sp.vcov {mgcv} | R Documentation |

## Extract smoothing parameter estimator covariance matrix from (RE)ML GAM fit

### Description

Extracts the estimated covariance matrix for the log smoothing parameter
estimates from a (RE)ML estimated `gam`

object, provided the fit was with a method
that evaluated the required Hessian.

### Usage

```
sp.vcov(x,edge.correct=TRUE,reg=1e-3)
```

### Arguments

`x` |
a fitted model object of class |

`edge.correct` |
if the model was fitted with |

`reg` |
regularizer for Hessian - default is equivalent to prior variance of 1000 on log smoothing parameters. |

### Details

Just extracts the inverse of the hessian matrix of the negative (restricted) log likelihood w.r.t the log smoothing parameters, if this has been obtained as part of fitting.

### Value

A matrix corresponding to the estimated covariance matrix of the log smoothing parameter estimators,
if this can be extracted, otherwise `NULL`

. If the scale parameter has been (RE)ML estimated (i.e. if the method was `"ML"`

or `"REML"`

and the scale parameter was unknown) then the
last row and column relate to the log scale parameter. If `edge.correct=TRUE`

and this was used in fitting then the edge corrected smoothing parameters are in attribute `lsp`

of the returned matrix.

### Author(s)

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

### References

Wood, S.N., N. Pya and B. Saefken (2016), Smoothing parameter and model selection for general smooth models (with discussion). Journal of the American Statistical Association 111, 1548-1575 doi:10.1080/01621459.2016.1180986

### See Also

### Examples

```
require(mgcv)
n <- 100
x <- runif(n);z <- runif(n)
y <- sin(x*2*pi) + rnorm(n)*.2
mod <- gam(y~s(x,bs="cc",k=10)+s(z),knots=list(x=seq(0,1,length=10)),
method="REML")
sp.vcov(mod)
```

*mgcv*version 1.9-0 Index]