Sam Brown wrote:
Hi Michael
Thank you very much for the intel regarding eta^2. It is pretty much the sort of thing that I am wanting.
The latest developer version of the heplots package on R-Forge now
includes an initial implementation of etasq() for
multivariate linear models. Note that for s>1 dimensional tests, the
values of eta^2 differ according to the test statistic:
Pillai trace (default), Hotelling-Lawley trace, Wilks' Lambda, Roy
maximum root test. See ?heplots:::etasq for details.
> # install.packages("heplots",repos="http://R-Forge.R-project.org";)
> library(heplots)
> data(Soils) # from car package
> soils.mod <- lm(cbind(pH,N,Dens,P,Ca,Mg,K,Na,Conduc) ~ Block +
Contour*Depth, data=Soils)
> etasq(Anova(soils.mod))
eta^2
Block 0.5585973
Contour 0.6692989
Depth 0.5983772
Contour:Depth 0.2058495
> etasq(soils.mod) # same
eta^2
Block 0.5585973
Contour 0.6692989
Depth 0.5983772
Contour:Depth 0.2058495
> etasq(Anova(soils.mod), anova=TRUE)
Type II MANOVA Tests: Pillai test statistic
eta^2 Df test stat approx F num Df den Df Pr(>F)
Block 0.55860 3 1.6758 3.7965 27 81 1.777e-06 ***
Contour 0.66930 2 1.3386 5.8468 18 52 2.730e-07 ***
Depth 0.59838 3 1.7951 4.4697 27 81 8.777e-08 ***
Contour:Depth 0.20585 6 1.2351 0.8640 54 180 0.7311
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>
Found a good paper regarding all this:
Estimating an Effect Size in One-Way Multivariate Analysis of Variance (MANOVA)
H. S. Steyn Jr; S. M. Ellisa
Multivariate Behavioral Research
2009 44: 1, 106 — 129
http://www.informaworld.com/smpp/content~db=all~content=a908623057~frm=titlelink
This paper may be more confusing than helpful, since the emphasis is on
the use of eta^2 as measures of multivariate
'effect size' and I think they try to blend in too many different
threads from the effect-size literature. And they don't
discuss what happens in designs with more than one factor or regressor.
In the general case, etasq() calculates
measures of partial eta^2, reflecting the *additional* proportion of
variance associated with a given term in the
full model that includes it, relative to the reduced model that excludes
it, analogous to partial R^2 in univariate
regression models.
Date: Wed, 16 Jun 2010 10:11:05 -0400
From: frien...@yorku.ca
To: s_d_j_br...@hotmail.com
CC: r-help@r-project.org
Subject: Re: MANOVA proportion of variance explained
I think you are looking for a multivariate measure of association,
analogous to R^2 for a univariate linear model. If so, there are
extensions of eta^2 from univariate ANOVAs for each of the multivariate
test statistics, e.g.,
for Pillai (-Bartlett) trace and Hotelling-Lawley trace and a given
effect tested on p response measures
eta2(Pillai) = Pillai / s
eta2(HLT) = HLT / (HLT+s)
where s = min(df_h, p)
Alternatively, you could look at the candisc package which, for an
s-dimensional effect, gives a breakdown of the variance reflected in
each dimension of the latents roots of HE^{-1}
Sam Brown wrote:
Hello everybody
After doing a MANOVA on a bunch of data, I want to be able to make some comment
on the amount of variation in the data that is explained by the factor of
interest. I want to say this in the following way: XX% of the data is explained
by A.
I can acheive something like what I want by doing the following:
X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3, 2)))
Y <- structure(c(0, 2, 4, 0), .Dim = as.integer(c(2, 2)))
Z <- structure(c(3, 1, 2, 8, 9, 7), .Dim = as.integer(c(3, 2)))
U <- rbind(X,Y,Z)
m <- manova(U~as.factor(rep(1:3, c(3, 2, 3))))
summary(m,test="Wilks")
SS<-summary(m)$SS
(a<-mean(SS[[1]]/(SS[[1]]+SS[[2]])))
and concluding that 94% of variation is explained.
Is my desire misguided? If it is a worthy aim, is this a valid way of acheiving
it?
Thanks a lot!
Sam
Samuel Brown
Research assistant
Bio-Protection Research Centre
PO Box 84
Lincoln University
Lincoln 7647
Canterbury
New Zealand
sam.br...@lincolnuni.ac.nz
http://www.the-praise-of-insects.blogspot.com
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
_________________________________________________________________
Find a way to cure that travel bug MSN NZ Travel
http://travel.msn.co.nz/
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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