# cond.mode

0th

Percentile

##### 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 as: cond.F, cond.quantile and splinefun .

• 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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