generalCorr (version 1.2.6)

kern2ctrl: Kernel regression with control variables and optional residuals and gradients. version 2 regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection. It admits control variables.

Description

Kernel regression with control variables and optional residuals and gradients. version 2 regtype="ll" for local linear, bwmethod="cv.aic" for AIC-based bandwidth selection. It admits control variables.

Usage

kern2ctrl(
  dep.y,
  reg.x,
  ctrl,
  tol = 0.1,
  ftol = 0.1,
  gradients = FALSE,
  residuals = FALSE
)

Value

Creates a model object `mod' containing the entire kernel regression output. If this function is called as mod=kern_ctrl(x,y,ctrl=z), the researcher can simply type names(mod) to reveal the large variety of outputs produced by `npreg' of the `np' package. The user can access all of them at will using the dollar notation of R.

Arguments

dep.y

Data on the dependent (response) variable

reg.x

Data on the regressor (stimulus) variable

ctrl

Data matrix on the control variable(s) kept outside the causal paths. A constant vector is not allowed as a control 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

Author

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, tools:::Rd_expr_doi("10.1080/03610918.2015.1122048")

See Also

See kern.

Examples

if (FALSE) {
set.seed(34);x=matrix(sample(1:600)[1:50],ncol=5)
require(np)
k1=kern_ctrl(x[,1],x[,2],ctrl=x[,4:5])
print(k1$R2) #prints the R square of the kernel regression
}