Estimates the control vector for a spline fit by penalized least squares. The penalty being the penalty parameter times the functional inner product of the second derivative of the spline curve.

1 | ```
fitLS(object, x, y, penalty = 0)
``` |

`object` |
The SplineBasis object ot be used to make the fit |

`x` |
predictor variable. |

`y` |
response variable. |

`penalty` |
The penalty multiplier. |

For numeric vector y, and x, and a set of basis functions, represented in `object`

, defined on the knots *(k_0,…,k_m)*.
The likelihood is defined by

*sum_i (y_i-b(x_i)mu) + integral mu^T b''(t)^T b''(t) mu dt*

The fucntion estimates *μ*.

a vector of the control points.

Andrew Redd <aredd at stat.tamu.edu>

1 2 3 4 5 |

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