valid_all.MSAR: Statistics plotting for validation of MSAR models

In NHMSAR: Non-Homogeneous Markov Switching Autoregressive Models

Description

plots some functional statistics to help to valid MSAR models: qqplot, covariance function, mean duration of sojourn over and under a threshold. For each of them the empirical statistic of the observed time series is plotted as well as the simulated one with (1-α)-fluctuation intervals.

Usage

valid_all.MSAR(data,simu,title="",id=1,alpha=.05,spaghetti=TRUE,
mfrow=NULL,save=FALSE,output=FALSE,
root.filename=" ",path=NULL,col="red",width=4,height=4)

Arguments

data	observed (or reference) time series, array of dimension T*N.samples*d
simu	simulated time series, array of dimension T*N.sim*d. N.sim have to be K*N.samples with K large enough (for instance, K=100)
title	title of plots
id	component to be considered when the data is multivariate (d>1). Default d=1.
alpha	level for the (1-α)-fluctuation intervals
spaghetti	statistics of every simulation batch are plotted instead of fluctuation intervals. A batch is a simulation block of the same size as the observations. Default spaghetti=TRUE
mfrow	if NULL, each plot is done in a new window
save	if save=TRUE plots are saved into .eps files
root.filename	root file name for saving plots
path	path of folder where to save the files
output	if TRUE some statistics are returned.
col	color of the lines for simulated data, default is red
width	width of the figure when is it save by dev.copy2eps
height	height of the figure when is it save by dev.copy2eps

Value

Returns plots and

qqp	statistics of marginal distributions
C	statistics of correlation functions
ENu.data	statistics of intensity of up crossings of the data
ENu.simu	statistics of intensity of up crossings of the simulations
MDO	statistics of mean duration over a level
MDU	statistics of mean duration under a level

Author(s)

Valerie Monbet, [e-mail]

Examples

data(meteo.data)
data = array(meteo.data$temperature,c(31,41,1)) 
k = 40
plot(data[,k,1],typ="l",xlab=("time (days)"),ylab=("temperature (degrees C)"))
T = dim(data)[1]
N.samples = dim(data)[2]
d = dim(data)[3]
# Fit Homogeneous MS-AR models
M = 2
order = 1
theta.init = init.theta.MSAR(data,M=M,order=order,label="HH") 
mod.hh = fit.MSAR(data,theta.init,verbose=TRUE,MaxIter=10)
# Simulation
yT = 31
Bsim = 10
Ksim = Bsim*N.samples
Y0 = array(data[1:2,sample(1:dim(data)[2],Ksim,replace=T),],c(2,Ksim,1))
Y.sim = simule.nh.MSAR(mod.hh$theta,Y0 = Y0,T,N.samples = Ksim) 
valid_all.MSAR(data,Y.sim$Y)

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