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

cond.mode

R Documentation

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.

Details

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

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.

Author(s)

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

References

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

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)

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