vcov.gam                package:mgcv                R Documentation

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 = TRUE, dispersion = NULL, ...)

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.

     ...: 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.

See Also:

     'gam'

Examples:

      
     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))

