vkgmss.bandwidth.selection.linkfunction {cvmgof} | R Documentation |
Bandwidth selection of the link function under the null hypothesis
Description
This function computes the optimal bandwidth of the link function under the null hypothesis
Usage
vkgmss.bandwidth.selection.linkfunction(data.X.H0, data.Y.H0, linkfunction.H0,
kernel.function = kernel.function.epan, verbose = TRUE)
Arguments
data.X.H0 |
a numeric data vector used to obtain the nonparametric estimator of the regression function under the null hypothesis. |
data.Y.H0 |
a numeric data vector used to obtain the nonparametric estimator of the regression function under the null hypothesis. |
linkfunction.H0 |
regression function under the null hypothesis |
kernel.function |
kernel function used to obtain the nonparametric estimator of the regression function. Default option is "kernel.function.epan" which corresponds to the Epanechnikov kernel function. |
verbose |
If |
Author(s)
Romain Azais, Sandie Ferrigno and Marie-Jose Martinez
References
I. Van Keilegom, W. Gonzalez Manteiga, and C. Sanchez Sellero. Goodness-of-fit tests in parametric regression based on the estimation of the error distribution. Test, 17, 401:415, 2008.
R. Azais, S. Ferrigno and M-J Martinez. cvmgof: An R package for Cramer-von Mises goodness-of-fit tests in regression models. Submitted. January 2021.hal-03101612
Examples
# Uncomment the following code block
#
# set.seed(1)
#
# # Data simulation
# n = 25 # Dataset size
# data.X = runif(n,min=0,max=5) # X
# data.Y = 0.2*data.X^2-data.X+2+rnorm(n,mean=0,sd=0.3) # Y
#
# ########################################################################
#
# # Bandwidth selection under H0
#
# # We generate a dataset under H0 to estimate the optimal bandwidth under H0
#
# linkfunction.H0 = function(x){0.2*x^2-x+2}
#
# data.X.H0 = runif(n,min=0,max=5)
# data.Y.H0 = linkfunction.H0(data.X.H0)+rnorm(n,mean=0,sd=0.3)
#
# h.opt.vkgmss = vkgmss.bandwidth.selection.linkfunction(data.X.H0, data.Y.H0,linkfunction.H0)