Description Usage Arguments Details Value Author(s) References See Also Examples
View source: R/Srho.ts_files.R
Entropy test of nonlinearity for time series based
on Srho.ts
and surrogate data obtained through the sieve bootstrap.
1 2 3 4 
x, y 
univariate numeric time series object or numeric vectors ( 
lag.max 
maximum lag at which to calculate Srho; 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 
order.max 
see 
fit.method 
see 
smoothed 
logical. If 
For each lag from 1 to lag.max
Srho.test.AR
computes a test for nonlinearity for time series based
on Srho.ts
. The distribution under the null hypothesis of linearity is obtained through the sieve bootstrap.
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 Srho is computed. 
stationary 
Object of class 
data.type 
Object of class 
notes 
Object of class 
Simone Giannerini<simone.giannerini@unibo.it>
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.ARs
, surrogate.AR
. See Srho.test.AR.p
for the parallel version.
1 2 3 4 5 6 7 8 9  ## Not run:
## ************************************************************
## WARNING: computationally intensive, increase B with caution
## ************************************************************
set.seed(13)
x < arima.sim(n=120, model = list(ar=0.8));
result < Srho.test.AR(x, lag.max = 5, B = 10, bw='reference', method='integral')
## End(Not run)

Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.