Calculate Variance-Covariance Matrix for a `nlregb' Fit

Usage

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

Arguments

object The return values of a nlregb fit.
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.

Description

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

Value

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

See Also

vcov


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