[R-sig-ME] mcmcpvalue and contrasts
Ken Beath
kjbeath at kagi.com
Tue Feb 26 05:46:22 CET 2008
On 26/02/2008, at 1:22 PM, Steven McKinney wrote:
>
> Hi Hank,
>
>>
>> -----Original Message-----
>> From: r-sig-mixed-models-bounces at r-project.org on behalf of Ken Beath
>> Sent: Mon 2/25/2008 4:05 PM
>> To: Hank Stevens
>> Cc: Help Mixed Models
>> Subject: Re: [R-sig-ME] mcmcpvalue and contrasts
>>
>> On 26/02/2008, at 9:42 AM, Hank Stevens wrote:
>>
>>> Hi Folks,
>>> I wanted to double check that my intuition makes sense.
>>>
>>> Examples of mcmcpvalue that I have seen use treatment "contrast"
>>> coding.
>>> However, in more complex designs, testing overall effects of a
>>> factor
>>> might be better done with other contrasts, such as sum or Helmert
>>> contrasts.
>>>
>>> My Contention:
>>> Different contrasts test different hypothesis, and therefore
>>> result in
>>> different P-values. This consequence of contrasts differs from
>>> analysis of variance, as in anova( lm(Y ~ X1*X2) ).
>>>
>>> *** This is right, isn't it? ***
>>>
>>
>> The main problem is testing for a main effect in the presence of
>> interaction. While it looks like it gives sensible results in some
>> cases like balanced ANOVA, they really aren't sensible and the effect
>> of parameterisation in other cases makes that clear.
>>
>> The difference for the interaction is probably just sampling
>> variation, increasing samples fixes this.
>>
>> Ken
>
> Ken is correct - testing some of the main effect terms resulting from
> different parameterizations due to the differing contrast structures
> will yield different results (though they in general will be somewhat
> meaningless if the corresponding interaction term is in the model
> and you do not have a balanced orthogonal design).
>
Even with orthogonal designs there is still a problem with
interpretation. If we have a model with A*B and the interaction and B
are significant, then it seems that the conclusion about B is limited
to the choice of A in the experiment. An assumption that the effect
of B will be the same with a different set of A seems rather risky,
although it seems to be what the FDA expect for multi-centre trials.
If there is some need to generalise then a model allowing for a
random effect for B seems more sensible.
Ken
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