Kernel regression with options for residuals and gradients.

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Description

Function to run kernel regression with options for residuals and gradients.

Usage

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kern(dep.y, reg.x, tol = 0.1, ftol = 0.1, gradients = FALSE,
  residuals = FALSE)

Arguments

dep.y

Data on the dependent (response) variable

reg.x

Data on the regressor (stimulus) variable

tol

Tolerance on the position of located minima of the cross-validation function (default =0.1)

ftol

Fractional tolerance on the value of cross validation function evaluated at local minima (default =0.1)

gradients

Set to TRUE if gradients computations are desired

residuals

Set to TRUE if residuals are desired

Value

Creates a model object ‘mod’ containing the entire kernel regression output. Type names(mod) to reveal the variety of outputs produced by ‘npreg’ of the ‘np’ package. The user can access all of them at will by using the dollar notation of R.

Note

This is a work horse for causal identification.

Author(s)

Prof. H. D. Vinod, Economics Dept., Fordham University, NY

References

Vinod, H. D.'Generalized Correlation and Kernel Causality with Applications in Development Economics' in Communications in Statistics -Simulation and Computation, 2015, http://dx.doi.org/10.1080/03610918.2015.1122048

Examples

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## Not run: 
set.seed(34);x=matrix(sample(1:600)[1:50],ncol=2)
require(np)
k1=kern(x[,1],x[,2])
print(k1$R2) #prints the R square of the kernel regression

## End(Not run)

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