cond.F: Conditional Distribution Function

In fda.usc: Functional Data Analysis and Utilities for Statistical Computing

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 argumentis a string that determines the type of integrated kernel(see Kernel.integrate). Integrate Epanechnikov kernel is selected by default. .
...	Further arguments passed to or from other methods.

Details

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

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.

Author(s)

Manuel Febrero-Bande, Manuel Oviedo de la Fuente [e-mail]

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

## Not run: 
# Read data
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)
x0<-rproc2fdata(n,seq(0,1,len=101),sigma="wiener")
x1<-rproc2fdata(n,seq(0,1,len=101),sigma=0.1)
x<-x0*3+x1
fbeta = fdata(beta,t)
y<-inprod.fdata(x,fbeta)+rnorm(n,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)

## End(Not run)

fda.usc documentation built on Oct. 17, 2022, 9:06 a.m.