Re: [R] OT: (quasi-?) separation in a logistic GLM

I know that this is a quite old post but I am dealing with a similar warning message and, also after reading all the posts here, I am still in doubt with what I should do with my analysis. I have a dataset where the binary response variable is sex, and the predictors are several variables (they

Re: [R] OT: (quasi-?) separation in a logistic GLM

[analogs]=x), sp+se - 1)) [1] 0.9443561 0.9269231 0.8712792 So it appears that the dataset is quite well separated into two samples at the cutpoint 0.209 Re: [R] OT: (quasi-?) separation in a logistic GLM Grant Izmirlian NCI On 15 Dec 2008, at 18:03, Gavin Simpson wrote: Dear List

Re: [R] OT: (quasi-?) separation in a logistic GLM

sorry for reposting. Some code was missing in my previous email... -- Dear Gavin glm reported exactly what it noticed, giving a warning that some very small fitted probabilities have been found. However, your data are **not** quasi-separated. The

Re: [R] OT: (quasi-?) separation in a logistic GLM

On Tue, 2008-12-16 at 13:31 +0100, vito muggeo wrote: dear Gavin, I do not know whether such comment may be still useful.. Very much so, Thank you. Why are you unsure about quasi-separation? I think that it is quite evident in the plot Unsure in the sense that I had been unable to

Re: [R] OT: (quasi-?) separation in a logistic GLM

dear Gavin, I do not know whether such comment may be still useful.. Why are you unsure about quasi-separation? I think that it is quite evident in the plot plot(analogs ~ Dij, data = dat) Also it may be useful to see the plot of the monotone (profile) deviance (or the log-lik) for the coef

Re: [R] OT: (quasi-?) separation in a logistic GLM

Dear Gavin, glm reported exactly what it noticed, giving a warning that some very small fitted probabilities have been found. However, your data are **not** quasi-separated. The maximum likelihood estimates are really those reported by glm. A first elementary way is to change the tolerance

[R] OT: (quasi-?) separation in a logistic GLM

Dear List, Apologies for this off-topic post but it is R-related in the sense that I am trying to understand what R is telling me with the data to hand. ROC curves have recently been used to determine a dissimilarity threshold for identifying whether two samples are from the same type or not.

Re: [R] OT: (quasi-?) separation in a logistic GLM

If you look at the distribution of those with analogs==TRUE versus the whole groups it is not surprising that the upper range of Dij's result in a very low probability estimate: plot(density(dat$Dij)) lines(density(dat[dat$analogs == TRUE, 2])) Appears as though more than 25% of the