Sorry. I am still using the 9-11 March 2011 version of course2.pdf.



On Wed, Mar 14, 2012 at 5:52 PM, David Winsemius <dwinsemius@comcast.net>wrote:

>
> 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<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<http://biostat.mc.vanderbilt.edu/wiki/pub/Main/DataSets/counties.sav>
>
>
> --
>
> David.
>
>>
>>
>>
>>
>> On Tue, May 17, 2011 at 4:04 PM, John Smith <zmring@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<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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