[R] nls confidence intervals

[Spencer Graves]

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

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
> 
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