Provided, of course, that I alter the lines for different data sets and data frames, the code to plot a line derived from nls() onto a plot works with no problems.
Here's an example: Year NOP 2002 6 2003 8 2004 11 2005 19 2006 26 2007 25 mod1 <- nls(NOP~alpha*exp(beta*Year), data=aic, start=list(alpha=1e-278, beta=0.3205), trace=T, nls.control(maxiter=30000, minFactor=0.000005)) plot(NOP~Year, data=aic, pch=19, ylab="Number of papers") mod1a=seq(2002, 2007, by=.0001) lines(mod1a, predict(mod1, list(Year = mod1a))) I've been using this code for several years not to get models from nls() onto plot and I've never had an issue with it until the dataset I referenced in my initial email. Thanks for your assistance. SR Steven H. Ranney http://www.steven-ranney.com http://stevenranney.blogspot.com On Mon, Jul 18, 2011 at 2:55 AM, Peter Ehlers <ehl...@ucalgary.ca> wrote: > On 2011-07-17 17:37, Steven Ranney wrote: >> >> All - >> >> I'm having an issue with trying to plot a model derived from nls() >> onto a simple plot. I have included a sample data set and the code >> that I've been using. >> >> year month day date location mileage cost gallon cpg >> mpg x >> 2009 1 4 1/4/2009 BZN 124585 19.39 14.37 1.349339 >> 10.71677 2009-01-04 >> 2009 1 15 1/15/2009 BZN 124888 23.2 16.12 1.439206 >> 18.79653 2009-01-15 >> 2009 1 27 1/27/2009 BZN 125133 21.51 14.35 1.498955 >> 17.07317 2009-01-27 >> 2009 2 16 2/16/2009 BZN 125429 27.96 15.54 1.799228 >> 19.04762 2009-02-16 >> 2009 2 27 2/27/2009 BZN 125702 26.82 14.27 1.879467 >> 19.13104 2009-02-27 >> 2009 3 19 3/19/2009 BZN 125941 24.38 12.91 1.888459 >> 18.51278 2009-03-19 >> 2009 4 9 4/9/2009 BZN 126260 32.59 16.30 1.999387 >> 19.57055 2009-04-09 >> 2009 4 28 4/28/2009 BZN 126587 34.58 16.79 2.059559 >> 19.47588 2009-04-28 >> 2009 5 17 5/17/2009 BZN 126925 35.78 16.57 2.159324 >> 20.39831 2009-05-17 >> 2009 5 27 5/27/2009 BZN 127240 35.57 15.01 2.369753 >> 20.98601 2009-05-27 >> 2009 6 7 6/7/2009 BZN 127590 40.99 16.60 2.469277 >> 21.08434 2009-06-07 >> 2009 6 21 6/21/2009 BZN 127910 41.52 15.64 2.654731 >> 20.46036 2009-06-21 >> 2009 7 21 7/21/2009 BZN 128264 43.37 16.67 2.601680 >> 21.23575 2009-07-21 >> 2009 8 11 8/11/2009 BZN 128618 42.68 16.42 2.599269 >> 21.55907 2009-08-11 >> 2009 8 27 8/27/2009 BZN 128947 43.12 16.60 2.597590 >> 19.81928 2009-08-27 >> 2009 9 21 9/21/2009 BZN 129255 40.44 15.56 2.598972 >> 19.79434 2009-09-21 >> 2009 10 1 10/1/2009 BZN 129541 38.55 14.83 2.599461 >> 19.28523 2009-10-01 >> 2009 10 11 10/11/2009 BZN 129806 36.65 14.10 2.599291 >> 18.79433 2009-10-11 >> 2009 10 22 10/22/2009 BZN 130027 30.18 11.61 2.599483 >> 19.03531 2009-10-22 >> 2009 11 9 11/9/2009 BZN 130358 43.19 16.62 2.598676 >> 19.91576 2009-11-09 >> 2009 11 22 11/22/2009 BZN 130631 39.23 15.09 2.599735 >> 18.09145 2009-11-22 >> 2009 12 5 12/5/2009 BZN 130950 44.43 17.09 2.599766 >> 18.66589 2009-12-05 >> 2009 12 30 12/30/2009 BZN 131239 42.14 16.70 2.523353 >> 17.30539 2009-12-30 >> >> After converting my dates into R-usable dates: >> >> #convert my dates to R-usable dates >> x<- strptime(date, format="%m/%d/%Y") >> x >> mileage<- cbind(mileage, x) >> >> I plot the data and model mpg as a function of date. In the nls() >> statement, I convert x back to a numeric value so that I can conduct >> the regression: >> >> plot(mpg~x, data=mileage[year==2009,], ylab="Miles per gallon", >> xlab="2009", yaxs="i", ylim=c(10,30)) >> nls.2009<- >> nls(mpg~(alpha*(as.numeric(x)^2))+(bravo*as.numeric(x))+(charlie), >> data=mileage[year==2009,], start=list(alpha=-2e-14, bravo=5e-5, >> charlie=-31407), >> trace=T, na.action=na.omit, >> nls.control(minFactor=0.000000000000000000001)) >> plot(mpg~x, data=mileage[year==2009,]) >> modb=seq(min(as.numeric(x)), max(as.numeric(x)), by=10000) >> lines(modb, predict(nls.2009, lines(as.numeric(x)=modb))) >> >> Unfortunately, when I run the final line of this code, I get the >> following: >> >> Error: unexpected '=' in " lines(modb, predict(nls.2009, >> lines(as.numeric(x)=" >> >> In other similar analyses, I've been able to plot an nls() model using >> this exact code--altered of course according to information--but here >> I'm at a loss. I'm certain it has something to do with the >> lines(...as.numeric(x)) value I'm trying to plot, but I can't figure >> out what I'm doing wrong. > > That last line of code doesn't look right to me. The arguments > that you need to supply to predict() are 'object' and 'newdata', > where 'newdata' must have the appropriate form. Unless you have > your own function lines(), I don't think that lines(as.numeric(x)=modb) > would qualify as newdata. > > It's usually a bad idea to shove too much stuff into a single command > and a good idea to use str() often. > > This 'exact' code worked in the past? > > Peter Ehlers > >> >> The model is fine, but it's the plotting of the model that escapes me. >> >> I'm running R version 2.12.1 on a Windows 7 machine. >> >> Thanks for your help - >> >> Steven H. Ranney >> >> http://stevenranney.blogspost.com >> http://www.steven-ranney.com >> >> ______________________________________________ >> R-help@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. > > ______________________________________________ R-help@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.
