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 -- View this message in context: http://r.789695.n4.nabble.com/function-logit-vs-logistic-regression-tp4646498.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
