Hi, Having searched google '[R] aov effect size' without any results I wonder if I not completely miss something. Is there any R function that calculates the effect size of an AOV's main effect or interaction effect? It should be related to the F's and the degree of freedom of the error, but the multitude in numbers in aov() baffle me a bit. Thanks, ---david
I think you want to call summary.lm on the aov object, but this depends on what you mean by `effect size'. For example, R does have an effects() function, and that might be what you want. On Mon, 15 Mar 2004, David A. van Leeuwen wrote:> Having searched google '[R] aov effect size' without any results I > wonder if I not completely miss something. > > Is there any R function that calculates the effect size of an AOV's main > effect or interaction effect? It should be related to the F's and the > degree of freedom of the error, but the multitude in numbers in aov() > baffle me a bit.Given that, I wonder if you are used to standard terminology. -- Brian D. Ripley, ripley at stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595
ETA2 <- function(lm.obj){
cat("\n ETA2:\n\n")
tmp <- anova(lm.obj)
print(round(tmp$'Sum Sq'/sum(tmp$'Sum Sq'),4))
}
At 10:58 p.m. 16/03/04 +0100, you wrote:>Prof Brian Ripley wrote:
>
>>I think you want to call summary.lm on the aov object, but this depends
>>on what you mean by `effect size'.
>>
>>
>I guess this is what we wanted.
>
>>Given that, I wonder if you are used to standard terminology.
>>
>No, I am not, unfortunately. We are doing lots of statistical analyses,
>using R because it is fab and such, but reviewers are looking for SPSS
>output using terminology that we can't find in the R bundle---but our
>general impression is that R does things way more cleverer and better than
>click-until-you-seed-red-signifficant-effect tools found elsewhere.
>Reactions on r-help caused us to request for a better specification of the
>`effect size' that people wanted, and it turns out to be
>$$ SS(effect) / \Sum SS $$ (SS being sums-of-squares). To me, a simple
>physicist, that sounds as `the fraction of explained variance' by the
>factor. Looking at the formulas in help(summary.lm) is seems that
>summary.lm()$r.squared is exactly what we want (for a one-way aov).
>
>Is there a way to quickly tabulate the expression $$ SS(effect) /
>(\Sum_{effects} SS(effect) + SS(residuals)) $$ ?
>
>The numbers are practically there in the summary.aov() table. Only the
>grand total SS needs to be calculated.
>
>>For example, R does have an effects() function, and that might be what
>>you want.
>>
>>
>I don't really understand the effects()---it must be related to
>coefficients() but it obviously is different. There is model.tables.aov()
>which is also enlightening, but I think it is really the $ r^2 $ that we
>were looking for (our reviewers calling this an $ \eta^2 $---if that
>clarifies things).
>
>Thanks,
>
>---david
>
>>On Mon, 15 Mar 2004, David A. van Leeuwen wrote:
>>
>>
>>
>>>Having searched google '[R] aov effect size' without any
results I
>>>wonder if I not completely miss something.
>>>Is there any R function that calculates the effect size of an
AOV's main
>>>effect or interaction effect? It should be related to the F's
and the
>>>degree of freedom of the error, but the multitude in numbers in
aov()
>>>baffle me a bit.
>>>
>
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http://www.R-project.org/posting-guide.html
> From: David A. van Leeuwen > > Prof Brian Ripley wrote: > > >I think you want to call summary.lm on the aov object, but > this depends on > >what you mean by `effect size'. > > > I guess this is what we wanted. > > >Given that, I wonder if you are used to standard terminology. > > > > > No, I am not, unfortunately. We are doing lots of > statistical analyses, > using R because it is fab and such, but reviewers are looking > for SPSS > output using terminology that we can't find in the R bundle---but our > general impression is that R does things way more cleverer and better > than click-until-you-seed-red-signifficant-effect tools found > elsewhere.I believe there is `effect size' in DOE, e.g., for two-level, main effect only designs, effect sizes are something like 0.5*(mean(high) - mean(low)). This is obviously not what you meant.> Reactions on r-help caused us to request for a better > specification of > the `effect size' that people wanted, and it turns out to be > $$ SS(effect) / \Sum SS $$ (SS being sums-of-squares). To > me, a simple > physicist, that sounds as `the fraction of explained variance' by the > factor. Looking at the formulas in help(summary.lm) is seems that > summary.lm()$r.squared is exactly what we want (for a one-way aov). > > Is there a way to quickly tabulate the expression $$ SS(effect) / > (\Sum_{effects} SS(effect) + SS(residuals)) $$ ? > > The numbers are practically there in the summary.aov() table. > Only the > grand total SS needs to be calculated.I guess you want a partition of R^2 by factors in the model. The only situations where I can see this being sensible are: - One-way classification; i.e., only one factor. - Completely balanced design. For designs with more than one factor, the balanceness guarantees the orthogonality among the sums of squares, so it makes sense to partition R^2 that way. Short of that, there's no unique way to compute the SS (that's where the infamous 4 types of SS come from), and I don't see a sensible way to go about it. Think about the analogous situation in multiple linear regression: unless all predictor variables are orthogonal, there's no sensible way to compute proportion of variance explained by one variable, because that variable is correlated with other variables, and it's contribution to R^2 depends on what other variables are in the model. Andy> >For example, R does have an effects() function, and that > might be what you > >want. > > > > > > > I don't really understand the effects()---it must be related to > coefficients() but it obviously is different. There is > model.tables.aov() which is also enlightening, but I think it > is really > the $ r^2 $ that we were looking for (our reviewers calling this an $ > \eta^2 $---if that clarifies things). > > Thanks, > > ---david > > >On Mon, 15 Mar 2004, David A. van Leeuwen wrote: > > > > > > > >>Having searched google '[R] aov effect size' without any results I > >>wonder if I not completely miss something. > >> > >>Is there any R function that calculates the effect size of > an AOV's main > >>effect or interaction effect? It should be related to the > F's and the > >>degree of freedom of the error, but the multitude in > numbers in aov() > >>baffle me a bit. > >> > >> > > ______________________________________________ > R-help at stat.math.ethz.ch mailing list > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! > http://www.R-project.org/posting-guide.html > >------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}}
>From help(relimp, package="relimp"):Details: If 'set1' and 'set2' both have length 1, relative importance is measured by the ratio of the two standardized coefficients. Equivalently this is the ratio of the standard deviations of the two contributions to the linear predictor, and this provides the generalization to comparing two sets rather than just a pair of predictors. Doesn't look like what David want (he's not comparing factor to factor). Andy> From: Andrew Robinson [mailto:andrewr at uidaho.edu] > > Thinking about effect sizes, a plausible alternative may be > found in the > relimp package. > > From CRAN: relimp: Relative Contribution of Effects in a > Regression Model > > Functions to facilitate inference on the relative importance > of predictors in > a linear or generalized linear model > Version: 0.8-2 > Depends: R (>= 1.8.0), tcltk, MASS > Author: David Firth > Maintainer: David Firth <d.firth at warwick.ac.uk> > License: GPL (version 2 or later) > URL: http://www.warwick.ac.uk/go/relimp > http://www.warwick.ac.uk/go/dfirth > > Andrew > -- > Andrew Robinson Ph: 208 885 7115 > Department of Forest Resources Fa: 208 885 6226 > University of Idaho E : andrewr at uidaho.edu > PO Box 441133 W : > http://www.uidaho.edu/~andrewr > Moscow ID 83843 > Or: http://www.biometrics.uidaho.edu > No statement above necessarily represents my employer's opinion. > > >------------------------------------------------------------------------------ Notice: This e-mail message, together with any attachments,...{{dropped}}