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

[John Maindonald]

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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