[R-sig-ME] ghlt different results for different hypotheses?

[m.fenati at libero.it]

Thanks a lot also for your good example!

Regards

Massimo


>----Messaggio originale----
>Da: 538280 at gmail.com
>Data: 10/02/2012 18.36
>A: "m.fenati at libero.it"<m.fenati at libero.it>
>Cc: <r-sig-mixed-models at r-project.org>
>Ogg: Re: [R-sig-ME] ghlt different results for different hypotheses?
>
>The more comparisons/tests that you do the more opportunities you have
>of getting a type I error.  The multiple comparisons procedures adjust
>for the number of comparisons so that the overall probability of
>making at least 1 type I error is fixed.  So the more comparisons the
>more adjustment needs to be made.
>
>Think of this simple example.  You are playing a game where you are
>trying to throw a wadded up piece of paper into a basket, you win if
>you get it in at least once.  What are your chances of winning if you
>get 10 tries compared to if you get 20 tries (from the same spot)?  If
>you want the same chance of winning with 20 tries as you had for 10
>tries (or 1 try), then you need to move further away or some other
>penalty.
>
>So with glht there is a bigger penalty when you do more comparisons
>since there are more opportunities of making a type I error.
>
>On Thu, Feb 9, 2012 at 3:25 AM, m.fenati at libero.it <m.fenati at libero.it> 
wrote:
>>
>>
>> Dear R users,
>> I would like to understand a simple problem related to glht() multeplicity 
correction and linear Hypotheses testing. Given a simple lme model with two 
predictors (group = 3 levels; time =  2 levels) and their interaction with 
treatment contrast, I see that the p-values are lower and higher when I test 
few or many hypotheses respectively. Because I dont't have a deep knowledge of 
multiple comparison theory, I ask you some suggestion or explanation about the 
different obtained results.
>> As you can see in the example below, "m1" and "m2" test a different number 
of hypotheses but comparing the same hypothesis a different results occurred.
>>
>>
>> time<-rep(c(rep(0,8),rep(1,8)),3)
>> group<-c(rep(0,16),rep(1,16),rep(2,16))
>> id<-c(rep(1:8,2),rep(9:16,2),rep(17:24,2))
>> w<-c(172.9, 185.8, 173.1, 187.3, 161.6, 167.1, 168.4, 161.1, 166.5, 175.3, 
167.1, 181.9, 163.0, 167.7, 172.1, 170.3, 167.2, 183.3, 160.7,167.8, 149.6, 
159.1, 164.2, 171.0, 168.6, 173.5, 161.8, 166.5, 148.4, 167.1, 166.8, 166.6, 
150.6, 178.4, 166.4, 159.2, 163.2, 167.8, 136.6, 161.8, 166.1, 175.8, 175.6, 
166.2, 168.5, 170.5, 152.0, 164.4)
>> dati<-data.frame(time,group,id,w)
>> dati$time<-as.factor(dati$time)
>> dati$group<-as.factor(dati$group)
>> dati$id<-as.factor(dati$id)
>>
>>
>>
>>
>> kp<-rbind("after Treatment: Group 1 - Controls"=c(0,1,0,0,0,0),
>>         "after Treatment: Group 2 - Controls"=c(0,0,1,0,0,0),
>>         "before Treatment: Group 1 - Controls"=c(0,1,0,0,1,0),
>>         "before Treatment: Group 2 - Controls"=c(0,0,1,0,0,1),
>>         "Controls: time trend (T1 - T0)"=c(0,0,0,-1,0,0),
>>         "Group 1: time trend (T1 - T0)"=c(0,0,0,-1,-1,0),
>>         "Group 2: time trend (T1 - T0)"=c(0,0,0,-1,0,-1))
>>
>>
>> k<-rbind("after Treatment: Group 1 - Controls"=c(0,1,0,0,0,0),
>>         "after Treatment: Group 2 - Controls"=c(0,0,1,0,0,0),
>>         "before Treatment: Group 1 - Controls"=c(0,1,0,0,1,0),
>>         "before Treatment: Group 2 - Controls"=c(0,0,1,0,0,1)
>>         )
>>
>>
>>
>>
>> w.lme<-lme(w~group*time,data=dati,random=~1|id)
>> m1<-summary(glht(w.lme,kp))
>> m2<-summary(glht(w.lme,k))
>>
>>
>> Thank in advances for your suggestions
>>
>>
>> Massimo
>>        [[alternative HTML version deleted]]
>>
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>
>
>
>-- 
>Gregory (Greg) L. Snow Ph.D.
>538280 at gmail.com
>