Kernel

Symmetric Smoothing Kernels.

Represent symmetric smoothing kernels:: normal, cosine, triweight, quartic and uniform.

Keywords
Kernel
Usage
Kernel(u, type.Ker = "Ker.norm")
Arguments
u

Data.

type.Ker

Type of Kernel. By default normal kernel.

Details

Ker.norm=dnorm(u)
Ker.cos=ifelse(abs(u)<=1,pi/4*(cos(pi*u/2)),0)
Ker.epa=ifelse(abs(u)<=1,3/4*(1-u^2),0)
Ker.tri=ifelse(abs(u)<=1,35/32*(1-u^2)^3,0)
Ker.quar=ifelse(abs(u)<=1,15/16*(1-u^2)^2,0)
Ker.unif=ifelse(abs(u)<=1,1/2,0)

Type of kernel:

Normal Kernel: Ker.norm
Cosine Kernel: Ker.cos
Epanechnikov Kernel: Ker.epa
Triweight Kernel: Ker.tri
Quartic Kernel: Ker.quar
Uniform Kernel: Ker.unif

Value

Returns symmetric kernel.

References

Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.

Hardle, W. Applied Nonparametric Regression. Cambridge University Press, 1994.

Aliases
  • Kernel
  • Ker.norm
  • Ker.cos
  • Ker.epa
  • Ker.tri
  • Ker.quar
  • Ker.unif
Examples
# NOT RUN {
y=qnorm(seq(.1,.9,len=100))
a<-Kernel(u=y)
b<-Kernel(type.Ker="Ker.tri",u=y)
c=Ker.cos(y)
# }
Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

Community examples

Looks like there are no examples yet.