Mike, Try plot(pref, ..., scat1d.opts=list(frac=0.025, lwd=0.3, nhistSpike=i))
where i = 1 to always use spike histograms (default is to use them if n >= 2000) or i=1e7 to never use them and to always jitter instead. There are many other scat1d options you can pass through scat1d.opts. Frank Mike Babyak wrote > > Dear colleagues, > > I have a question regarding controlling the jitter when plotting > predictions in the rms package. Below I've simulated some data that > reflect what I'm working with. The model predicts a continuous variable > with an ordinal score, a two-level group, and a continuous covariate. Of > primary interest is a plot of the group by score interaction, where the > score is the ordinal variable, and the group Ns are quite disparate. > > When I produce the plot for the predicted values with the data=llist > argument, as expected I get datadensity hatch marks. However, in the > group > with the smaller N, I get jittered datadensity points, while in the group > with the larger N, the jitter apparently defaults to single vertical > lines, > which I assume is because the jitter would look like a black blob. Some > of > my co-authors are a bit worried about how that looks, so here I am. > > Apart from abandoning data=llist altogether, is there a way to modify the > jitter in the smaller group so it behaves like the larger one? > > Of secondary importance, anything you can tell me about improving my > clumsy > little simulation code would be welcome--I have little to no idea what I'm > doing there. for example, would there be a way to produce the group > variable with the disparate Ns more directly? > > Thanks, > > Mike Babyak > Behavioral Medicine Research Center > Duke University Medical Center > > > > #question about jitter/llist in rms > #R v 2.14.1 under windows 7 > #################################################################### > > #question about jitter/llist in rms > require(rms) > #simulate some data > n = 5000 > age = runif(n) > score = runif(n) + 0.5*age > group<- as.numeric(sample(c(FALSE,TRUE), 5000, replace=T, prob=c(.1, .9))) > ordscore = as.numeric(factor(rep(1:5, length.out=n))) > table(group,ordscore) > e = rnorm(n, 0, 0.1) > > #true model > y = group + 0.6*ordscore + group*ordscore + .2*age + e > > #convert group to factor > group.f<-as.factor(group) > > #save data characterics > dd1<-datadist(age,ordscore,group.f) > options(datadist="dd1") > > #estimate model > reg1<-ols(y~group.f+ordscore+group.f*ordscore+age,x=T,y=T) > > #plot results > preg<-Predict(reg1,ordscore,group.f) > > #produces interaction plot with datadensity hatch marks > plot(preg,data=llist(ordscore,group.f)) > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@ 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. > ----- Frank Harrell Department of Biostatistics, Vanderbilt University -- View this message in context: http://r.789695.n4.nabble.com/question-on-jitter-in-plot-Predict-in-rms-tp4598555p4600795.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.