fda.usc (version 1.1.0)

Kernel: Symmetric Smoothing Kernels.

Description

Represent symmetric smoothing kernels:: normal, cosine, triweight, quartic and uniform. ll{ Ker.norm=dnorm(u) Ker.cos=ifelse(abs(u)

Usage

Kernel(u,type.Ker="Ker.norm")
Ker.norm(u)
Ker.cos(u)
Ker.epa(u)
Ker.tri(u)
Ker.quar(u)
Ker.unif(u)

Arguments

type.Ker

Type of Kernel. By default normal kernel.

u

Data.

Value

• res
  Returns symmetric kernel.

Details

Type of kernel: ll{ Normal Kernel: Ker.norm Cosine Kernel: Ker.cos Epanechnikov Kernel: Ker.epa Triweight Kernel: Ker.tri Quartic Kernel: Ker.quar Uniform Kernel: Ker.unif }

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.

Examples

y=qnorm(seq(.1,.9,len=100))
a<-Kernel(u=y)
b<-Kernel(type.Ker="Ker.tri",u=y)
c=Ker.cos(y)