Conditional mode

Computes the mode for conditional distribution function.

Usage

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

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.

Value

Return the mode for conditional distribution function.

mode.cond: Conditional mode.
x: A grid of length n where the conditional density function is evaluated.
f: The conditional density function evaluated at x.

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.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

Examples

if (FALSE) { # \dontrun{
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")
} # }