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Smooth response data

`yy = smooth(y)`

yy = smooth(y,span)

yy = smooth(y,* method*)

yy = smooth(y,span,

`method`

yy = smooth(y,'sgolay',degree)

yy = smooth(y,span,'sgolay',degree)

yy = smooth(x,y,...)

gpuarrayYY = smooth(gpuarrayY)

`yy = smooth(y)`

smooths the
data in the column vector `y`

using a moving average
filter. Results are returned in the column vector `yy`

.
The default span for the moving average is `5`

.

The first few elements of `yy`

are given by

yy(1) = y(1) yy(2) = (y(1) + y(2) + y(3))/3 yy(3) = (y(1) + y(2) + y(3) + y(4) + y(5))/5 yy(4) = (y(2) + y(3) + y(4) + y(5) + y(6))/5 ...

Because of the way endpoints are handled, the result differs
from the result returned by the `filter`

function.

`yy = smooth(y,span)`

sets
the span of the moving average to `span`

. `span`

must
be odd.

`yy = smooth(y,`

smooths
the data in * method*)

`y`

using the method `method`

`method`

| Description |
---|---|

| Moving average (default). A lowpass filter with filter coefficients equal to the reciprocal of the span. |

| Local regression using weighted linear least squares and a 1st degree polynomial model |

| Local regression using weighted linear least squares and a 2nd degree polynomial model |

| Savitzky-Golay filter. A generalized moving average with filter coefficients determined by an unweighted linear least-squares regression and a polynomial model of specified degree (default is 2). The method can accept nonuniform predictor data. |

| A robust version of |

| A robust version of |

`yy = smooth(y,span,`

sets
the span of * method*)

`method`

`span`

.
For the `loess`

and `lowess`

methods, `span`

is
a percentage of the total number of data points, less than or equal
to 1. For the moving average and Savitzky-Golay methods, `span`

must
be odd (an even `span`

is automatically reduced by `1`

).`yy = smooth(y,'sgolay',degree)`

uses
the Savitzky-Golay method with polynomial degree specified by `degree`

.

`yy = smooth(y,span,'sgolay',degree)`

uses
the number of data points specified by `span`

in
the Savitzky-Golay calculation. `span`

must be odd
and `degree`

must be less than `span`

.

`yy = smooth(x,y,...)`

additionally
specifies `x`

data. If `x`

is not
provided, methods that require `x`

data assume ```
x
= 1:length(y)
```

. You should specify `x`

data
when it is not uniformly spaced or sorted. If `x`

is
not uniform and you do not specify `method`

, `lowess`

is
used. If the smoothing method requires `x`

to be
sorted, the sorting occurs automatically.

`gpuarrayYY = smooth(gpuarrayY)`

performs
the operation on a GPU. The input `gpuarrayY`

is
a gpuArray column vector. The output `gpuarrayYY`

is
a gpuArray column vector. This syntax requires the Parallel
Computing Toolbox™.

You can use gpuArray x and y inputs with the smooth function,
but this is only recommended with the default `'method', 'moving'`

.
Using GPU data with other methods does not offer any performance advantage.

Another way to generate smoothed data is to fit it with a smoothing
spline. Refer to the `fit`

function
for more information.

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