kern {generalCorr} | R Documentation |
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, 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)