AngDensPlot {ExtremalDep} | R Documentation |
Plots (log)-angular densities on the three-dimensional simplex. Contour levels and data points (optional) are represented.
AngDensPlot(model='Pairwise', para=c(2,4,15,1), log=TRUE, data=NULL, contour=TRUE, labels=c(expression(w[1]),expression(w[3]), expression(w[2])), cex.dat=1, cex.lab=1, cex.cont=1)
model |
A string with the name of the parametric model for the angular density. |
para |
A numeric vector with the parameters of the parameteric model. Default is |
log |
Logical; if |
data |
If a (three-dimensional) dataset if provided then the data points are added to the density plot. |
contour |
Logical; if |
labels |
Labels for the three corners of the simplex. Default is
|
cex.dat |
Magnification of data points. Only if |
cex.lab |
Magnification of the labels. |
cex.cont |
Magnification of the contour labels. |
Contour levels are given for the deciles. If data != NULL
then the deciles are calculated using the density
values of each data point. If data = NULL
then the deciles are calcuated using all the points of the grid.
labels
are given in an anticlockwise order: bottom right, top middle and bottom left.
Simone Padoan, simone.padoan@unibocconi.it, http://faculty.unibocconi.it/simonepadoan; Boris Beranger, borisberanger@gmail.com http://www.borisberanger.com;
Beranger, B. and Padoan, S. A. (2015). Extreme dependence models, chapater of the book Extreme Value Modeling and Risk Analysis: Methods and Applications, Chapman Hall/CRC.
Beranger, B., Padoan, S. A. and Sisson, S. A. (2017). Models for extremal dependence derived from skew-symmetric families. Scandinavian Journal of Statistics, 44(1), 21-45.
################################################ # The following examples provide the plots of # Figure 1.2 of the paper Beranger and Padoan (2015) ################################################ # The code has been frozen to speed up the package check. # Please remove the hash symbol to test the code. # Asymmetric Logistic AngDensPlot('Asymmetric', c(rep(1,3),5.75, rep(0,6), 0.5,0.5,0.5), FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot('Asymmetric', c(rep(1,3),1.01, rep(0,6), 0.9,0.9,0.9), FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot('Asymmetric', c(rep(1,3),1.25, rep(0,6), 0.5,0.5,0.5), FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot('Asymmetric', c(rep(1,3),1.4, rep(0,6),0.7,0.15,0.15), FALSE, cex.lab=1.5, cex.cont=1.3) # Tilted Dirichlet AngDensPlot(model='Dirichlet', para=c(2,2,2), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Dirichlet', para=c(0.5,0.5,0.5), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Dirichlet', para=c(2,2.5,30), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Dirichlet', para=c(0.1,0.25,0.95), log=FALSE, cex.lab=1.5, cex.cont=1.3) # Pairwise Beta AngDensPlot(model='Pairwise', para=c(2,2,2,4), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Pairwise', para=c(1,1,1,0.5), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Pairwise', para=c(2,4,15,1), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Pairwise', para=c(10,10,10,1), log=FALSE, cex.lab=1.5, cex.cont=1.3) # Husler-Reiss AngDensPlot(model='Husler', para=c(0.3,0.3,0.3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Husler', para=c(1.4,1.4,1.4), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Husler', para=c(1.7,0.7,1.1), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Husler', para=c(0.52,0.71,0.52), log=FALSE, cex.lab=1.5, cex.cont=1.3) # Extremal-t AngDensPlot(model='Extremalt', para=c(0.95,0.95,0.95,2), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Extremalt', para=c(-0.3,-0.3,-0.3,5), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Extremalt', para=c(0.52,0.71,0.52,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Extremalt', para=c(0.52,0.71,0.52,2), log=FALSE, cex.lab=1.5, cex.cont=1.3) ################################################ # The following examples provide # the plots of Figure 1.3 of the paper # Beranger and Padoan (2015) ################################################ ## Load datasets data(pollution) Nsim <- 50e+4 Nbin <- 30e+4 MCpar <- 0.35 Hpar.pb <- list(mean.alpha=0, mean.beta=3,sd.alpha=3, sd.beta=3) Hpar.hr <- list(mean.lambda=0, sd.lambda=3) Hpar.di <- list(mean.alpha=0, sd.alpha=3) Hpar.et <- list(mean.rho=0, mean.mu=3,sd.rho=3, sd.mu=3) ## PNS Data est.pb.PNS <- posteriorMCMC(Nsim, Nbin, Hpar.pb, MCpar, PNS, seed=14342, model='Pairwise') est.hr.PNS <- posteriorMCMC(Nsim, Nbin, Hpar.hr, MCpar, PNS, seed=14342, model='Husler') est.di.PNS <- posteriorMCMC(Nsim, Nbin, Hpar.di, MCpar, PNS, seed=14342, model='Dirichlet') lab1 <- c("PM10","NO","SO2") AngDensPlot("Pairwise", est.pb.PNS$emp.mean, data=PNS, labels=lab1, cex.dat=0.8) AngDensPlot("Husler", est.hr.PNS$emp.mean, data=PNS, labels=lab1, cex.dat=0.8) AngDensPlot("Dirichlet", est.di.PNS$emp.mean, data=PNS, labels=lab1, cex.dat=0.8) ## NSN data est.pb.NSN <- posteriorMCMC(Nsim, Nbin, Hpar.pb, MCpar, NSN, seed=14342, model='Pairwise') est.hr.NSN <- posteriorMCMC(Nsim, Nbin, Hpar.hr, MCpar, NSN, seed=14342, model='Husler') est.di.NSN <- posteriorMCMC(Nsim, Nbin, Hpar.di, MCpar, NSN, seed=14342, model='Dirichlet') lab2 <- c("NO2","NO","SO2") AngDensPlot("Pairwise", est.pb.NSN$emp.mean, data=NSN, labels=lab2, cex.dat=0.8) AngDensPlot("Husler", est.hr.NSN$emp.mean, data=NSN, labels=lab2, cex.dat=0.8) AngDensPlot("Dirichlet", est.di.NSN$emp.mean, data=NSN, labels=lab2, cex.dat=0.8) ## PNN data est.pb.PNN <- posteriorMCMC(Nsim, Nbin, Hpar.pb, MCpar, PNN, seed=14342, model='Pairwise') est.hr.PNN <- posteriorMCMC(Nsim, Nbin, Hpar.hr, MCpar, PNN, seed=14342, model='Husler') est.di.PNN <- posteriorMCMC(Nsim, Nbin, Hpar.di, MCpar, PNN, seed=14342, model='Dirichlet') lab3 <- c("PM10","NO","NO2") AngDensPlot("Pairwise", est.pb.PNN$emp.mean, data=PNN, labels=lab3, cex.dat=0.8) AngDensPlot("Husler", est.hr.PNN$emp.mean, data=PNN, labels=lab3, cex.dat=0.8) AngDensPlot("Dirichlet", est.di.PNN$emp.mean, data=PNN, labels=lab3, cex.dat=0.8) ################################################ # The following examples provide the plots of # the supplementary material for # Beranger, Padoan and Sisson (2016) ################################################ AngDensPlot(model='Skewt', para=c(0.6,0.8,0.7,0,0,0,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Skewt', para=c(0.6,0.8,0.7,-3,-3,7,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Skewt', para=c(0.6,0.8,0.7,7,-10,3,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Skewt', para=c(0.7,0.7,0.7,0,0,0,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Skewt', para=c(0.7,0.7,0.7,-3,-3,7,3), log=FALSE, cex.lab=1.5, cex.cont=1.3) AngDensPlot(model='Skewt', para=c(0.7,0.7,0.7,7,-10,3,3), log=FALSE, cex.lab=1.5, cex.cont=1.3)