dmudr {assist} | R Documentation |

## Interface of dmudr subroutine in RKPACK

### Description

To calculate a spline estimate with multiple smoothing parameters

### Usage

```
dmudr(y, q, s, weight = NULL, vmu = "v", theta = NULL, varht = NULL,
tol = 0, init = 0, prec = 1e-06, maxit = 30)
```

### Arguments

`y` |
a numerical vector representing the response. |

`q` |
a list, or an array, of square matrices of the same order as the length of y, which are the reproducing kernels evaluated at the design points. |

`s` |
the design matrix of the null space |

`weight` |
a weight matrix for penalized weighted least-square: |

`vmu` |
a character string specifying a method for choosing the smoothing parameter. "v", "m" and "u" represent GCV, GML and UBR respectively. "u |

`theta` |
If ‘init=1’, theta includes intial values for smoothing parameters. Default is NULL. |

`varht` |
needed only when vmu="u", which gives the fixed variance in calculation of the UBR function. Default is NULL. |

`tol` |
the tolerance for truncation in the tridiagonalization. Default is 0.0. |

`init` |
an integer of 0 or 1 indicating if initial values are provided for theta. If init=1, initial values are provided using theta. Default is 0. |

`prec` |
precision requested for the minimum score value, where precision is the weaker of the absolute and relative precisions. Default is |

`maxit` |
maximum number of iterations allowed. Default is 30. |

### Value

`info` |
an integer that provides error message. info=-1 indicates dimension error,
info=-2 indicates |

`fit` |
fitted values. |

`c` |
estimates of c. |

`d` |
estimates of d. |

`resi` |
vector of residuals. |

`varht` |
estimate of variance. |

`theta` |
estimates of parameters |

`nlaht` |
the estimate of |

`score` |
the minimum GCV/GML/UBR score at the estimated smoothing parameters. |

`df` |
equavilent degree of freedom. |

`nobs` |
length(y), number of observations. |

`nnull` |
dim( |

`nq` |
length(rk), number of reproducing kernels. |

`s` , `q` , `y` |
changed from the inputs. |

### Author(s)

Chunlei Ke chunlei_ke@yahoo.com and Yuedong Wang yuedong@pstat.ucsb.edu

### References

Gu, C. (1989). RKPACK and its applications: Fitting smoothing spline models. Proceedings of the Statistical Computing Section, ASA, 42-51.

Wahba, G. (1990). Spline Models for Observational Data. SIAM, Vol. 59

### See Also

*assist*version 3.1.9 Index]