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# Polynomial

## NAME

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

## DESCRIPTION

This module contains routines for doing simple polynomial fits to data

## SYNOPSIS

```    \$yfit = fitpoly1d \$data;
```

## FUNCTIONS

fitpoly1d
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

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

## BUGS

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