Thank you Gerrit for the quick reply! And yes, i'm Matti.
I can get the coeffs now, though i'm not sure whether i'm doing
something wrong or whether poly is just not the right method for what
i'm trying to find. I will look into this more closely and give it
another try.
Is poly best for fitting on noisy data that's been generated by a
polynomial and not that good for approximating an arbitrary function? I
tried a least squares fitting with a web applet and got all exited
because the approximation looked quite promising. I understand that R is
designed mainly for statistical computing and may not be the best tool
for my purposes. Before i look elsewhere i would like to ask if there is
some other R method i should try, perhaps a least squares approximation?
Thank you for your help!
Matti Jokipii
08.07.2011 08:25, Gerrit Eichner kirjoitti:
Hello, mfa (Matti?),
if x and y contain the coordinates of your data points and k is the
wanted polynomial degree, then
fit <- lm( y ~ poly( x, k))
fits orthonormal polynomials up to degree k to your data. Using
dummy.coef( fit)
should give the coefficients you are interested in.
Hth -- Gerrit
On Thu, 7 Jul 2011, mfa wrote:
Hello,
i'm fairly familiar with R and use it every now and then for math related
tasks.
I have a simple non polynomial function that i would like to approximate
with a polynomial. I already looked into poly, but was unable to
understand
what to do with it. So my problem is this. I can generate virtually any
number of datapoints and would like to find the coeffs a1, a2, ... up
to a
given degree for a polynomial a1x^1 + a2x^2 + ... that approximates my
simple function. How can i do this with R?
Your help will be highly appreciated!
--
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