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)

### Arguments

`X` |
is the model matrix. |

`object` |
is the list returned by `magic` after fitting the
model with model matrix `X` . |

`w` |
is the weight vector used in fitting, or the weight matrix used
in fitting (i.e. supplied to `magic` , if one was.) `t(w)%*%w` should typically give
the inverse of the covariance matrix of the response data supplied to `magic` . |

### Details

`object`

contains `rV`

(*V*, say), and
`scale`

(*s*, say) which can be
used to obtain the require quantities as follows. The Bayesian covariance matrix of
the parameters is *VV's*. The vector of
estimated degrees of freedom for each parameter is the leading diagonal of
*VV'X'W'WX*
where *W* is either the
weight matrix `w`

or the matrix `diag(w)`

. The
hat/influence matrix is given by
*WXVV'X'W'*
.

The frequentist parameter estimator covariance matrix is
*VV'X'W'WXVV's*:
it is 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

`magic`

[Package

*mgcv* version 1.3-12

Index]