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 |

`B` |
Complete dataset of regressors in matrix form. |

`y` |
Dependent Variable |

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

`eval.points` |
Regressor points to be evaluated. Set to |

`order` |
NNS.reg order, defaults to 1 for multivariate regressions. If error, make sure |

`s.t.n` |
Signal to noise parameter, sets the threshold of |

`h` |
Percentage step used for finite step method. Defaults to |

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

`mixed` |
If mixed derivative is to be evaluated, set |

`plot` |
Set to |

`precision` |
Sets the number of regression points for estimates. Set to |

`norm` |
Normalizes regressors between 0 and 1 for multivariate regression when set to |

`noise.reduction` |
IIn 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 `precision="HIGH"`

. 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),precision="HIGH")
``` |

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