# estimdep: Dependence estimation In subrank: Computes Copula using Ranks and Subsampling

## Description

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

## Usage

 `1` ```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

Jerome Collet

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25``` ```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 May 2, 2019, 10:24 a.m.