Plots empirical expected number of upcrossings of level u with respect to P(Y<u)
1 2 3 
data 
array of univariate or multivariate series with dimension T*N.samples*d. T: number of time steps of each sample, N.samples: number of realisations of the same stationary process, d: dimension. 
u 
sequence of levels to be considered 
lty 
type of line 
col 
color of line 
add 
if add=TRUE lines is added to current plot 
CI 
if CI=TRUE a fluctuation interval at 1alpha level of confidence is computed and plotted 
alpha 
confidence level 
N.s.data 

xlab 
a title for the x axis 
ylab 
a title for the y axis 
ylim 
numeric vectors of length 2, giving the y coordinates ranges. 
list including
u 
sequence of levels 
F 
empirical cdf: P(data<u) 
Nu 
number of upcrossings 
CI. 
fluctuation interval 
Valerie Monbet, valerie.monbet@univrennes1.fr
valid_all
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21  data(meteo.data)
data = array(meteo.data$temperature,c(31,41,1))
T = dim(data)[1]
N.samples = dim(data)[2]
d = dim(data)[3]
M = 2
order = 1
theta.init = init.theta.MSAR(data,M=M,order=order,label="HH")
mod.hh= NULL
mod.hh$theta = theta.init
mod.hh$theta$A = matrix(c(0.40,0.88,.09,.13),2,2)
mod.hh$theta$A0 = matrix(c(6.75,1.08),2,1)
mod.hh$theta$sigma = matrix(c(1.76,3.40),2,1)
mod.hh$theta$prior = matrix(c(0.37,0.63),2,1)
mod.hh$theta$transmat = matrix(c(0.82,0.09,0.18,0.91),2,2)
#B.sim = 100*N.samples
#Y0 = array(data[1:2,sample(1:dim(data)[2],B.sim,replace=TRUE),],c(2,B.sim,1))
#Y.sim = simule.nh.MSAR(mod.hh$theta,Y0=Y0,T,N.samples=B.sim)
u = seq(min(data),max(data),by=.3)
gr.d = ENu_graph(data,u)
#gr = ENu_graph(Y.sim$Y,u,col=2,add=TRUE,CI = TRUE,N.s.data=dim(data)[2])

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