# lspoly_n

Calculates a set of coefficients for a weighted least squares polynomial fit to the given data on the given dimension.

*Available in version 6.2.0 and later.*

## Prototype

function lspoly_n ( x : numeric, y : numeric, wgt : numeric, n [1] : integer, dim [1] : integer ) return_val : float or double

## Arguments

*x*

Abscissa values of the data.

This can be one-dimensional or multi-dimensional. If one-dimensional,
it must be the same length as the rightmost dimension of *y*.
If multi-dimensional, it must be the same dimensionality
as *y*.

*y*

Ordinate values of the data.

*wgt*

Weights for a weighted least squares model. If all data values are to be assigned equal weights, then setting the argument equal to a scalar 1.0 will result in all the weights being set to 1.0. Note: if x or y is equal to _FillValue (if present), the weight will be set to 0.0 for that coordinate pair.

This can be one-dimensional or multi-dimensional. If one-dimensional,
it must be the same length as the rightmost dimension of *y*.
If multi-dimensional, it must be the same dimensionality
as *y*.

*n*

The number of coefficients desired (i.e., *n*-1 will be
the degree of the polynomial). It is suggested that
*n* be less than or equal to five.

*dim*

A scalar integer indicating which dimension of *y*
to do the calculation on. Dimension numbering starts at 0.

## Return value

The return array will have the same dimensionality as *y*,
except the *dim*-th dimension will be of length *n*.

If either *x* or *y* are of type double, then the return
array is returned as double. Otherwise, the returned coefficients
are returned as type float.

## Description

Given a set of data (x(i),y(i)), i = 1,...,m,
**lspoly_n** calculates a set of coefficients for a
weighted least squares polynomial fit to the given data on the given
dimension. It is necessary that the number of data points) be greater
than *n* (the number of coefficients).

## See Also

**lspoly**,
**regCoef**, **regline**,
**regcoef**, **reg_multlin**

## Examples

**Example 1**

Note: we don't need to use **lspoly_n** in this
example, since we are only using 1D arrays, and hence there's no
reordering necessary.

x = (/-4.5, -3.2, -1.4, 0.8, 2.5, 4.1/) y = (/ 0.7, 2.3, 3.8, 5.0, 5.5, 5.6/) n = 4 c =The 3rd degree polynomial islspoly_n(x, y, 1, n, 0) ; all weights are set to one

Y = c(0) + c(1)*x + c(2)*x^2 + c(3)*x^3The coefficients (which agree with those returned from

*Mathematica*) are:

(0) 4.66863 (1) 0.489392 (2) -0.0742387 (3) 0.00267663

**Example 2**

Similar as the previous example, but this time assume x and y are 3D arrays and that we want to solve the equation for the middle (dim=1) dimension:

n = 4 c =lspoly_n(x, y, 1, n, 1) ; all weights are set to one