From: John Smith <zmring_at_gmail.com>

Date: Tue, 17 May 2011 16:04:48 -0400

low <- x[10]; high <- x[n-9]

college.pts >= low & college.pts <= high }

windows()

plot(Predict(f, college, income=incomes, conf.int=FALSE),

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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 - 20:10:53 GMT

Date: Tue, 17 May 2011 16:04:48 -0400

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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