For binary w.r.t. continuous, how about a smoothing spline? As in, x<-rnorm(100) y<-rbinom(100,1,exp(.3*x-.07*x^2)/(1+exp(.3*x-.07*x^2))) plot(x,y) lines(smooth.spline(x,y))
OR how about a more parametric approach, logistic regression? As in, glm1<-glm(y~x+I(x^2),family=binomial) plot(x,y) lines(sort(x),predict(glm1,newdata=data.frame(x=sort(x)),type="response")) FOR binary w.r.t. categorical it depends. Are the categories ordinal (is there a natural ordering?) or are the categories nominal (no ordering)? For nominal categories, the data is essentially a contingency table, and "strength of the predictor" is a test of independence. You can still do a graphical exploration: maybe plotting the proportion of Y=1 for each category of X. As in, z<-cut(x,breaks=-3:3) plot(tapply(y,z,mean)) If your goal is to find strong predictors of Y, you may want to consider graphical measures that look at the predictors jointly. Maybe with a generalized additive model (gam)? There is probably a lot more you can do. Be creative. -tgs On Tue, May 4, 2010 at 9:04 PM, Kim Jung Hwa <kimhwamaill...@gmail.com>wrote: > Hi All, > > I'm dealing with binary response data for the first time, and I'm confused > about what kind of graphics I could explore in order to pick relevant > predictors and their relation with response variable. > > I have 8-10 continuous predictors and 4-5 categorical predictors. Can > anyone > suggest what kind of graphics I can explore to see how predictors behave > w.r.t. response variable... > > Any help would be greatly appreciated, thanks, > Kim > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org 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. > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org 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.
