Re: [R] can not use plot.Predict {rms} reproduce figure 7.8 from Regression Modeling Strategies (http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf)

[David Winsemius]

On Mar 14, 2012, at 4:09 PM, John Smith wrote:
With most current version of R and RMS, the 4 curves are drew in
4 separate panels. Can anyone show me how can I get the figure  
exactly like
the figure 7.8 in  *Regression Modeling Strategies* (
http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf)

In case anyone else is scratching their head wondering what connection  
there might be between figure 7.8 in that document (which happens to  
be a nomogram), they should realize that the questioner is most  
probably asking about 7.8 in the RMS *book* and that there is no such  
Figure in chapter 7 of the pdf document . (Nor, for that matter, does  
a search for "democrat" bring up any hits in the document so I don't  
think the code comes from there either.)

I suspect the data is here:

http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/counties.sav


--

David.




On Tue, May 17, 2011 at 4:04 PM, John Smith <zmr...@gmail.com> wrote:

Dear R-users,

I am using R 2.13.0 and rms 3.3-0 , but can not reproduce figure  
7.8 of
the handouts *Regression Modeling Strategies* (
http://biostat.mc.vanderbilt.edu/wiki/pub/Main/RmS/course2.pdf) by  
the
following code. Could any one help me figure out how to solve this?


setwd('C:/Rharrell')
require(rms)
load('data/counties.sav')

older <- counties$age6574 + counties$age75
label(older) <- '% age >= 65, 1990'
pdensity <- logb(counties$pop.density+1, 10)
label(pdensity) <- 'log 10 of 1992 pop per 1990 miles^2'
counties <- cbind(counties, older, pdensity)  # add 2 vars. not in  
data
frame
dd <- datadist(counties)
options(datadist='dd')

f <- ols(democrat ~ rcs(pdensity,4) + rcs(pop.change,3) +
        rcs(older,3) + crime + rcs(college,5) + rcs(income,4) +
        rcs(college,5) %ia% rcs(income,4) +
        rcs(farm,3) + rcs(white,5) + rcs(turnout,3), data=counties)

incomes <- seq(22900, 32800, length=4)
show.pts <- function(college.pts, income.pt)
 {
   s <- abs(income - income.pt) < 1650
                                       # Compute 10th smallest and  
10th
largest % college
                                       # educated in counties with  
median
family income within
                                       # $1650 of the target income
   x <- college[s]
   x <- sort(x[!is.na(x)])
   n <- length(x)
   low <- x[10]; high <- x[n-9]
   college.pts >= low & college.pts <= high
 }
windows()
plot(Predict(f, college, income=incomes, conf.int=FALSE),
    xlim=c(0,35), ylim=c(30,55), lty=1, lwd=c(.25,1.5,3.5,6),
col=c(1,1,2,2), perim=show.pts)





David Winsemius, MD
West Hartford, CT

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