Re: [R] What is the most useful way to detect nonlinearity in logistic regression?
Peter Dalgaard [EMAIL PROTECTED] writes: Re. the smoothed residuals, you do need to be careful about the smoother. Some of the robust ones will do precisely the wrong thing in this context: You really are interested in the mean, not some trimmed mean (which can easily amount to throwing away all your cases...). Here's an idea: x - runif(500) y - rbinom(500,size=1,p=plogis(x)) xx - predict(loess(resid(glm(y~x,binomial))~x),se=T) matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20) Not sure my money isn't still on the splines, though. Doh. You might also want to make sure that the residuals are of a type that can be _expected_ to have mean zero. Apparently, the default deviance residuals do not have that property, whereas response residuals do. I did check that loess (as opposed to lowess!) does a plain least-squares based fitting by default, but I didn't think to check what kind of residuals I was looking at. Serves me right for posting way beyond my bedtime... Anyways, you're probably better off believing Frank and not me in these matters. -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [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
[R] What is the most useful way to detect nonlinearity in logistic regression?
It is easy to spot response nonlinearity in normal linear models using plot(something.lm). However plot(something.glm) produces artifactual peculiarities since the diagnostic residuals are constrained by the fact that y can only take values 0 or 1. What do R users find most useful in checking the linearity assumption of logistic regression (i.e. log-odds =a+bx)? Patrick Foley [EMAIL PROTECTED] __ [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
Re: [R] What is the most useful way to detect nonlinearity in logistic regression?
Patrick Foley [EMAIL PROTECTED] writes: It is easy to spot response nonlinearity in normal linear models using plot(something.lm). However plot(something.glm) produces artifactual peculiarities since the diagnostic residuals are constrained by the fact that y can only take values 0 or 1. What do R users find most useful in checking the linearity assumption of logistic regression (i.e. log-odds =a+bx)? Well, there's basically - grouping - higher-order terms - smoothed residuals A simple technique is to include a variable _both_ as a continuous term and cut up into a factor (as in ~ age + cut(age,seq(30,70,10))). The model that you are fitting is a bit weird but it gives you a clean test for omitting the grouped term. A somewhat nicer variant of the same theme is to do a linear spline (or a higher order one for that matter) with selected knots. Re. the smoothed residuals, you do need to be careful about the smoother. Some of the robust ones will do precisely the wrong thing in this context: You really are interested in the mean, not some trimmed mean (which can easily amount to throwing away all your cases...). Here's an idea: x - runif(500) y - rbinom(500,size=1,p=plogis(x)) xx - predict(loess(resid(glm(y~x,binomial))~x),se=T) matplot(x,cbind(xx$fit, 2*xx$se.fit, -2*xx$se.fit),pch=20) Not sure my money isn't still on the splines, though. -- O__ Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 __ [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
