From: Levi Waldron <leviwaldron_at_gmail.com>

Date: Mon, 24 Mar 2008 16:14:12 -0400

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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 Mon 24 Mar 2008 - 20:19:23 GMT

Date: Mon, 24 Mar 2008 16:14:12 -0400

*Thank you* Greg, this function works perfectly! I had imagined that the ideal solution would iteratively modify the vector to fix new violations of mindiff created by each subsequent spreading of tight clusters, but couldn't figure how to do it. A small note, the vector x must be sorted before using this function, which is a reasonable requirement.

Thanks also to Jim Lemon for pointing out the very useful plotrix package - the spread.labels function didn't work so well for this application because the timelines looked strange with the labels spread evenly, but I like a number of the functions provided by the package.

-levi

On Mon, Mar 24, 2008 at 12:43 PM, Greg Snow <Greg.Snow_at_imail.org> wrote:

> Levi,

*>
**> Here is one possible function:
**>
**> spread <- function(x, mindiff) {
**> df <- x[-1] - x[-length(x)]
**> i <- 1
**> while (any(df < mindiff)) {
**> x[c(df < mindiff, FALSE)] <- x[c(df < mindiff, FALSE)] - mindiff/10
**> x[c(FALSE, df < mindiff)] <- x[c(FALSE, df < mindiff)] + mindiff/10
**> df <- x[-1] - x[-length(x)]
**> i <- i + 1
**> if (i > 100) {
**> break
**> }
**> }
**> x
**> }
**>
**> I have tried experimenting with using optim to minimize a function of
**> the distances between new points and old points penealized for being to
**> close, but it sometimes gave me some weird results, the above has worked
**> fairly well for me (the above is based on some of the code inside the
**> triplot function in the TeachingDemos package).
**>
**> Hope this helps,
**>
**> --
**> Gregory (Greg) L. Snow Ph.D.
**> Statistical Data Center
**> Intermountain Healthcare
**> greg.snow_at_imail.org
**> (801) 408-8111
*

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