NNS.diff: NNS Numerical Differentiation

In NNS: Nonlinear Nonparametric Statistics

NNS Numerical Differentiation

Description

Determines numerical derivative of a given univariate function using projected secant lines on the y-axis. These projected points infer finite steps h, in the finite step method.

Usage

NNS.diff(f, point, h = 0.1, tol = 1e-10, digits = 12, print.trace = FALSE)

Arguments

f	an expression or call or a formula with no lhs.
point	numeric; Point to be evaluated for derivative of a given function f.
h	numeric [0, ...]; Initial step for secant projection. Defaults to (h = 0.1).
tol	numeric; Sets the tolerance for the stopping condition of the inferred h. Defaults to (tol = 1e-10).
digits	numeric; Sets the number of digits specification of the output. Defaults to (digits = 12).
print.trace	logical; FALSE (default) Displays each iteration, lower y-intercept, upper y-intercept and inferred h.

Value

Returns a matrix of values, intercepts, derivatives, inferred step sizes for multiple methods of estimation.

Author(s)

Fred Viole, OVVO Financial Systems

References

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" (ISBN: 1490523995)

Examples

## Not run: 
f <- function(x) sin(x) / x
NNS.diff(f, 4.1)

## End(Not run)

NNS documentation built on Oct. 14, 2024, 5:09 p.m.