1 4 0.0066516 0.9389
> time 2 8 0.1579473 0.8565
> treat:sex 1 4 0.0862850 0.7836
> treat:time 2 8 0.2177028 0.8090
> sex:time 2 8 0.7453185 0.5049
> treat:sex:time 2 8 1.2049883 0.3488
>
>
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