magic.post.proc {mgcv} | R Documentation |

## Auxilliary information from magic fit

### Description

Obtains Bayesian parameter covariance matrix, frequentist
parameter estimator covariance matrix, estimated degrees of
freedom for each parameter and leading diagonal of influence/hat matrix,
for a penalized regression estimated by `magic`

.

### Usage

```
magic.post.proc(X,object,w=NULL)
```

### Arguments

`X` |
is the model matrix. |

`object` |
is the list returned by |

`w` |
is the weight vector used in fitting, or the weight matrix used
in fitting (i.e. supplied to |

### Details

`object`

contains `rV`

(` {\bf V}`

, say), and
`scale`

(` \phi`

, say) which can be
used to obtain the require quantities as follows. The Bayesian covariance matrix of
the parameters is ` {\bf VV}^\prime \phi`

. The vector of
estimated degrees of freedom for each parameter is the leading diagonal of
` {\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime {\bf W}{\bf X}`

where `\bf{W}`

is either the
weight matrix `w`

or the matrix `diag(w)`

. The
hat/influence matrix is given by
` {\bf WX}{\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime `

.

The frequentist parameter estimator covariance matrix is
` {\bf VV}^\prime {\bf X}^\prime {\bf W}^\prime {\bf WXVV}^\prime \phi`

:
it is sometimes useful for testing terms for equality to zero.

### Value

A list with three items:

`Vb` |
the Bayesian covariance matrix of the model parameters. |

`Ve` |
the frequentist covariance matrix for the parameter estimators. |

`hat` |
the leading diagonal of the hat (influence) matrix. |

`edf` |
the array giving the estimated degrees of freedom associated with each parameter. |

### Author(s)

Simon N. Wood simon.wood@r-project.org

### See Also

*mgcv*version 1.9-1 Index]