Yes! Thank you.
*
*
*Ben Caldwell*




On Thu, May 31, 2012 at 4:03 PM, peter dalgaard <pdalgd@gmail.com> wrote:

>
> 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.mes@cbs.dk  Priv: PDalgd@gmail.com
>
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