Given nls has a lot of C code (and is pretty complicated), I doubt
you'll find much joy doing that.
nlxb from my nlmrt package is all in R, but you'll need to do quite a
bit of work at each stage. I don't form the J' J matrix, and do a
Marquardt approximation by adding appropriate rows to the J matrix then
do a qr decomposition on that.
In any event, the Hessian (which J' J is only a rather poor
appriximation to) is what you want, and it may not be positive definite
at the iterates, so you have infinite standard errors. Well, if the
curvature was 0, they'd be infinite. Since the curvature is negative,
maybe the SEs are more than infinite, if that has any meaning.
I have one problem for which I generated 1000 starting points and >75%
had the Hessian indefinite. That is a simple logistic nonlinear
regression, albeit with nasty data.
JN
Message: 90 Date: Fri, 5 Apr 2013 05:06:57 +0000 From: Rebecca Lester <rebecca.les...@deakin.edu.au>
To: "r-help@r-project.org" <r-help@r-project.org> Subject: [R] Assessing the fit of a
nonlinear model to a new dataset Message-ID:
<5a72faa65583bc45a816a698a960e92788612...@mbox-f-3.du.deakin.edu.au> Content-Type: text/plain Hi all,
I am attempting to apply a nonlinear model developed using nls to a new dataset and assess the fit of that
model. At the moment, I am using the fitted model from my fit dataset as the starting point for an nls fit
for my test dataset (see below). I would like to be able to view the t-statistic and p-values for each of
the iterations using the trace function, but have not yet worked out how to do this. Any other suggestions
are also welcome. Many thanks, Rebecca
model.wa <- nls(y ~ A*(x^B), start=list(A=107614,B=-0.415)) # create nls()
power model for WA data
summary(model.wa) # model summary
Formula: y ~ A * (x^B)
Parameters:
Estimate Std. Error t value Pr(>|t|)
A 7.644e+04 1.240e+04 6.165 4.08e-06 ***
B -3.111e-01 4.618e-02 -6.736 1.15e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 5605 on 21 degrees of freedom
Number of iterations to convergence: 6
Achieved convergence tolerance: 7.184e-06
(6 observations deleted due to missingness)
model.vic <- nls(y.vic ~ A*(x.vic^B), start = list(A = 7.644e+04, B =
-3.111e-01), trace = T)
3430193778 : 76440.0000 -0.3111
2634092902 : 48251.9235397 -0.2552481
2614516166 : 27912.1921354 -0.1772322
2521588892 : 32718.3764594 -0.1862611
2521233646 : 32476.4536126 -0.1836836
2521230904 : 32553.0767231 -0.1841362
2521230824 : 32540.063480 -0.184059
2521230822 : 32542.2970040 -0.1840721
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