From: John Smith <zmring_at_gmail.com>

Date: Tue, 17 May 2011 16:25:04 -0400

R-help_at_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. Received on Tue 17 May 2011 - 20:29:13 GMT

Date: Tue, 17 May 2011 16:25:04 -0400

Also I can not reproduce figure 7.11 by

f <- Newlabels(f, list(turnout='voter turnout (%)'))
windows()

nomogram(f, interact=list(income=incomes), turnout=seq(30,100,by=10),

lplabel='estimated % voting Democratic', cex.var=.8, cex.axis=.75)

On Tue, May 17, 2011 at 4:04 PM, John Smith <zmring_at_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)
**>
**>
**>
**>
**> Thanks
**>
**>
**>
*

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