Hi all, I'm struggling with nls. How do you know if your model is significant? For a lm, you get a p-value, but you don't get it for a nls. Is there a way to calculate it?
For a lm I use this: a<-summary(lm(model ~obs)) f.stat<-a$fstatistic p.value<-1-pf(f.stat["value"],f.stat["numdf"],f.stat["dendf"]) Is there something similar for a nls? The kind of output that I get is: Formula: y ~ exp.f(x, a, b) Parameters: Estimate Std. Error t value Pr(>|t|) a 1.381e+02 1.192e+01 11.583 3.19e-08 *** b 1.790e-02 2.459e-03 7.279 6.19e-06 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 13.21 on 13 degrees of freedom Number of iterations to convergence: 6 Achieved convergence tolerance: 9.123e-06 I know that my parameters are significant but I need to say something about the whole model. Many thanks, Nerak -- View this message in context: http://r.789695.n4.nabble.com/nls-how-do-you-know-if-the-model-is-significant-tp4632401.html Sent from the R help mailing list archive at Nabble.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.