It calculates the occurrence points in the three indicator processes of a bivariate Common Poisson Shock Process (CPSP), using as input the two marginal processes \(N_1\) and \(N_2\).
CPSPpoints(N1,N2,date=NULL, dplot=T, pmfrow=c(2,1), axispoints=NULL,...)Binary vector of the first CPSP marginal process; occurrence points must be marked with 1 and the other with 0.
Binary vector of the second CPSP marginal process; occurrence points must be marked with 1 and the other with 0.
Optional. A vector or matrix indicating the date of each observation.
Optional. A logical flag. If it is TRUE, the marginal and indicator processes are plotted.
Optional. A vector of the form (nr, nc) to be supplied as value of the argument mfrow in
par.
Optional. Numeric vector with the points in the time index where axis ticks and labels (from the first column in date)
have to be drawn.
Further arguments to be passed to the function plot.
A list with components
Vector of the occurrence points in \(N_{(1)}\).
Vector of the occurrence points in \(N_{(2)}\).
Vector of the occurrence points in \(N_{(12)}\).
Input argument.
Input argument.
Input argument.
A bivariate CPSP \(N\) is usually specified by its two marginal, and possibly dependent, processes \(N_1\) and \(N_2\), which are the observed processes. However, \(N\) can be decomposed into three independent indicator processes: \(N_{(1)}\), \(N_{(2)}\) and \(N_{(12)}\), which are the processes of the points occurring only in the first marginal process, only in the second and in both of them (simultaneous points). The union of \(N_{(1)}\) and \(N_{(12)}\), and \(N_{(2)}\) and \(N_{(12)}\) gives respectively the two marginal processes.
The points in the marginal \(N_{1}\), \(N_{2}\) and indicator
\(N_{(1)}\), \(N_{(2)}\) and \(N_{(12)}\) processes can be optionally plotted.
If date is NULL, default axis are used. Otherwise, the values in axispoints are used
as the points in the time index where axis ticks and labels, from the first column in date,
have to be drawn. If axispoints is NULL, a default grid of points is built using the
function marca.
Abaurrea, J. Asin, J. and Cebrian, A.C. (2015). A Bootstrap Test of Independence Between Three Temporal Nonhomogeneous Poisson Processes and its Application to Heat Wave Modeling. Environmental and Ecological Statistics, 22(1), 127-144.
# NOT RUN {
set.seed(123)
X<-as.numeric(runif(100)<0.10)
set.seed(124)
Y<-as.numeric(runif(100)<0.15)
aux<-CPSPpoints(N1=X,N2=Y)
# }
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