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 <kimhwamaillist@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
>
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