fda.usc (version 0.9.4)

cond.F: Conditional Distribution Function

Description

Calculate the conditional distribution function of a scalar response with functional data.

Usage

cond.F(fdata0,y0,fdataobj,y,h=0.15,g=0.15,metric=metric.lp,
Ker=list(AKer=AKer.epa,IKer=IKer.epa),...)

Arguments

fdata0

Conditional explanatory functional data of fdata class.

y0

Vector of conditional response with length n.

fdataobj

fdata class object.

y

Vector of scalar response with length nn.

h

Smoothing parameter or bandwidth of response y.

g

Smoothing parameter or bandwidth of explanatory functional data fdataobj.

metric

Metric function, by default metric.lp.

Ker

List of 2 arguments. The fist argument is a character string that determines the type of asymetric kernel (see Kernel.asymmetric). Asymmetric Epanechnikov kernel is selected by default. The second

...

Further arguments passed to or from other methods.

Value

• Fc
  Conditional distribution function.
• y0
  Vector of conditional response.
• g
  Smoothing parameter or bandwidth of explanatory functional data (fdataobj).
• h
  Smoothing parameter or bandwidth of respone, y.
• x.dist
  Distance matrix between curves of fdataobj object.
• xy.dist
  Distance matrix between cuves of fdataobj and fdata0 objects.

Details

If x.dist=NULL the distance matrix between fdata objects is calculated by function passed in metric argument.

References

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

See Also

See Also as: cond.mode and cond.quantile.

Examples

# Read data
library(e1071)
n= 500
t= seq(0,1,len=101)
beta = t*sin(2*pi*t)^2
x = matrix(NA, ncol=101, nrow=n)
y=numeric(n)
for (i in 1:n){
	x[i,] = rwiener(1,101)*3 + rnorm(101,sd=0.1)
	y[i] = mean((x[i,])*beta)+rnorm(1,sd=0.1)
}

prx=x[1:100,];pry=y[1:100]
ind=101;ind2=102:110
pr0=x[ind,];pr10=x[ind2,]
ndist=61
gridy=seq(-1.598069,1.598069, len=ndist)
# Conditional Function
res1 = cond.F(pr10, gridy, prx, pry,p=1)
res2 = cond.F(pr10, gridy, prx, pry,h=0.3)
res3 = cond.F(pr10, gridy, prx, pry,g=0.25,h=0.3)

plot(res1$Fc[,1],type="l",ylim=c(0,1))
lines(res2$Fc[,1],type="l",col=2)
lines(res3$Fc[,1],type="l",col=3)