mgcv (version 1.8-29)

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 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.

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.

Details

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

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.

See Also

gam

Examples

# NOT RUN {
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))
# }