
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
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
Vector of the occurrence points in
Vector of the occurrence points in
Input argument.
Input argument.
Input argument.
A bivariate CPSP
The points in the marginal 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)
# }
Run the code above in your browser using DataLab