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# Smoothing

This section describes a number of functions for fitting piecewise smooth curves to data. Functions in this section are particularly useful for plotting charts; there are even convenience functions for using these functions to show fitted values in some graphics packages.

## Splines

One method for fitting a function to source data is with splines. With a linear model, a single line is fitted to all the data. With spline methods, a set of different polynomials is fitted to different sections of the data.

You can compute simple cubic splines with the `spline` function in the `stats` package:

```spline(x, y = NULL, n = 3 * length(x), method = "fmm", xmin = min(x),
xmax = max(x), xout, ties = mean)```

Here is a description of the arguments to `smooth.spline`.

ArgumentDescriptionDefault
xA vector specifying the predictor variable, or a two-column matrix specifying both the predictor and the response variables.
yIf `x` is a vector, then `y` is a vector containing the response variable.`NULL`
nIf `xout` is not specified, then interpolation is done at n equally spaced points between `xmin` and `xmax`.`3*length(x)`
methodSpecifies the type of spline. Allowed values include `"fmm"`, `"natural"`, `"periodic"`, and `"monoH.FC"`.`"fmm"`
xminLowest `x` value for interpolations.`min(x)`
xmaxHighest `x` value for interpolations.`max(x)`
xoutAn optional vector of values specifying where interpolation should be done.
tiesA method for handling ties. Either the string `"ordered"` or a function that returns a single numeric value.`mean`

To return a function ...

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