Hi all I have fitted a model usinf nls function to these data:
> x [1] 1 0 0 4 3 5 12 10 12 100 100 100 > y [1] 1.281055090 1.563609934 0.001570796 2.291579783 0.841891853 [6] 6.553951324 14.243274230 14.519899320 15.066473610 21.728809880 [11] 18.553054450 23.722637370 The model fitted is: modellogis<-nls(y~SSlogis(x,a,b,c)) It runs OK. Then I calculate confidence intervals for the actual data using: dataci<-predict(as.lm(modellogis), interval = "confidence") BUt I don´t get smooth curves when plotting it, so I want to get other "confidence vectors" based on a new x vector by defining a new data to do predictions: x0 <- seq(0,15,1) dataci<-predict(as.lm(modellogis), newdata=data.frame(x=x0), interval = "confidence") BUt it does not work: I get the same initial confidence interval Any ideas on how to get tconfidence and prediction intervals using new X data on a previous model? Thanks Francisco ---------------------- Francisco Mora Ardila Estudiante de Doctorado Centro de Investigaciones en Ecosistemas Universidad Nacional Autónoma de México ______________________________________________ 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.