Calculate Variance-Covariance Matrix for a nlregb Fit

DESCRIPTION:

Method for vcov to find the variance-covariance matrix of the coefficients of fits by nlregb.

USAGE:

vcov.nlregb(object, method=c("Fisher", "observed", "Huber"),
   scale=object$scale, eps=0.001, tol=1)

REQUIRED ARGUMENTS:

object
The return values of a nlregb fit.

OPTIONAL ARGUMENTS:

method
The theoretical basis for the estimate. This can be based on the Fisher information (the usual assumption) or the observed information assuming the model is true or a Huber-White sandwich estimator which allows the model to be false. Only the Fisher method is available if the model was fitted without gradient (Jacobian) information.
scale
An initial scaling for the parameters.
eps
The step size (as a multiple of min(1, abs(param))) for finite-difference approximations to terms in the Hessian.
tol
Relative change in sum-of-squares sought in a local quadratic approximation. See the code for the scaling used.

VALUE:

A matrix of the estimated covariances between the parameter estimates in the non-linear regression.

SEE ALSO: