fda.usc (version 1.2.3)

cond.mode: Conditional mode

Description

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

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

f

The conditional density function evaluated in 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.

See Also

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

Examples

## Not run: 
# 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")
# ## End(Not run)