SARMA: Create a Seasonal Autoregressive Moving Average (SARMA)...
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Author(s) Examples

Sets up the necessary backend for the SARMA process.

1	SARMA(ar = 1, ma = 1, sar = 1, sma = 1, s = 12, sigma2 = 1)

`ar`	A `vector` or `integer` containing either the coefficients for phi's or the process number p for the Autoregressive (AR) term.
`ma`	A `vector` or `integer` containing either the coefficients for theta's or the process number q for the Moving Average (MA) term.
`sar`	A `vector` or `integer` containing either the coefficients for PHI's or the process number P for the Seasonal Autoregressive (SAR) term.
`sma`	A `vector` or `integer` containing either the coefficients for THETA's or the process number Q for the Seasonal Moving Average (SMA) term.
`s`	A `integer` indicating the seasonal value of the data.
`sigma2`	A `double` value for the standard deviation, sigma, of the SARMA process.

A variance is required since the model generation statements utilize randomization functions expecting a variance instead of a standard deviation unlike R.

An S3 object with called ts.model with the following structure:

process.desc: AR x p, MA x q, SAR x P, SMA x Q
theta: sigma
plength: Number of Parameters
obj.desc: y desc replicated x times
obj: Depth of Parameters e.g. list(c(length(ar), length(ma), length(sar), length(sma), 1) )
starting: Guess Starting values? TRUE or FALSE (e.g. specified value)

JJB

# Create an SARMA(1,2)x(1,1) process
SARMA(ar = 1, ma = 2,sar = 1, sma =1)

# Creates an SARMA(1,1)x(1,1) process with predefined coefficients.
SARMA(ar=0.23, ma=0.4, sar = .3, sma = .3)