Conditional mode
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 Grid of length n where the the conditional density function is evaluated.
f The conditional density function evaluated in x.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.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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