[R] function logit() vs logistic regression

[Achim Zeileis]

On Wed, 17 Oct 2012, swertie wrote:

> Hello!
> When I am analyzing proportion data, I usually apply logistic regression
> using a glm model with binomial family. For example:
> m <- glm( cbind("not realized", "realized") ~ v1 + v2 , family="binomial")
>
> However, sometimes I don't have the number of cases (realized, not 
> realized), but only the proportion and thus cannot compute the binomial 
> model. I just found out that the package car contains a function "logit" 
> which allows for logit transformation. Would it be possible to transform 
> the proportion data with this function and analyze the transformed data 
> with a glm with family="gaussian"?

In situations like this, beta regression can be useful. It models the mean 
and optionally also the precision (related to the variance) of a 
beta-distributed response on the open (0, 1) interval. See
http://www.jstatsoft.org/v34/i02/ for an introduction to the betareg 
package in R and http://www.jstatsoft.org/v48/i11/ for various extended 
features.

Best,
Z

> Thank you very much
>
>
>
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