Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/Srho.ts.par_files.R
Entropy test of nonlinearity for time series based
on Srho.ts
and surrogate data obtained through
Simulated Annealing. Parallel version – requires parallel.
1 2 3 4  Trho.test.SA.p(x, y, lag.max = 10, B = 100, plot = TRUE, quant = c(0.95, 0.99),
bw = c("reference","mlcv", "lscv", "scv", "pi"), method =c("integral","summation"),
maxpts=0, tol=1e03, nlag=trunc(length(x)/4), Te=0.0015,
RT=0.9, eps.SA=0.01, nsuccmax=30, nmax=300, che=100000, nslaves=detectCores())

x, y 
univariate numeric time series object or numeric vectors ( 
lag.max 
maximum lag at which to calculate Trho; default is 
B 
number of surrogate time series. 
plot 
logical. If 
quant 
quantiles to be specified for the computation of the significant lags and the plot of confidence bands. Up to 2 quantiles can be specified. Defaults are 95% and 99%. 
bw 
see 
method 
see 
maxpts 
see 
tol 
see 
nlag 
see 
Te 
see 
RT 
see 
eps.SA 
see 
nsuccmax 
see 
nmax 
see 
che 
see 
nslaves 
number of slaves/processes to be used in parallel environments. 
For each lag from 1 to lag.max
Trho.test.SA
computes a test for nonlinearity for time series based
on Srho.ts
. The distribution under the null hypothesis of a linear Gaussian process is obtained through a generalization of surrogate data
methods. Surrogate time series are obtained through Simulated Annealing (SA). Sensible (Ndependent) defaults are derived for the parameters of the SA
algorithm, there should not be the need to change them. The routine requires the package parallel to spawn multiple slaves.
An object of class "Srho.test", which is a list with the following elements:
.Data 
vector of 

Object of class 

Object of class 
quantiles 
Object of class 

Object of class 
significant.lags 
Object of class 
p.value 
Object of class 
lags 
integer vector that contains the lags at which Trho is computed. 
stationary 
Object of class 
data.type 
Object of class 
notes 
Object of class 
Simone Giannerini<[email protected]>
Giannerini S., Maasoumi E., Bee Dagum E., (2015), Entropy testing for nonlinear serial dependence in time series, Biometrika, 102(3), 661–675 http://doi.org/10.1093/biomet/asv007.
See Also Srho.ts
, surrogate.SA
, Trho.test.SA
.
1 2 3 4 5 6 7 8 9 10 11  ## Not run:
# modifiy nslaves to match the number of available cores
set.seed(13)
x < arima.sim(n=120, model = list(ar=0.8));
result < Trho.test.SA.p(x, lag.max = 5, B = 100, bw='reference', nslaves=2)
## ** Compare timings **
system.time(Trho.test.SA.p(x, lag.max = 5, B = 100, bw='reference', nslaves=4))
system.time(Trho.test.SA(x, lag.max = 5, B = 100, bw='reference'))
## End(Not run)

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