Re: [R] nls minFactor and constraints [was (no subject)]

[Spencer Graves]

In my experience, transformations of the type Doug just described has 
often made sums of squares (or log(likelihood)) contours more parabolic, 
thereby increasing the accuracy of the simple normal approximations to 
the distributions of parameter estimates.  It is wise to check these 
things, as it is not always true.

hope this helps.  spencer graves

Douglas Bates wrote:
giovanni caggiano <[EMAIL PROTECTED]> writes:


A couple of questions about the nls package.


1. I'm trying to run a nonlinear least squares
regression but the routine gives me the following
error message:
step factor 0.000488281 reduced below `minFactor' of
0.000976563
even though I previously wrote the following command: 
nls.control(minFactor = 1/4096), which should set the
minFactor to a lower level than the default one,
1/1024=0.000976563. 
Is there any way of setting the new minfactor to a
lower level?


You need to set control=nls.control(minFactor=1/4096) in the call to
nls.

2. Is it possible to set some constraints upon the
parameters to be estimated in a nls regression?


Other than by parameter transformation, no.  See section 3.4.1 of
Bates and Watts (1988), "Nonlinear Regression Analysis and Its
Applications", Wiley to see how to use parameter transformations for
this.
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