print.reg.fun {Ake} | R Documentation |
The function allows to print the result of computation in regression as a data frame.
## S3 method for class 'reg.fun'
print(x, digits = NULL, ...)
x |
object of class |
digits |
The number of digits |
... |
Further arguments |
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.
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 |
W. E. Wansouwé, S. M. Somé and C. C. Kokonendji
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.
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)