# smth: Smooth Numerical Data In smoother: Functions Relating to the Smoothing of Numerical Data

## Description

Helper function to smooth numerical data using methods specified by the user.

## Usage

 ```1 2``` ```smth(x = stop("Numeric Vector 'x' is Required"), method = getOption("smoother.method"), ...) ```

## Arguments

 `x` numeric vector of values to smooth `method` one of ```'gaussian', 'sma', 'ema', 'dema'``` or `'wma'`. `...` any other arguments to be passed to each specific smoothing methodology.

## Details

At this moment in time, the only method is the `'gaussian'` window function (similar to the Matlab Gaussian Window Smoothing Function) and a number of moving averages `'sma', 'ema', 'dema'` or `'wma'`. These are functions that allows the user to smooth an input vector, returning vector of the same length as the input. This can also be achieved using the specific `smth.gaussian` function.

## Value

a numeric vector of same length as input `'x'` vector

## References

If the `'method'` argument is equal to `'gaussian'`, then this function is a port of the function described here: http://goo.gl/HGM47U, very loosly based of code which has also been ported to c++ here: http://goo.gl/NK79bJ

Refer to specific man files: `smth.gaussian`, `SMA`, `EMA`, `DEMA` or `WMA`

## Examples

 ```1 2 3 4 5 6 7 8``` ```#Prepare Data n = 1000 x = seq(-pi,pi,length.out=n) y = sin(x) + (runif(length(x))*0.1) #NOISY DATA ys = smth(y,window = 0.1,method = "gaussian") #SMOOTHING plot(x,y,type="l",col="red") lines(x,ys,col="black",lwd=3) title("Example Smoothing of Noisy Data") ```

### Example output ```Loading required package: TTR
```

smoother documentation built on May 2, 2019, 4 p.m.