[R] confidence intervals for nls or nls2 model

[Francisco Mora Ardila]

Thanks! Now it is clear.

Francisco

On Wed, 16 May 2012 07:32:56 -0400, Gabor Grothendieck wrote
> On Tue, May 15, 2012 at 11:20 PM, Gabor Grothendieck
> <ggrothendieck at gmail.com> wrote:
> > On Tue, May 15, 2012 at 8:08 PM, Francisco Mora Ardila
> > <fmora at 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
> >
> 
> I have added a FAQ to the home page since this isn't the first time
> this question has come up:
> 
> http://nls2.googlecode.com#FAQs
> 
> -- 
> Statistics & Software Consulting
> GKX Group, GKX Associates Inc.
> tel: 1-877-GKX-GROUP
> email: ggrothendieck at gmail.com


----------------------
Francisco Mora Ardila
Estudiante de Doctorado
Centro de Investigaciones en Ecosistemas
Universidad Nacional Autónoma de México