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(xh) 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; 
norm 

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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