This thread unfortunately pushes a number of buttons:

- Excel computing a model by linearization which fits to

residual = log(data) - log(model)

rather than

wanted_residual = data - model

The COBB.RES example in my (freely available but rather dated) book

at

http://macnash.telfer.uottawa.ca/nlpe/ shows an example where

comparing the results shows how extreme the differences can be.

- nls not doing well when the fit is near perfect. Package nlmrt is

happy to compute such models, which have a role in approximation. The

builders of nls() are rather (too?) insistent that nls() is a

statistical function rather than simply nonlinear least squares. I can

agree with their view in its context, but not for a general scientific

computing package that R has become. It is one of the gotchas of R.

- Rolf's suggestion to inform Microsoft is, I'm sure, made with the sure

knowledge that M$ will ignore such suggestions. They did, for example,

fix one financial function temporarily (I don't know which). However,

one of Excel's maintainers told me he would disavow admitting that

"Bill" called to tell them to put the bug back in because the president

of a large American bank called to complain his 1998 profit and loss

spreadsheet had changed in the "new" version of Excel. Appearances are

more important than getting things right. At the same conference where

this "I won't admit I told you" conversation took place, a presentation

was made estimating that 95% of major investment decisions were made

based on Excel spreadsheets. The conference took place before the 2008

crash. One is tempted to make non-statistical inferences.

JN

On 13-02-19 06:00 AM,

[hidden email] wrote:

> Message: 79

> Date: Mon, 18 Feb 2013 22:40:25 -0800

> From: Jeff Newmiller<

[hidden email]>

> To: Greg Snow<

[hidden email]>, David Gwenzi<

[hidden email]>

> Cc: r-help<

[hidden email]>

> Subject: Re: [R] R nls results different from those of Excel ??

> Message-ID:<

[hidden email]>

> Content-Type: text/plain; charset=UTF-8

>

> Excel definitely does not use nonlinear least squares fitting for power curve fitting. It uses linear LS fitting of the logs of x and y. There should be no surprise in the OP's observation.

> ---------------------------------------------------------------------------

> Jeff Newmiller The ..... ..... Go Live...

> DCN:<

[hidden email]> Basics: ##.#. ##.#. Live Go...

> Live: OO#.. Dead: OO#.. Playing

> Research Engineer (Solar/Batteries O.O#. #.O#. with

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> ---------------------------------------------------------------------------

> Sent from my phone. Please excuse my brevity.

>

> Greg Snow<

[hidden email]> wrote:

>

>> >Have you plotted the data and the lines to see how they compare? (see

>> >fortune(193)).

>> >

>> >Is there error around the line in the data? The nls function is known

>> >to

>> >not work well when there is no error around the line. Also check and

>> >make

>> >sure that the 2 methods are fitting the same model.

>> >

>> >You might consider taking the log of both sides of the function to turn

>> >it

>> >into a linear function and using lm to fit the logs.

>> >

>> >

>> >On Mon, Feb 18, 2013 at 9:49 PM, David Gwenzi<

[hidden email]>

>> >wrote:

>> >

>>> >>Hi all

>>> >>

>>> >>I have a set of data whose scatter plot shows a very nice power

>>> >>relationship. My problem is when I fit a Power Trend Line in an Excel

>>> >>spreadsheet, I get the model y= 44.23x^2.06 with an R square value of

>> >0.72.

>>> >>Now, if I input the same data into R and use

>>> >>model< -nls(y~ a*x^b , trace=TRUE, data= my_data, start = c(a=40,

>> >b=2)) I

>>> >>get a solution with a = 246.29 and b = 1.51. I have tried several

>> >starting

>>> >>values and this what I always get. I was expecting to get a value of

>> >a

>>> >>close to 44 and that of b close to 2. Why are these values of a and b

>>> >>so different from those Excel gave me. Also the R square value for

>> >the nls

>>> >>model is as low as 0.41. What have I done wrong here? Please help.

>> >Thanks

>>> >>in advance

>>> >>

>>> >>David

>>> >>

>>> >> [[alternative HTML version deleted]]

>>> >>

>>> >>______________________________________________

>>> >>

[hidden email] mailing list

>>> >>

https://stat.ethz.ch/mailman/listinfo/r-help>>> >>PLEASE do read the posting guide

>>> >>

http://www.R-project.org/posting-guide.html>>> >>and provide commented, minimal, self-contained, reproducible code.

>>> >>

______________________________________________

[hidden email] mailing list

https://stat.ethz.ch/mailman/listinfo/r-helpPLEASE do read the posting guide

http://www.R-project.org/posting-guide.htmland provide commented, minimal, self-contained, reproducible code.