kern: Kernel regression with options for residuals and gradients.

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Kernel regression with options for residuals and gradients.

Description

Function to run kernel regression with options for residuals and gradients asssuming no missing data.

Usage

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) variables
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	Make this TRUE if gradients computations are desired
residuals	Make this 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, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")}

See Also

See kern_ctrl.

Examples

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

## End(Not run)

generalCorr documentation built on Oct. 10, 2023, 1:06 a.m.