print.reg.fun {Ake} | R Documentation |

## Print for regression function

### Description

The function allows to print the result of computation in regression as a data frame.

### Usage

```
## S3 method for class 'reg.fun'
print(x, digits = NULL, ...)
```

### Arguments

`x` |
object of class |

`digits` |
The number of digits |

`...` |
Further arguments |

### Details

The associated kernel estimator `\widehat{m}_n`

of `m`

is defined in the above sections; see Kokonendji and Senga Kiessé (2011). The bandwidth parameter in the function is obtained using the cross-validation technique for the associated kernels.

### Value

Returns a list containing:

`data` |
The explanatory variable, printed as a data frame |

`y` |
The response variable, printed as a data frame |

`n` |
The size of the sample |

`kernel` |
The associated kernel |

`h` |
The smoothing parameter |

`eval.points` |
The grid where the regression is computed, printed as data frame |

`m_n` |
The estimated values, printed as data frame |

`Coef_det` |
The Coefficient of determination |

### Author(s)

W. E. Wansouwé, S. M. Somé and C. C. Kokonendji

### References

Kokonendji, C.C. and Senga Kiessé, T. (2011). Discrete associated kernel method and extensions,
*Statistical Methodology* **8**, 497 - 516.

Kokonendji, C.C., Senga Kiessé, T. and Demétrio, C.G.B. (2009). Appropriate kernel regression on a count explanatory variable and applications,
*Advances and Applications in Statistics* **12**, 99 - 125.

Zougab, N., Adjabi, S. and Kokonendji, C.C. (2014). Bayesian approach in nonparametric count regression with Binomial Kernel, * Communications in Statistics - Simulation and Computation * **43**, 1052 - 1063.

### Examples

```
data(milk)
x=milk$week
y=milk$yield
##The bandwidth is the one obtained by cross validation.
h<-0.10
## We choose binomial kernel.
m_n<-reg.fun(x, y, "discrete",ker="bino", h)
print.reg.fun(m_n)
```

*Ake*version 1.0.1 Index]