Yes,I checked the difference between the two command:
r1<-lme(sqrt(CD4) ~ group + group:time+0,random = ~time|id,data=data_ori)
r2<-lme(sqrt(CD4) ~ group + group:time-1,random = ~time|id,data=data_ori)

anova's result (i.e anova(r1) and anova(r2)) are totally the same.
summary's result is mainly same,and the only difference is:
r1 has the content:
Correlation

group1:group2
... ...
... ...
... ...

But r2 hasn't the above content.

That's the only difference.




2011/7/27 ONKELINX, Thierry <Thierry.ONKELINX@inbo.be>

> Both fit the same model but the parametrisation is different. Hence the
> hypotheses tested by summary() or anova() can be different as well. It's up
> to you to determine which parametrisation is the most useful.
>
> Essentially, all models are wrong, but some are useful (George Box)
>
>
>
> Van: Lao Meng [mailto:laomeng.3@gmail.com]
> Verzonden: woensdag 27 juli 2011 9:57
> Aan: ONKELINX, Thierry
> CC: r-sig-mixed-models@r-project.org
> Onderwerp: Re: [R-sig-ME] question about the lme result
>
> Thanks Thierry.It works well!
>
> According to your suggestion,I find that the following command has the same
> effect:
> lme(sqrt(CD4) ~ group + group:time-1,random = ~time|id,data=data_ori)
>
> From the result,can I say that maybe the opinion that "a*b = a + a:b" is
> not "right"?
>
> My best
>
>
> 2011/7/27 ONKELINX, Thierry <Thierry.ONKELINX@inbo.be>
> Dear Lao,
>
> Rewrite your model as lme(sqrt(CD4) ~ 0 + group + group:time,random =
> ~time|id,data=data_ori)
>
> Best regards,
>
> Thierry
>
> > -----Oorspronkelijk bericht-----
> > Van: r-sig-mixed-models-bounces@r-project.org [mailto:
> r-sig-mixed-models-
> > bounces@r-project.org] Namens Lao Meng
> > Verzonden: woensdag 27 juli 2011 9:26
> > Aan: r-sig-mixed-models@r-project.org
> > Onderwerp: [R-sig-ME] question about the lme result
> >
> > Hi all.
> > My data is a longitudinal data,which measure everyone(id)'s CD4 level at
> some
> > specific time point.All persons are grouped into 3 groups(0,1,2,3).
> >
> > y:sqrt(CD4)
> > x:time,GROUP(3levels: 0,1,2,3)
> >
> > data_ori is my original data,which inclues:CD4,group,time,id.
> >
> > I performed a linear mixed model using lme:
> > result <- lme(sqrt(CD4) ~ group*time,random = ~time|id,data=data_ori)
> >
> > >From the summary(result),I can only get the intercept and slope of
> > GROUP0(which is described as "intercept" and "time").
> > If I wanna get intercepts and slopes of GROUP1,GROUP2,GROUP3,how can I
> > do?
> >
> > Simply,I can do this manually by the following steps(GROUP1 for
> instance):
> > "intercept" + GROUP1 = intercept_GROUP1; "time" + time:GROUP1 =
> > slope_GROUP1;
> >
> > But is there any "shortcut" method to choose?Also,I cann't get the
> > corresponding SE and p value for each parameter.So the above
> > "manually"method is not effective to some degree.
> >
> > Any help from you are welcome.
> >
> > Many thanks!
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-mixed-models@r-project.org mailing list
> > https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models
>
>

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