[R] ANOVA for proportions with large mass on an extreme of [0, 1]
gunter.berton at gene.com
Sat Jun 21 19:27:27 CEST 2014
Although your queries certainly intersect R, they are primarily about
statistical modeling, which is OT for this list. Your issues also
appear to be complex. I would therefore suggest that you eschew remote
Internet advice and consult a local statistical expert for help.
Genentech Nonclinical Biostatistics
"Data is not information. Information is not knowledge. And knowledge
is certainly not wisdom."
On Fri, Jun 20, 2014 at 8:54 PM, Joaquin Rapela <rapela at ucsd.edu> wrote:
> I am trying to perform an ANOVA on a dependent variable that has large mass
> on the 1 side of the (0, 1] interval. I decided to use Fractional Regression
> Models, as implemented in the package frm. This package seems well-suited for
> my problem, but I don't see how to perform model comparisons of nested frm
> models. Please, see data and code below.
> I would like to do:
> anova(model1, model2)
> There is a function frm.ptest(model1, model2), but does not work with nested
> Are there alternatives to the frm package to perform ANOVAs on proportions
> (with large mass on an extreme of [0, 1])?
> Is there a way to model repeated measures (as in package lme4) when the dependent variable is a proportion?
> Data and code
> con <- url("http://sccn.ucsd.edu/~rapela/avshift/anovaDataFrame.RData")
> myData <- get(load(con))
> myData <- myData[!is.na(myData$alternationRate),]
> y <- myData$alternationRate
> model1 <- frm(y=y, x=model.matrix(~modality*condition+clusterID, data=myData)[, -1], linkfrac="logit", linkbin="logit", type="2P", inflation=1)
> model2 <- frm(y=y, x=model.matrix(~modality+condition+clusterID, data=myData)[, -1], linkfrac="logit", linkbin="logit", type="2P", inflation=1)
> # this works
> frm.ptest(model2, model3)
> # but this does not
> # frm.ptest(model1, model2)
> # Error in frm.ptest(model1, model2) :
> # object 2 is nested in object 1 - no need to use the P test
> Thanks, Joaquin
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> and provide commented, minimal, self-contained, reproducible code.
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