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.

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`

.

Argument | Description | Default |
---|---|---|

x | A vector specifying the predictor variable, or a two-column matrix specifying both the predictor and the response variables. | |

y | If `x` is a vector, then
`y` is a vector containing the
response variable. | `NULL` |

n | If `xout` is not
specified, then interpolation is done at n equally spaced points
between `xmin` and `xmax` . | `3*length(x)` |

method | Specifies the type of spline. Allowed values include
`"fmm"` , `"natural"` , `"periodic"` , and `"monoH.FC"` . | `"fmm"` |

xmin | Lowest `x` value for
interpolations. | `min(x)` |

xmax | Highest `x` value for
interpolations. | `max(x)` |

xout | An optional vector of values specifying where interpolation should be done. | |

ties | A 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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