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List:       r-help
Subject:    [R] question on jitter in plot.Predict in rms
From:       Mike Babyak <mababyak () gmail ! com>
Date:       2012-04-30 15:45:50
Message-ID: CAFKsTL+5+xv==qbSbwyp4xoQZNP7uVuMB3T3wzaAYKDxfo15tQ () mail ! gmail ! com
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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))

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