R help - Aug 2004 - type III sum of squares

R-help

What are the strengths and weakness of 'aov' in 'car' package?

My model looks something like this :

library(car)
aov(lm(fish.length~zone*area,data=my.data))

Thank you

Luis Ridao Cruz
Fiskiranns??knarstovan
N??at??n 1
P.O. Box 3051
FR-110 T??rshavn
Faroe Islands
Phone:             +298 353900
Phone(direct): +298 353912
Mobile:             +298 580800
Fax:                 +298 353901
E-mail:              luisr at frs.fo
Web:                www.frs.fo

Luis Rideau Cruz wrote:
> R-help
> 
> What are the strengths and weakness of 'aov' in 'car'
package?
Have you read John Fox's book this packages correpsonds to? Do it, it 
is great.

Uwe Ligges

> My model looks something like this :
> 
> library(car)
> aov(lm(fish.length~zone*area,data=my.data))
> 
> Thank you
> 
> Luis Ridao Cruz
> Fiskiranns??knarstovan
> N??at??n 1
> P.O. Box 3051
> FR-110 T??rshavn
> Faroe Islands
> Phone:             +298 353900
> Phone(direct): +298 353912
> Mobile:             +298 580800
> Fax:                 +298 353901
> E-mail:              luisr at frs.fo
> Web:                www.frs.fo
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html

>What are the strengths and weakness of 'aov' in 'car'
package?
>My model looks something like this :
> library(car)
> aov(lm(fish.length~zone*area,data=my.data))

'aov' is in the package 'stats', not in 'car'. (see
?aov)

One of the interests of 'aov' (compared to 'lm') is that using
the 'Error' term in the formula allows to analyse designs  where
sampling occurs at several levels (as in some though this term is not really
correct)..

You may be thinking of the *Anova* function in the car package (?)
'Anova(model)' allows to compute so-called 'type II' and
'type III' sums of squares
sparing you the need to play with the order of terms if you used 'anova'
(notice the lowercase).

Type III sums of square are useful in factorial designs with unequal number of
observations.
When the factors are coded with the contrasts contr.sum or contr.helmert, the
test for the main effect of a factor weights equally all subgroups
(see John Fox's book : "An R and S-plus Companion to Applied
Regression", p.140).

I discussed this topic in a recent thread on this list ([R] Re: Enduring LME
confusion.... or Psychologists and Mixed-Effects)

The archive of R-help contains several post about this topic.
 
I hope this helps.

Christophe Pallier
http://www.pallier.org