estimdep: Dependence estimation

In subrank: Computes Copula using Ranks and Subsampling

Dependence estimation

Description

From a set of observations, builds a description of the multivariate distribution

Usage

estimdep(dataframe,varnames,subsampsize,nbsafe=5,mixties=FALSE,nthreads=2)

Arguments

dataframe	a data frame containing the observations
varnames	the name of the variables we want to estimate the multivariate distribution
subsampsize	the sub-sample size
nbsafe	the ratio between the discretized copula size and the number of sub-samples
mixties	if TRUE, put equal weight on tied values, using random permutations
nthreads	number of number of threads, assumed to be strictly positive. For "full throttle" computations, consider using parallel::detectCores()

Value

the description of the dependence, it is an object with the following parts:

cop	the array representing the discretized copula
margins	the matrix representing the margins, estimated using kernel density estimation
varnames	the names of the variables

Author(s)

Jerome Collet

Examples

lon=3000
plon=3000
subsampsize=20

##############
x=(runif(lon)-1/2)*3
y=x^2+rnorm(lon)
z=rnorm(lon)
donori=as.data.frame(cbind(x,y,z))
depori=estimdep(donori,c("x","y","z"),subsampsize)

knownvalues=data.frame(z=rnorm(plon))
prev <- predictdep(knownvalues,depori)
plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)
points(donori[,1:2],col='red',pch=20,cex=.5)

knownvalues=data.frame(x=(runif(lon)-1/2)*3)
prev <- predictdep(knownvalues,depori)
plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)
points(donori[,1:2],col='red',pch=20,cex=.5)

knownvalues=data.frame(y=runif(plon,min=-2,max=4))
prev <- predictdep(knownvalues,depori)
plot(prev$x,prev$y,xlim=c(-2,2),ylim=c(-2,5),pch=20,cex=0.5)
points(donori[,1:2],col='red',pch=20,cex=.5)

subrank documentation built on April 6, 2023, 1:11 a.m.