vkgmss.sd.estim {cvmgof} | R Documentation |
This function computes the kernel (Nadaraya-Watson) estimation of the standard deviation function.
vkgmss.sd.estim(x, data.X, data.Y, bandwidth, kernel.function = kernel.function.epan)
x |
a numeric vector. |
data.X |
a numeric data vector used to obtain the kernel estimator of the standard deviation function. |
data.Y |
a numeric data vector used to obtain the kernel estimator of the standard deviation function. |
bandwidth |
bandwidth used to obtain the kernel estimator of the standard deviation function. |
kernel.function |
kernel function used to obtain the kernel estimator of the standard deviation function. Default option is "kernel.function.epan" which corresponds to the Epanechnikov kernel function. |
Inappropriate bandwidth or x choices can produce "NaN" values in function estimates.
Romain Azais, Sandie Ferrigno and Marie-Jose Martinez
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 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 ######################################################################## # Estimation of residuals standard deviation bandwidth = 0.75 # Here, the bandwidth is arbitrarily fixed xgrid = seq(0,5,by=0.1) sd = vkgmss.sd.estim(xgrid,data.X,data.Y,bandwidth) plot(xgrid,sd , type='l',xlab='X',ylab='SD(X)') abline(h=0.3)