Sorry for last mail's mistake.
I recheck the difference between r1 and r2.Both result of anova and summary
are the same.


2011/7/28 Lao Meng <laomeng.3@gmail.com>

> 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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