[R] weighting in nls

[Robert Brown FM CEFAS]

Hi there,

this is the output from R

> solb2wvb<-nls(~sqrt(novervar)*(weight-(a*(1-exp(-b*(age-c))))^3),data=solb2.na.rm,start=list(a=0.85,b=0.45,c=0.48))
> summary(solb2wvb)

Formula:  ~ sqrt(novervar) * (weight - (a * (1 - exp( - b * (age - c))))^3)

Parameters:
      Value Std. Error  t value 
a  1.087370 0.01193090  91.1392
b  0.151838 0.00714963  21.2372
c -1.809770 0.13186000 -13.7250

Residual standard error: 4.41368 on 109 degrees of freedom

The output from Prism is:

von Bertalanffy	
Best-fit values	
     A	0.8957
     B	0.2381
     C	-1.358
Std. Error	
     A	0.002280
     B	0.002568
     C	0.02919
95% Confidence Intervals	
     A	0.8912 to 0.9001
     B	0.2331 to 0.2431
     C	-1.415 to -1.300

The latter has much better visual fit and reasonable residuals. Furthermore theory and practice both lead to the expectation that this model should fit the data.

Incidentally, I was under the impression that with a weighted nls in R the SE values were not accurate.

Finally I've attached the dataset




-----Original Message-----
From: Liaw, Andy [mailto:andy_liaw at merck.com]
Sent: 27 January 2005 15:25
To: Robert Brown FM CEFAS; r-help at stat.math.ethz.ch
Subject: RE: [R] weighting in nls


Can you show us the difference; i.e., what are the parameter estimates and
associated SEs from the two programs?  Even better, can you supply an
example data set?

[With is `trick' for weighted nls, you need to be careful with the output of
predict().]

Andy

> From: Robert Brown FM CEFAS
> 
> I'm fitting nonlinear functions to some growth data but  I'm 
> getting radically different results in R to another program 
> (Prism). Furthermore the values from the other program give a 
> better fit and seem more realistic.  I think there is a 
> problem with the results from the r nls function. The 
> differences only occur with weighted data so I think I'm 
> making a mistake in the weighting. I'm following the 
> procedure outlined on p 244 of MASS (or at least I'm trying to).
> 
> Thus, I'm using mean data with heteroscedasticity so I'm 
> weighting by n/ variance, where the variance is well known 
> from a large data set. This weighting factor is available as 
> the variable 'novervar'.
> 
> The function is a von Bertalanffy curve of the form 
> weight~(a*(1-exp(-b*(age-c))))^3.  Thus I'm entering the 
> command in the form:
> 
> solb1wvb<-nls(~sqrt(novervar)*(weight-(a*(1-exp(-b*(age-c))))^
> 3),data=solb1.na.rm,start=list(a=0.85,b=0.45,c=0.48))
> 
> Can anyone suggest what I'm doing wrong?  I seem to be 
> folowing the instructions in MASS. I tried following the 
> similar instructions on page 450 of the white book but these 
> were a bit cryptic.
> 
> I'm using R 2.0.0 on a Windows 2000 machine
> 
> Regards,
> 
> Robert Brown
> 
> 
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