I'm not sure that I follow entirely what you've done, but perhaps the following suggestions will help: (1) If the plotted curve isn't smooth because it's evaluated at too few x-values or at x-values that are too unevenly spaced, what about getting a sufficient number of predicted values [via predict()] that are evenly spaced along the range of ht -- i.e., not at the observations? (2) Rather than connecting the fitted values with line segments, you could use spline() to interpolate.
I hope that this helps, John
At 11:23 AM 1/15/2004 -0800, Jeff D. Hamann wrote:
I've been trying to plot the predicted values, as a line, over the data for a simple nonlinear fit with the following commands:
plot( hg ~ ht ) ... define some function hg ~ ht + junk ... ... blah, blah, obtain parameter estimates and predicted values, blah... ... then... lines( sort( $predicted ) ~ sort( ht ) )
which results in a line that isn't smooth (which I knew would happen). I've checked the FAQ,docs and archives and I'm not sure if there's function that will so what Heut et. al (2004) do with their plfit(). So, is there already an R function, or process to do this, or will I have to write one?
Thanks, Jeff.
----------------------------------------------------- John Fox Department of Sociology McMaster University Hamilton, Ontario, Canada L8S 4M4 email: [EMAIL PROTECTED] phone: 905-525-9140x23604 web: www.socsci.mcmaster.ca/jfox
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