cond.quantile

Conditional quantile

Computes the quantile for conditional distribution function.

Keywords
distribution
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.

Aliases
  • cond.quantile
Examples
# NOT RUN {
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))
# }
# NOT RUN {
# }
Documentation reproduced from package fda.usc, version 2.0.1, License: GPL-2

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