MVAkernelfunctions (R 2.12.1)

MVAkernelfunctions plots different kernel functions

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Andrija Mihoci, Awdesch Melzer

Applied Multivariate Statistical Analysis

Wed, May 16 2012 by Dedy Dwi Prastyo

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Description: -


rm(list=ls(all=TRUE))
graphics.off()
u = seq(-3,3,0.01)
K_Uniform = 0.5 * (abs(u) <= 1); # kernel of uniform distribution
K_Triangle = (1 - abs(u)) * (abs(u) <= 1); # kernel of triangle distribution
K_Epanechnikov = 0.75 * (1 - u^2) * (abs(u) <= 1); # epanechnikov kernel
K_Quartic = 0.9375 * (1 - u^2)^2 * (abs(u) <= 1); # kernel of quadratic biweighted distribution
K_Gaussian = 0.3989 * exp(-0.5 * u^2); # kernel of a gaussian distribution
par(mfrow=c(2,3))
plot(u, K_Uniform,type="s",col="blue3",ylim=c(0,1),ylab="",xlab="",lwd=2.5); title('Uniform')
plot(u, K_Triangle,type="s",col="blue3",ylim=c(0,1),ylab="",xlab="",lwd=2.5); title('Triangle')
plot(u, K_Epanechnikov,type="s",col="blue3",ylim=c(0,1),ylab="",xlab="",lwd=2.5); title('Epanechnikov')
plot(u, K_Quartic,type="s",col="blue3",ylim=c(0,1),ylab="",xlab="",lwd=2.5); title('Quartic (biweight)')
plot(u, K_Gaussian,type="s",col="blue3",ylim=c(0,1),ylab="",xlab="",lwd=2.5); title('Gaussian')