Conditional quantile

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.

Author

Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@udc.es

Examples

if (FALSE) { # \dontrun{
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))
} # }