kern_ctrl | R Documentation |

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

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

`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 |

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.

This is a work horse for causal identification.

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

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 `kern`

.

```
## 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)
```

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