Provided by: pdl_2.4.7+dfsg-2ubuntu5_amd64 bug


       PDL::Fit::Polynomial - routines for fitting with polynomials


       This module contains routines for doing simple polynomial fits to data


           $yfit = fitpoly1d $data;


       Fit 1D polynomials to data using min chi^2 (least squares)

        Usage: ($yfit, [$coeffs]) = fitpoly1d [$xdata], $data, $order, [Options...]

       Signature: (x(n); y(n); [o]yfit(n); [o]coeffs(order))

       Uses a standard matrix inversion method to do a least squares/min chi^2 polynomial fit to
       data. Order=2 is a linear fit (two parameters).

       Returns the fitted data and optionally the coefficients.

       One can thread over extra dimensions to do multiple fits (except the order can not be
       threaded over - i.e. it must be one fixed scalar number like "4").

       The data is normalised internally to avoid overflows (using the mean of the abs value)
       which are common in large polynomial series but the returned fit, coeffs are in
       unnormalised units.

         $yfit = fitpoly1d $data,2; # Least-squares line fit
         ($yfit, $coeffs) = fitpoly1d $x, $y, 4; # Fit a cubic

         $fitimage = fitpoly1d $image,3  # Fit a quadratic to each row of an image

         $myfit = fitpoly1d $line, 2, {Weights => $w}; # Weighted fit

            Weights    Weights to use in fit, e.g. 1/$sigma**2 (default=1)


       May not work too well for data with large dynamic range.


       "polyfit" in PDL::Slatec


       This file copyright (C) 1999, Karl Glazebrook (  All rights
       reserved. There is no warranty. You are allowed to redistribute this software
       documentation under certain conditions. For details, see the file COPYING in the PDL
       distribution. If this file is separated from the PDL distribution, the copyright notice
       should be included in the file.