Trouble Replicating Unstructured Mixed Procedure in R

-----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 Charles Determan Jr
Sent: January-26-12 4:07 PM
To: John Maindonald
Cc: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Trouble Replicating Unstructured Mixed Procedure in
R

The only thing I am looking for is the appropriate R code to replicate the
SAS analysis shown in the previously mentioned paper.  That is all I ask.
What should the code be in order to analyze this 'dental' data to replicate
the 'UN' or 'unstructured' analysis in the prior paper.

Regards,

Charles

On Thu, Jan 26, 2012 at 4:37 PM, John Maindonald
<john.maindonald at anu.edu.au

wrote:

It is not really a matter of computational accuracy.  One can get highly
accurate values for an inappropriate statistic.

Or if there is insistence on using the word, accuracy, what is the
meaning?

i) the wrong formula is used?  Then in what sense is it 'wrong'?

ii) there is a numerical inaccuracy in the calculation?  This is almost
never an issue in a relatively simple calculation such as this, given
the care taken by the code writers in such matters.

iii) where an approximation is used, as in using an F-distribution
approximation, is the best choice of degrees of freedom  made to
for use of this approximation?  I judge that the degrees of freedom
for lme's F-statistic for the interaction are not well chosen.  Users
really have to sort this out for themselves, rather than relying on
what may be a fairly wild approximation that appears in lm's
output.  Using 75df rather than 25df does not however make the
difference that a choice between (e.g.) 5df and 25df would.

A further and more basic issue is whether the statistic that is
provided is appropriate to the intended generalisation.  I'd take
this to be generalisation to another sample of youths from the
same population.  In order to understand why R and SAS are
giving different F-statistics for the interaction, one needs to
understand just what variance-covariance structure is assumed
in each case.  One might extract the two estimates of the
var-cov structure and compare them. Look for terms in one that
do not appear, or maybe that are zero, in the other.

Finally, it is not just that Venables does not like type III SS.
He is saying that they almost never correspond to a null
hypothesis that makes any sense.  Those who disagree try to
write down the model to which the null hypothesis corresponds
in testing for the main effect of factor1 with a factor1:factor2
interaction.

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 27/01/2012, at 2:41 AM, Thompson,Paul wrote:

OK, I've looked at that reference.

There are 2 aspects of an estimate like a SS. The first is the

stability

of the estimate, and the second is the interpretation of the estimate.

The

issues with the interpretation of the different estimates go back to

1970,

and they are simply a matter of interpretation. The point of the Venables
discussion is that he does not like Type III SS, not that they are wrong.
He does not agree with the interpretation.

The issue here is the accuracy of the Type III or Type I or Type II or

whatever. Accuracy comes before interpretation. If the r module and SAS

do

not arrive at the same estimates, that is an important thing.

Once we agree upon computation, we can argue about interpretation.

Charles Determan is inquiring as to computational accuracy. The use and
interpretation of the various Type I, II, III, IV, LVX SS are secondary.

-----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 Luca Borger

Sent: Thursday, January 26, 2012 9:03 AM
To: r-sig-mixed-models at r-project.org
Subject: Re: [R-sig-ME] Trouble Replicating Unstructured Mixed

Procedure

in R

I think:

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

HTH
Luca



Le 26/01/2012 15:52, Thompson,Paul a ?crit :

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)                                         ,-

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| email: davidD at qimr.edu.au  ph: INT+61+7+3362-0217 fax: -0101  /

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