| compKernVals {locpol} | R Documentation |
Some R code provided to compute kernel related values.
computeRK(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
subdivisions = 25)
computeK4(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
subdivisions = 25)
computeMu(i, kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
subdivisions = 25)
computeMu0(kernel, lower=dom(kernel)[[1]], upper=dom(kernel)[[2]],
subdivisions = 25)
Kconvol(kernel,lower=dom(kernel)[[1]],upper=dom(kernel)[[2]],
subdivisions = 25)
kernel |
Kernel used to perform the estimation, see Kernels |
i |
Order of kernel moment to compute |
lower, upper |
Integration limits. |
subdivisions |
the maximum number of subintervals. |
These functions uses function integrate.
A numeric value returning:
computeK4 |
The fourth order autoconvolution of K. |
computeRK |
The second order autoconvolution of K. |
computeMu0 |
The integral of K. |
computeMu2 |
The second order moment of K. |
computeMu |
The $i$-th order moment of K. |
Kconvol |
The autoconvolution of K. |
normal-bracket44bracket-normal
These functions are implemented by means of integrate.
Jorge Luis Ojeda Cabrera.
Fan, J. and Gijbels, I. Local polynomial modelling and its applications/. Chapman & Hall, London (1996).
Wand, M.~P. and Jones, M.~C. Kernel smoothing/. Chapman and Hall Ltd., London (1995).
RK, Kernel characteristics, integrate.
## Note that lower and upper params are set in the definition to
## use 'dom()' function.
g <- function(kernels)
{
mu0 <- sapply(kernels,function(x) computeMu0(x,))
mu0.ok <- sapply(kernels,mu0K)
mu2 <- sapply(kernels,function(x) computeMu(2,x))
mu2.ok <- sapply(kernels,mu2K)
Rk.ok <- sapply(kernels,RK)
RK <- sapply(kernels,function(x) computeRK(x))
K4 <- sapply(kernels,function(x) computeK4(x))
res <- data.frame(mu0,mu0.ok,mu2,mu2.ok,RK,Rk.ok,K4)
res
}
g(kernels=c(EpaK,gaussK,TriweigK,TrianK))