cond.mode | R Documentation |
Computes the mode for conditional distribution function.
cond.mode(Fc, method = "monoH.FC", draw = TRUE)
Fc |
Object estimated by |
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. |
The conditional mode is calculated as the maximum argument of the derivative
of the conditional distribution function (density function f
).
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
.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es
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 .
## 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)
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