fda.usc (version 2.1.0)

cond.mode: Conditional mode

Description

Computes the mode for conditional distribution function.

Usage

cond.mode(Fc, method = "monoH.FC", draw = TRUE)

Value

Return the mode for conditional distribution function.

• mode.cond Conditional mode.

• x Grid of length n where the the conditional density function is evaluated.

• f The conditional density function evaluated in x.

Arguments

Fc

Object estimated by cond.F function.

method

Specifies the type of spline to be used. Possible values are "diff", "fmm", "natural", "periodic" and "monoH.FC".

draw

=TRUE, plots the conditional distribution and density function.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente

Details

The conditional mode is calculated as the maximum argument of the derivative of the conditional distribution function (density function f).

References

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

See Also

See Also as: cond.F, cond.quantile and splinefun .

Examples

if (FALSE) {
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=101:110
pr0=x[ind];pr10=x[ind2]
ndist=161
gridy=seq(-1.598069,1.598069, len=ndist)
# Conditional Function
I=5
# Time consuming
res = cond.F(pr10[I], gridy, prx, pry, h=1)
mcond=cond.mode(res)
mcond2=cond.mode(res,method="diff")
}