
Thanks for the response. I'm sorry I didn't provide the code or data example earlier. I was using the polynomial fitting technique of this form;

test <- lm(x[,34] ~ I(x[,1]) + I(x[,1]^2) + I(x[,1]^3))

for the original fitting operation. I also tried to use;

lm(y ~ poly(x,3,raw=TRUE))

with the same results for the polynomial coefficients in both cases. If my understanding is correct, both of the methods above produce the coefficients of a polynomial based on the data in 'y' as that data varies over 'x'. Therefore, I would assume that the function of the polynomial should always produce the same results as the predict() function in R produces. However, here are the raw data for anyone that has the time to help me out.

y:
[1] 9097 9074 9043 8978 8912 8847 8814 8786 8752 8722 8686 8657 8610 8604 8554
[16] 8546 8496 8482 8479 8462 8460 8438 8428 8418 8384

x:
[1] 17.50    NA 20.59 21.43 17.78 21.89    NA 22.86    NA  6.10    NA  5.37
[13]  3.80    NA  6.80    NA    NA    NA  5.80    NA    NA    NA    NA    NA
[25]    NA

I think that R lm() just ignores the NA values, but I've also tried this by first eliminating NAs and the corresponding x values from the data before fitting the poly and the result was the same coefficients. Thanks very much to anyone who is willing to provide information.

Chris Carleton

> CC: r-help@r-project.org
> From: r.turner@auckland.ac.nz
> Subject: Re: [R] Polynomial Fitting
> Date: Tue, 29 Sep 2009 13:30:07 +1300
> To: w_chris_carleton@hotmail.com
> 
> 
> On 29/09/2009, at 10:52 AM, chris carleton wrote:
> 
> >
> > Hello All,
> >
> >  This might seem elementary to everyone, but please bear with me. I've
> >  just spent some time fitting poly functions to time series data in R
> >  using lm() and predict(). I want to analyze the functions once I've
> >  fit them to the various data I'm studying. However, after pulling the
> >  first function into Octave (just by plotting the polynomial function
> >  using fplot() over the same x interval as my original data) I was
> >  surprised to see that the scale and y values were vastly different
> >  than the ones I have in R. The basic shape of the polynomial over the
> >  same interval looks similar in both Octave and R, but the y values  
> > are
> >  all different. When I compute the y values using the polynomial
> >  function by hand, the y values from the Octave plot are returned and
> >  not the y values predicted by predict() in R. Can someone explain to
> >  me why the values for a function would be different in R? Thanks,
> >  Chris Carleton
> 
> Presumably because you were using poly() with the argument "raw" left
> equal to its default, i.e. FALSE.
> 
> 	cheers,
> 
> 		Rolf Turner
> 
> P. S.  The posting guide asks for reproducible examples .....
> 
> 		R. T.
> 
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