Curve.fit.&.test.r

dat<-data.frame(x=Duration, y=as.numeric(idfkurve["2",]))
#polynomial fit
library(MASS)
LFts<-6
Fits<-vector("list",length=LFts)
for(i in 1:LFts){
  Fits[[i]]$fit <-lm(y~poly(x,i),data=dat);
  Fits[[i]]$col=i
  Fits[[i]]$AIC=round(AIC(Fits[[i]]$fit ),0)
  Fits[[i]]$coef=coef(Fits[[i]]$fit)
}
plot(dat$x,dat$y,pch=19,log="xy",cex=2,col="white")
plot(dat$x,dat$y,pch=19,cex=2,col="white")
L<-lapply(Fits,function(z)lines(dat$x,predict(z$fit),col=z$col,lty=z$col,lwd=z$col/2))
points(dat$x,dat$y,pch=19,cex=2,col=rgb(1,0,0,0.5))
aic<-unlist(lapply(Fits,FUN=function(z)z$AIC))
legend("topright",legend= paste(1:LFts,aic),text.col=1:LFts,title="n AIC",lty=1:LFts,lwd=(1:LFts/2),col=1:LFts)
par(new=T)
plot(diff(aic),type="o",axes=F,xlab="",ylab="")

Fitting and testing fit of data with polynomials.
Nice plot floows