deriva: Numerical approch to derivate.

In ellessenne/mla: An Algorithm for Least-squares Curve Fitting

Description

Function to return the first, second derivate and the information score matrix. The central finite-difference and forward finite-difference will be used.

Usage

deriva(b, funcpa)

Arguments

b	Value of parameters to be optimized over.
funcpa	Function to be minimized (or maximized), with argument the vector of parameters over which minimization is to take place. It should return a scalar result.

Value

A list with the following elements:

• v, the information score matrix;

• rl, log-likelihood or likelihood of the model.

Author(s)

Daniel Commenges

References

Donald W. Marquardt (1963). An algorithm for least-squares estimation of nonlinear parameters. Journal of the Society for Industrial and Applied Mathematics, 11(2):431–441

Examples

b <- 0.1
f <- function(b) {
  return((2 * b[1]**2 + 3 * b[1]))
}
d <- deriva(b = b, funcpa = f)

ellessenne/mla documentation built on March 29, 2020, 12:20 p.m.