fda.usc (version 0.9.4)

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

# Read data
library(e1071)
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)
for (i in 1:n){
	x[i,] = rwiener(1,101)*3 + rnorm(101,sd=0.1)
	y[i] = mean((x[i,])*beta)+rnorm(1,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)
ind4=5
y0 = gridy[ind4]

#Conditional median an CI 95 conditional
med=cond.quantile(qua=0.5,fdata0=pr0,fdataobj=prx,y=pry,fn=cond.F,h=1)
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))