On Thu, 14 Aug 2003, Spencer Graves wrote:
> This seems to identify a possible bug in R 1.7.1 under Windows 2000:
>
> > tstDf <- data.frame(y = 1:11, x=1:11)
> > fit <- nls(y~a/x, data=tstDf, start=list(a=1))
> > predict(fit, se.fit=TRUE)
> [1] 7.0601879 3.5300939 2.3533960 1.7650470 1.4120376 1.1766980 1.0085983
> [8] 0.8825235 0.7844653 0.7060188 0.6418353
>
?predict.nls states:
At present `se.fit' and `interval' are ignored.
and therefore works as advertised.
Torsten
> The same code in S-Plus 6.1 produces the following:
>
> > predict(fit, se.fit = TRUE)
> $fit:
> [1] 7.0601876 3.5300938 2.3533959 1.7650469 1.4120375 1.1766979
> 1.0085982 0.8825234
> [9] 0.7844653 0.7060188 0.6418352
>
> $se.fit:
> [1] 5.2433042 2.6216521 1.7477681 1.3108261 1.0486608 0.8738840
> 0.7490435 0.6554130
> [9] 0.5825894 0.5243304 0.4766640
>
> $residual.scale:
> [1] 6.544753
>
> $df:
> [1] 10
>
> Unfortunately, I'm not in a position to fix the problem, but this toy
> example might make it easier for someone else to fix it.
>
> spencer graves
> p.s. The following command in S-Plus 6.1 seems to work fine but
> produces an error in R 1.7.1:
>
> nls(y~a, data=tstDf, start=list(a=1))
> Error in nlsModel(formula, mf, start) : singular gradient matrix at
> initial parameter estimates
> #############################################################
> Enrique Portilla wrote:
> > Hi,
> > Does anyone know how to compute the confidence prediction intervals for
> > a nonlinear least squares models (nls)?
> >
> > I was trying to use the function 'predict' as I usually do for other
> > models fitting (glm, lm, gams...), but it seems that se.fit, and
> > interval computation is not implemented for the nls...
> >
> > Cheers
> >
> > Enrique
> >
> > ~~~~~~~~~~~~~~~~~~~~~~~~~~~
> > Fisheries Research Services,
> > Marine Laboratory,
> > Victoria Road,
> > Torry,
> > Aberdeen, UK.
> > Tel. 44 (0) 1224 295314
> > ~~~~~~~~~~~~~~~~~~~~~~~~~~~
> >
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