QuantifQuantile.d: QuantifQuantile for general X

Description Usage Arguments Details Value References See Also Examples

View source: R/QuantifQuantile.d.R

Description

Estimation of conditional quantiles using optimal quantization when X is d-dimensional.

Usage

1
2
3
QuantifQuantile.d(X, Y, x, alpha = c(0.05, 0.25, 0.5, 0.75, 0.95),
  testN = c(35, 40, 45, 50, 55), p = 2, B = 50, tildeB = 20,
  same_N = TRUE, ncores = 1)

Arguments

X

matrix of covariates.

Y

vector of response variables.

x

matrix of values for x in q_alpha(x).

alpha

vector of order of the quantiles.

testN

grid of values of N that will be tested.

p

L_p norm optimal quantization.

B

number of bootstrap replications for the bootstrap estimator.

tildeB

number of bootstrap replications for the choice of N.

same_N

whether to use the same value of N for each alpha (TRUE by default).

ncores

number of cores to use. Default is set to 1 (see Details below).

Details

Value

An object of class QuantifQuantile which is a list with the following components:

hatq_opt

A matrix containing the estimated conditional quantiles. The number of columns is the number of considered values for x and the number of rows the size of the order vector alpha. This object can also be returned using the usual fitted.values function.

N_opt

Optimal selected value for N. An integer if same_N=TRUE and a vector of integers of length length(alpha) otherwise.

hatISE_N

The matrix of estimated ISE provided by our selection criterion for N before taking the mean according to alpha. The number of columns is then length(testN) and the number of rows length(alpha).

hatq_N

A 3-dimensional array containing the estimated conditional quantiles for each considered value for alpha, x and N.

X

The matrix of covariates.

Y

The vector of response variables.

x

The considered vector of values for x in q_alpha(x).

alpha

The considered vector of order for the quantiles.

testN

The considered grid of values for N that were tested.

References

Charlier, I. and Paindaveine, D. and Saracco, J., Conditional quantile estimation through optimal quantization, Journal of Statistical Planning and Inference, 2015 (156), 14-30.

Charlier, I. and Paindaveine, D. and Saracco, J., Conditional quantile estimator based on optimal quantization: from theory to practice, Submitted.

See Also

QuantifQuantile and QuantifQuantile.d2 for particular dimensions one and two.

plot.QuantifQuantile, print.QuantifQuantile, summary.QuantifQuantile

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
## Not run: 
set.seed(644925)
n <- 500
X <- runif(n,-2,2)
Y <- X^2+rnorm(n)
x <- seq(min(X),max(X),length=100)
res <- QuantifQuantile.d(X,Y,x,testN=seq(15,35,by=5))

## End(Not run)
## Not run: 
set.seed(272422)
n <- 1000
X <- matrix(runif(n*2,-2,2),ncol=n)
Y <- apply(X^2,2,sum)+rnorm(n)
x1 <- seq(min(X[1,]),max(X[1,]),length=20)
x2 <- seq(min(X[2,]),max(X[2,]),length=20)
x <- matrix(c(rep(x1,20),sort(rep(x2,20))),nrow=nrow(X),byrow=TRUE)
res <- QuantifQuantile.d(X,Y,x,testN=seq(90,140,by=10),B=20,tildeB=15)

## End(Not run)

QuantifQuantile documentation built on May 29, 2017, 3:31 p.m.

Search within the QuantifQuantile package
Search all R packages, documentation and source code