vcov.maxLik: Variance Covariance Matrix of maxLik objects

In maxLik: Maximum Likelihood Estimation and Related Tools

Variance Covariance Matrix of maxLik objects

Description

Extract variance-covariance matrices from maxLik objects.

Usage

   ## S3 method for class 'maxLik'
vcov( object, eigentol=1e-12, ... )

Arguments

object	a ‘maxLik’ object.
eigentol	eigenvalue tolerance, controlling when the Hessian matrix is treated as numerically singular.
...	further arguments (currently ignored).

Details

The standard errors are only calculated if the ratio of the smallest and largest eigenvalue of the Hessian matrix is less than “eigentol”. Otherwise the Hessian is treated as singular.

Value

the estimated variance covariance matrix of the coefficients. In case of the estimated Hessian is singular, it's values are Inf. The values corresponding to fixed parameters are zero.

Author(s)

Arne Henningsen, Ott Toomet

See Also

vcov, maxLik.

Examples

## ML estimation of exponential random variables
t <- rexp(100, 2)
loglik <- function(theta) log(theta) - theta*t
gradlik <- function(theta) 1/theta - t
hesslik <- function(theta) -100/theta^2
## Estimate with numeric gradient and hessian
a <- maxLik(loglik, start=1, control=list(printLevel=2))
vcov(a)
## Estimate with analytic gradient and hessian
a <- maxLik(loglik, gradlik, hesslik, start=1)
vcov(a)

maxLik documentation built on May 29, 2024, 2:32 a.m.