R: Variance Covariance Matrix of maxLik objects

vcov.maxLik {maxLik} | R Documentation

## 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)

[Package maxLik version 1.3-4 Index]