update.gmwm: Update (Robust) GMWM object for IMU or SSM
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Examples

Provides a way to estimate different models over the previously estimated wavelet variance values and covariance matrix.

1 2	## S3 method for class 'gmwm' update(object, model, ...)

`object`	A `gmwm` object.
`model`	A `ts.model` object containing one of the allowed models
`...`	Additional parameters (not used)

The motive behind this function is to allow for reuse of the gmwm object's computation of the wavelet variance and covariance matrix. The function permits this by allowing for the underlying time series model to be changed at will. As a result, the function is particular useful for working with large time series objects. Alternatively, one can also use this function to supply a custom diagonal weighting matrix by modifying the gmwm object.

A gmwm object with the structure:

estimate: Estimated Parameters Values from the GMWM Procedure
init.guess: Initial Starting Values given to the Optimization Algorithm
wv.empir: The data's empirical wavelet variance
ci.low: Lower Confidence Interval
ci.high: Upper Confidence Interval
orgV: Original V matrix
V: Updated V matrix (if bootstrapped)
omega: The V matrix inversed
obj.fun: Value of the objective function at Estimated Parameter Values
theo: Summed Theoretical Wavelet Variance
decomp.theo: Decomposed Theoretical Wavelet Variance by Process
scales: Scales of the GMWM Object
robust: Indicates if parameter estimation was done under robust or classical
eff: Level of efficiency of robust estimation
model.type: Models being guessed
compute.v: Type of V matrix computation
augmented: Indicates moments have been augmented
alpha: Alpha level used to generate confidence intervals
expect.diff: Mean of the First Difference of the Signal
N: Length of the Signal
G: Number of Guesses Performed
H: Number of Bootstrap replications
K: Number of V matrix bootstraps
model: ts.model supplied to gmwm
model.hat: A new value of ts.model object supplied to gmwm
starting: Indicates whether the procedure used the initial guessing approach
seed: Randomization seed used to generate the guessing values
freq: Frequency of data

# AR
set.seed(1336)
n = 200
exact.model = AR1(phi=.99, sigma2 = 0.01) + WN(sigma2=1)
data = gen_gts(n, exact.model)

# Create an initial model that is not accurate
bad.model = gmwm(AR1(), data = data)

# Models can contain specific parameters e.g.
updated.model = update(bad.model, exact.model)

# Or...
updated.model.guided = update(bad.model, AR1()+AR1())