Returns the numerical partial derivate of `y`

with respect to [wrt] any regressor for a point of interest. Finite difference method is used with NNS.reg estimates as `f(x+h)`

and `f(x-h)`

values.

1 2 3 |

`x` |
a numeric matrix or data frame. |

`y` |
a numeric vector with compatible dimsensions to |

`wrt` |
integer; Selects the regressor to differentiate with respect to. |

`eval.points` |
numeric or options: ("median","last"); Regressor points to be evaluated. Set to |

`order` |
integer; NNS.reg |

`stn` |
numeric [0,1]; Signal to noise parameter, sets the threshold of NNS.dep which reduces |

`h` |
numeric [0,...]; Percentage step used for finite step method. Defaults to |

`n.best` |
integer; Sets the number of closest regression points to use in weighting. Defaults to 2. |

`mixed` |
logical; |

`plot` |
logical; |

`noise.reduction` |
the method of determing regression points options: ("mean","median","mode","off"); In low signal to noise situations, |

Returns the 1st derivative `"First Derivative"`

, 2nd derivative `"Second Derivative"`

, and mixed derivative `"Mixed Derivative"`

(for two independent variables only).

For known function testing and analysis, regressors should be transformed via expand.grid to fill the dimensions with `(order="max")`

. Example provided below.

Fred Viole, OVVO Financial Systems

Viole, F. and Nawrocki, D. (2013) "Nonlinear Nonparametric Statistics: Using Partial Moments" http://amzn.com/1490523995

1 2 3 4 5 6 7 8 9 | ```
set.seed(123);x_1<-runif(100);x_2<-runif(100); y<-x_1^2*x_2^2
B=cbind(x_1,x_2)
## To find derivatives of y wrt 1st regressor
dy.d_(B,y,wrt=1,eval.points=c(.5,.5))
## Known function analysis
x_1<-seq(0,1,.1);x_2<-seq(0,1,.1)
B=expand.grid(x_1,x_2); y<-B[,1]^2*B[,2]^2
dy.d_(B,y,wrt=1,eval.points=c(.5,.5),order="max")
``` |

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