Conditional quantile
Computes the quantile for conditional distribution function.
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, ... )
qua |
Quantile value, by default the median ( |
fdata0 |
Conditional functional explanatory data of |
fdataobj |
Functional explanatory data of |
y |
Scalar Response. |
fn |
Conditional distribution function. |
a |
Lower limit. |
b |
Upper limit. |
tol |
Tolerance. |
iter.max |
Maximum iterations allowed, by default |
... |
Further arguments passed to or from other methods. |
Return the quantile for conditional distribution function.
Manuel Febrero-Bande, Manuel Oviedo de la Fuente manuel.oviedo@usc.es
Ferraty, F. and Vieu, P. (2006). Nonparametric functional data analysis. Springer Series in Statistics, New York.
## 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)) ## End(Not run)
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