mgcv: vcov.gam – R documentation

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vcov.gam

Extract parameter (estimator) covariance matrix from GAM fit

Description

Extracts the Bayesian posterior covariance matrix of the parameters or frequentist covariance matrix of the parameter estimators from a fitted gam object.

Usage

## S3 method for class 'gam'
vcov(object, freq = FALSE, dispersion = NULL,unconditional=FALSE, ...)

Arguments

`object`	fitted model object of class `gam` as produced by `gam()`.
`freq`	`TRUE` to return the frequentist covariance matrix of the parameter estimators, `FALSE` to return the Bayesian posterior covariance matrix of the parameters.
`dispersion`	a value for the dispersion parameter: not normally used.
`unconditional`	if `TRUE` (and `freq==FALSE`) then the Bayesian smoothing parameter uncertainty corrected covariance matrix is returned, if available.
`...`	other arguments, currently ignored.

Details

Basically, just extracts object$Ve or object$Vp from a gamObject.

Value

A matrix corresponding to the estimated frequentist covariance matrix of the model parameter estimators/coefficients, or the estimated posterior covariance matrix of the parameters, depending on the argument freq.

Author(s)

Henric Nilsson. Maintained by Simon N. Wood simon.wood@r-project.org

References

Wood, S.N. (2006) On confidence intervals for generalized additive models based on penalized regression splines. Australian and New Zealand Journal of Statistics. 48(4): 445-464.

Examples

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

mgcv

Mixed GAM Computation Vehicle with Automatic Smoothness Estimation

v1.8-35

GPL (>= 2)

Authors

Simon Wood <simon.wood@r-project.org>

Initial release