fda.usc (version 1.2.3)

cond.quantile: Conditional quantile

Description

Computes the quantile for conditional distribution function.

Usage

cond.quantile(qua=0.5,fdata0,fdataobj,y,fn,a=min(y),b=max(y),
tol=10^floor(log10(max(y)-min(y))-3),iter.max=100,...)

Arguments

qua

Quantile value, by default the median (qua=0.5).

fdata0

Conditional functional explanatory data of fdata class object.

fdataobj

Functional explanatory data of fdata class object.

y

Scalar Response.

fn

Conditional distribution function.

a

Lower limit.

b

Upper limit.

tol

Tolerance.

iter.max

Maximum iterations allowed, by default 100.

...

Further arguments passed to or from other methods.

Value

Return the quantile for conditional distribution function.

References

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

See Also

See Also as: cond.F and cond.mode.

Examples

n= 100
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[1:50];pry=y[1:50]
ind=50+1;ind2=51:60
pr0=x[ind];pr10=x[ind2]
ndist=161
gridy=seq(-1.598069,1.598069, len=ndist)
ind4=5
y0 = gridy[ind4]

## Conditional median
med=cond.quantile(qua=0.5,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)

## Not run
## Conditional CI 95% conditional
# lo=cond.quantile(qua=0.025,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
# up=cond.quantile(qua=0.975,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
# print(c(lo,med,up))