On Jun 1, 2012, at 00:14 , Benjamin Caldwell wrote: > temppow<-lm(log(y)~log(x)) > plot(log(y)~log(x)) > plot(residuals(temppow), main="pow") > abline(temppow) > plot(y~x, main="pow") > tempsum<-summary(temppow)$adj.r.squared > tempint<-summary(temppow)$coefficients[1,1] #intercept of power function > tempslope<-summary(temppow)$coefficients[2,1] #slope of power function > tempmin<-min(x) > tempmax<-max(x) > lngth<-c(tempmin:tempmax) # vector from the minimum to the maximum values > of independent variable > prediction<-exp(tempint)*((lngth)^tempslope)*exp((summary(temppow)$sigma^2)/2) > # exp((summary(temppow)$sigma^2)/2) is the bias correction > #prediction<-exp(tempint+tempslope*log(lngth))*exp((summary(temppow)$sigma^2)/2) > lines(prediction)
It does help considerably to use lines(length, prediction)! (And that bias correction looks really dubious to me, but let's not go there...) -- Peter Dalgaard, Professor, Center for Statistics, Copenhagen Business School Solbjerg Plads 3, 2000 Frederiksberg, Denmark Phone: (+45)38153501 Email: pd....@cbs.dk Priv: pda...@gmail.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.
