# NNS.diff: NNS Numerical Differentiation In NNS: Nonlinear Nonparametric Statistics

## 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

 `1` ```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" https://www.amazon.com/dp/1490523995/ref=cm_sw_su_dp

## Examples

 ```1 2``` ```f <- function(x) sin(x) / x NNS.diff(f, 4.1) ```

NNS documentation built on June 26, 2021, 1:07 a.m.