kern_ctrl: Kernel regression with control variables and optional...

In generalCorr: Generalized Correlations, Causal Paths and Portfolio Selection

Kernel regression with control variables and optional residuals and gradients.

Description

Allowing matrix input of control variables, this function runs kernel regression with options for residuals and gradients.

Usage

kern_ctrl(
  dep.y,
  reg.x,
  ctrl,
  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
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

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.

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.

Examples

## Not run: 
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

## End(Not run)

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