On Tue, May 15, 2012 at 11:20 PM, Gabor Grothendieck <ggrothendi...@gmail.com> wrote: > On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila > <fm...@oikos.unam.mx> wrote: >> 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? >> > > as.lm is a linear model between the response variable and the gradient > of the nonlinear model and as we see below x is not part of that > linear model so x can't be in newdata when predicting from the tangent > model. We can only make predictions at the original x points. For > other x's we could use Interpolation. See ?approx (?spline can also > work in smooth cases but in the example provided the function has a > kink and that won't work well with splines.) > >> as.lm(modellogis)$model > y a b c (offset) > 1 1.281055090 0.06601796 -4.411829e-01 1.168928e+00 1.397153 > 2 1.563609934 0.04798815 -3.268846e-01 9.766080e-01 1.015584 > 3 0.001570796 0.04798815 -3.268846e-01 9.766080e-01 1.015584 > 4 2.291579783 0.16311227 -9.767241e-01 1.597189e+00 3.451981 > 5 0.841891853 0.12203013 -7.665928e-01 1.512752e+00 2.582551 > 6 6.553951324 0.21464369 -1.206154e+00 1.564573e+00 4.542552 > 7 14.243274230 0.74450055 -1.361047e+00 -1.455630e+00 15.756031 > 8 14.519899320 0.59707858 -1.721353e+00 -6.770205e-01 12.636107 > 9 15.066473610 0.74450055 -1.361047e+00 -1.455630e+00 15.756031 > 10 21.728809880 1.00000000 -2.943955e-13 -9.073765e-12 21.163223 > 11 18.553054450 1.00000000 -2.943955e-13 -9.073765e-12 21.163223 > 12 23.722637370 1.00000000 -2.943955e-13 -9.073765e-12 21.163223 >
Also regarding your comment about not getting smooth curves be sure that you order the x's (and permute the y's the same way) before plotting. -- Statistics & Software Consulting GKX Group, GKX Associates Inc. tel: 1-877-GKX-GROUP email: ggrothendieck at gmail.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.
