Re: [R] function logit() vs logistic regression

2012-10-18 Thread swertie
Thank you very much for replies and the nice explanation about variance stabilization. I heard about the arcsin transformation, but some recent papers were very critical about it (i.e., Warton & Hui, 2011), so that I would better try another way. I will have a look at beta regression. Best, V.

Re: [R] function logit() vs logistic regression

2012-10-17 Thread 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

Re: [R] function logit() vs logistic regression

2012-10-17 Thread Rolf Turner
On 18/10/12 07:58, 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 (r

Re: [R] function logit() vs logistic regression

2012-10-17 Thread David Winsemius
On Oct 17, 2012, at 11:58 AM, 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

[R] function logit() vs logistic regression

2012-10-17 Thread swertie
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 t