fda.usc (version 2.0.2)

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,
  ...
)

Value

Return the quantile for conditional distribution function.

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.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente

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

if (FALSE) {
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)

# 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))
}