[R-sig-ME] Trouble Replicating Unstructured Mixed Procedure in R

[Luca Borger]

I think:

http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf

HTH
Luca



Le 26/01/2012 15:52, Thompson,Paul a écrit :
> I am unfamiliar with this critique of Type III SS. Can you point me to a reference discussing the difficulties with Type III SS?
>
> -----Original Message-----
> From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of John Maindonald
> Sent: Wednesday, January 25, 2012 11:19 PM
> To: David Duffy
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Trouble Replicating Unstructured Mixed Procedure in R
>
> It is well to note that type III sums of squares are problematic.
> For testing the effects of a main effect, the null model is constraining
> the main effect in a manner that depends on the parameterisation.
>
> There are situations where it makes sense to fit interactions without
> main effects, and it is clear what constraint on the main effect is the
> relevant null (with an interaction between a factor and a variable,
> does one want all lines to go though the same point, or through
> perhaps the origin?), but that situation is unusual.  For lines that
> are separate or all through the one point, one does not need
> type III sums of squares.
>
> Analyses often or frequently have enough genuine complications
> worrying (unless it is blindingly obvious that one ought to worry
> about it) without the rarely relevant complication of attending to a
> type III sum of squares.
>
> I'd guess that SAS and lme are, effectively, making different
> assumptions about the intended generalisation.  They are
> clearly using different denominator degrees of freedom for F.
> As one is looking for consistency across the 27 different youths,
> SAS's denominator degrees of freedom for the interaction seem
> more or less right, pretty much equivalent to calculating slopes
> for females and slopes for males and using a t-test to compare
> them.  (Sure, in the analyses presented, age has been treated
> as a categorical variable, but the comment still applies.)
>
> John Maindonald             email: john.maindonald at anu.edu.au
> phone : +61 2 (6125)3473    fax  : +61 2(6125)5549
> Centre for Mathematics&  Its Applications, Room 1194,
> John Dedman Mathematical Sciences Building (Building 27)
> Australian National University, Canberra ACT 0200.
> http://www.maths.anu.edu.au/~johnm
>
> On 26/01/2012, at 1:54 PM, David Duffy wrote:
>
>> On Tue, 24 Jan 2012, Charles Determan Jr wrote:
>>
>>> Greetings,
>>>
>>> I have been working on R for some time now and I have begun the endeavor of
>>> trying to replicate some SAS code in R.  I have scoured the forums but
>>>
>> This is also the Orthodont dataset, distributed with nlme.
>>
>> As David Atkins pointed out, R defaults to Type I SS. so you would need to use, for example, the Anova() command from the car package.  The other thing is that the SAS F statistics are only approximate, depending on which covariance structure is chosen (perhaps John Maindonald or someone clever could comment), so SAS offers different possibilities for ddf eg
>>
>> http://www2.sas.com/proceedings/sugi26/p262-26.pdf
>>
>> while lme and lmer offer one or none.
>>
>> -- 
>> | David Duffy (MBBS PhD)                                         ,-_|\
>> | email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /     *
>> | Epidemiology Unit, Queensland Institute of Medical Research   \_,-._/
>> | 300 Herston Rd, Brisbane, Queensland 4029, Australia  GPG 4D0B994A v
>>
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