Variance Covariance Matrix of maxLik objects
Extract variance-covariance matrices from maxLik objects.
## S3 method for class 'maxLik' vcov( object, eigentol=1e-12, ... )
object |
a ‘maxLik’ object. |
eigentol |
eigenvalue tolerance, controlling when the Hessian matrix is treated as numerically singular. |
... |
further arguments (currently ignored). |
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.
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.
Arne Henningsen, Ott Toomet
## 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)
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