R nls results different from those of Excel ??
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.
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Greg Snow <538280 at gmail.com> 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 <dgwenzi at gmail.com> 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
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