ENu_graph: Plots empirical expected number of upcrossings of level u...

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

Plots empirical expected number of upcrossings of level u with respect to P(Y<u)

Usage

ENu_graph(data, u, lty = 1, col = 1, add = FALSE, CI = FALSE, alpha = 0.05,
 N.s.data = NULL, xlab = "P(Y<u)", 
 ylab = "Intensity of upcrossings", ylim = NULL)

Arguments

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 1-alpha 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.

Value

list including

u	sequence of levels
F	empirical cdf: P(data<u)
Nu	number of upcrossings
CI.	fluctuation interval

Author(s)

Valerie Monbet, [e-mail]

See Also

valid_all

Examples

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])

NHMSAR documentation built on Feb. 9, 2022, 9:06 a.m.