Description Usage Arguments Details Value References See Also Examples

A fast, FORTRAN based function for cubic spline interpolation.

1 |

`x` |
The x values that define the curve or a two column matrix of x and y values. |

`y` |
The y values that are paired with the x's. |

`xgrid` |
The grid to evaluate the fitted cubic interpolating curve. |

`derivative` |
Indicates whether the function or a a first or second derivative should be evaluated. |

`wt` |
Weights for different obsrevations in the scale of reciprocal variance. |

`lam` |
Value for smoothing parameter. Default value is zero giving interpolation. |

`lambda` |
Same as |

`df` |
Effective degrees of freedom. Default is to use lambda =0 or a df equal to the number of observations. |

`nx` |
If not NULL this should be the number of points
to evaluate on an equally spaced grid in the
range of |

Fits a piecewise interpolating or smoothing cubic
polynomial to the x and y values.
This code is designed to be fast but does not many options in
`sreg`

or other more statistical implementations.
To make the solution well posed the
the second and third derivatives are set to zero at the limits of the x
values. Extrapolation outside the range of the x
values will be a linear function.

It is assumed that there are no repeated x values; use sreg followed by predict if you do have replicated data.

A vector consisting of the spline evaluated at the grid values in `xgrid`

.

See Additive Models by Hastie and Tibshriani.

sreg, Tps

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | ```
x<- seq( 0, 120,,200)
# an interpolation
splint(rat.diet$t, rat.diet$trt,x )-> y
plot( rat.diet$t, rat.diet$trt)
lines( x,y)
#( this is weird and not appropriate!)
# the following two smooths should be the same
splint( rat.diet$t, rat.diet$con,x, df= 7)-> y1
# sreg function has more flexibility than splint but will
# be slower for larger data sets.
sreg( rat.diet$t, rat.diet$con, df= 7)-> obj
predict(obj, x)-> y2
# in fact predict.sreg interpolates the predicted values using splint!
# the two predicted lines (should) coincide
lines( x,y1, col="red",lwd=2)
lines(x,y2, col="blue", lty=2,lwd=2)
``` |

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