fda.usc (version 1.1.0)

cond.mode: Conditional mode

Description

Computes the mode for conditional distribution function.

Usage

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

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".

Value

• Return the mode for conditional distribution function.
• mode.cond
  Conditional mode.
• ind.mode.cond
  Index of Conditional mode.

Details

The conditional mode is calculated as the maximum argument of the derivative of the conditional distribution function.

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

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")