Calculates and adds the scores to a fitted model object

Calculates and adds the scores to a fitted model object of. The scores are the gradient observations/first derivatives of the log-likelihood function. The function is a wrapper around numDeriv::jacobian().

Usage

add_scores(object, func, x, ...)

Arguments

object: A fitted model object of class
func: A function with real (vector) results. This is typically the log-likelihood function. The function must return the function values at the observation level.
x: A real or real vector argument to func, indicating the point at which the gradient is to be calculated.
...: Additional arguments passed to numDeriv::jacobian().

Value

A fitted model object with the scores added to the object.