Dear all, I have several response variables estimated from some simulations, and I would like to identify the thresholds for trend changes. Fro example, below I forced two different response behaviours and on x is time unit.
x<-1:1500 y<-x/exp(x^0.2) smaller15<-y[y<15] y<-ifelse(y<15,y+rnorm(length(smaller15)), 15+rnorm(1000-length(smaller15), 0, 0.9)) myDF1<-data.frame(cbind(x,y)) plot(y~x, data=myDF1) k1<-1:10 l1<- -65*k1*k1+750*k1-500 k2<-12:25 l2<-2.6299*k2*k2- 104.39*k2 + 1000 myDF2<-data.frame(cbind(k=c(k1,k2),l=c(l1,l2))) plot(l~k, data=myDF2) As one can see, the first simulation we have a non-linear ascendent y-response, and after ~500 time steps the simulation change the behaviour to almost stable results. By other side, on second example I get a fast increasing, subsequent decreasing and ~11 time steps I get a different trend. For the first case I think that segmented package could do the job, and for second case I think that it will not work properly. But as my simulations is for time-series, I was thinking if we can have a ts-like way of identify trend changes on the outcome results. Cheers miltinho University of Sao Paulo, br [[alternative HTML version deleted]] ______________________________________________ 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.