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

## Description

Determines numerical derivative of a given 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, 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`. Defualts to `(tol=1e-10)`. `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" http://amzn.com/1490523995

## Examples

 ```1 2 3 4 5``` ```f<- function(x) sin(x)/x NNS.diff(f,4.1) g<- function(x) sin(x) NNS.diff(g,1) ```

NNS documentation built on Aug. 16, 2017, 1:06 a.m.