cond.mode

Conditional mode

Computes the mode for conditional distribution function.

Keywords
distribution
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.

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 .

Aliases
  • cond.mode
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")
# }
# NOT RUN {
# }
Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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