acgm.bandwidth.selection.linkfunction {cvmgof} | R Documentation |
This function computes the optimal bandwidth of the link function under the null hypothesis
acgm.bandwidth.selection.linkfunction(data.X.H0, data.Y.H0, linkfunction.H0, kernel.function = kernel.function.epan, verbose = TRUE)
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 |
Romain Azais, Sandie Ferrigno and Marie-Jose Martinez
J. T. Alcala, J. A. Cristobal, and W. Gonzalez Manteiga. Goodness-of-fit test for linear models based on local polynomials. Statistics & Probability Letters, 42(1), 39:46, 1999.
R. Azais, S. Ferrigno and M-J Martinez. cvmgof: An R package for Cramér-von Mises goodness-of-fit tests in regression models. 2018. Preprint in progress.
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.acgm = acgm.bandwidth.selection.linkfunction(data.X.H0 , data.Y.H0,linkfunction.H0)