- ar
Any autoregressive terms to go directly to the
arima.sim
function. Leave as it is if you wish to
simulate white noise.
- ma
As ar
but for moving average terms.
- npow
The number of realizations to carry out. The best
assessments are carried out with high values of npow
,
e.g. 1000 or even 10000.
- nom.size
The nominal statistical size of the test. Note: this
does not change the nomimal size for ALL tests. You need to check
each help pages for each function to check what can be changed.
- ndata
The length of the white noise or ARMA realizations.
Power for both these tests depends on sample size.
- lapplyfn
If you have the library parallel
and a suitable
multicore machine then this function can run the realizations
in parallel. If so, then you can change this argument to
lapplyfn=mclapply
to take advantage of this.
- Box.lag
The Box test tests for white noise by examining
autocorrelation coefficients. This argument specifies the max number
of autocorrelation coefficients, ie. coefficients from lag 1 up
to Box.lag
.
- rand.gen
Alternative innovation generator. By default Gaussian
innovations are used, but you can specify alternatives to get
heavy-tailed innovations, for example.
- hwwn
If TRUE then the hwwn.test
will be evaluated,
if FALSE then it won't be.
- box
If TRUE then the Box.test
will be evaluated,
if FALSE then it won't be.
- bartlett
If TRUE then the bartlettB.test
will be evaluated,
if FALSE then it won't be.
- d00test
If TRUE then the d00.test
will be evaluated, if FALSE then it won't be.
- genwwn
If TRUE then the genwwn.test
will be evaluated, if FALSE then it won't be.
- hywn
If TRUE then the hywn.test
will be evaluated, if FALSE then it won't be.
- hywavwn
If TRUE then the hywavwn.test
will be evaluated, if FALSE then it won't be.
- filter.number
The number of vanishing moments of wavelets
used in the general wavelet tests (genwwn, hywn and hywavwn).
- family
Wavelet family, as for filter.number
argument.
- away.from
The number of finer scales not to use for the
general wavelet tests. These tests work by relying on the
asymptotic normality of wavelet coefficients, but this only
becomes useful away from the finer scales. This argument
can be an integer in which case it defines the number of fine
scales to ignore. Alternatively, you can supply the argument
"standard"
which chooses an automatically selected number
of scales to stay away from which works well up to time series
in length of 1000. Better performance can be obtained for
series longer than 1000 by adapting the away.from
argument.
- ...
Other arguments to hwwn.test
.