plot_ExtDep {ExtremalDep} | R Documentation |
Graphical summaries of parametric representations of extremal dependence.
Description
This function displays the angular density, Pickands dependence function and return levels for bivariate and trivariate extreme values models.
Usage
plot_ExtDep(object="angular", model, par, log=TRUE, data=NULL, contour=TRUE,
style, labels, cex.dat=1, cex.lab=1, cex.cont=1,
Q.fix, Q.range, Q.range0, cond=FALSE, ...)
Arguments
object |
A character string indicating which graphical summary to plot. Takes value |
model |
A string with the name of the model considered. Takes value |
par |
A vector representing the parameters of the model. |
log |
A logical value specifying if the log density is computed. Required when |
data |
A matrix representing angular data to be added to the density plot. Required when |
contour |
A logical value; if |
style |
A character string indicating the plotting style of the data. Takes value |
labels |
A vector of character strings indicating the labels. Must be of length |
cex.dat |
A positive real indicating the size of the data points. Required for the trivariate angular density. |
cex.lab |
A positive real indicating the size of the labels. |
cex.cont |
A positive real indicating the size of the contour labels. |
Q.fix |
A vector of length the dimension of the model, indicating some fixed quantiles to compute joint return levels. Must contain |
Q.range |
A vector or matrix indicating quantile values on the unit Frechet scale, for the components that are allowed to vary. Must be a vector or a one-column matrix if there is one |
Q.range0 |
A object of the same format as |
cond |
A logical value; if |
... |
Additional graphical arguments for the |
Details
The angular density is computed using the function dExtDep
with arguments method="Parametric"
and angular=TRUE
. The Pickands dependence function is computed using the function index.ExtDep
with argument object="pickands"
.
When displaying the bivariate angular density and some data are provided (a 2-column matrix is specified for data
), there is the choice to summarise the data using a histogram (style="hist"
) or to display the observations using tick marks (style="ticks"
).
When displaying return levels, there are two possibilities: univariate and bivariate return levels. Since the model dimensions are restricted to a maximum of three, in that case, aunivariate return level corresponds to fixing two components while a bivariate return level fixes only one component. The choice of the fixed component is decided by the position of the NA
value(s) in the Q.fix
argument. If par
is a vector then the corresponding return level(s) are printed. However if par
is a matrix then the return level(s) are evaluated for each value of the parameter vector and the mean, and empirical 95\%
empirical interval are displayed. Typically this is used when posterior samples are available. When par
is a matrix with only two rows, resulting plots may not provide much information.
When contours are displayed, levels are chosen to be the deciles.
Value
A graph depending on argument object
.
Author(s)
Simone Padoan, simone.padoan@unibocconi.it, https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com;
See Also
Examples
data(pollution)
###############################
### Trivariate Husler-Reiss ###
###############################
f.hr <- fExtDep(method="PPP", data=PNS, model="HR", par.start=rep(1,3))
plot_ExtDep(object="angular", model="HR", par=f.hr$par, data=PNS,
labels=c(expression(PM[10]), expression(NO), expression(SO[2])),
cex.lab=2)
plot_ExtDep(object="pickands", model="HR", par=f.hr$par, data=PNS,
labels=c(expression(PM[10]), expression(NO), expression(SO[2])),
cex.lab=2) # Takes time!
###############################
### Bivariate Husler-Reiss ###
###############################
PN <- na.omit(Leeds.frechet[,1:2])
PN <- cbind(PN, rowSums(PN))
PN <- PN[order(PN[,3], decreasing = TRUE)[1:100],]
PN <- PN[,1:2]/PN[,3]
f.hr2 <- fExtDep(method="PPP", data=PN, model = "HR", par.start = 1)
plot_ExtDep(model="HR", par=f.hr2$par, log=FALSE, data=PN, style="hist")
plot_ExtDep(model="HR", par=f.hr2$par, log=FALSE, data=PN, style="ticks")
plot_ExtDep(object="pickands", model="HR", par=f.hr2$par)