Provides a way to estimate different models over the previously estimated wavelet variance values and covariance matrix.
# S3 method for gmwm
update(object, model, ...)A gmwm object.
A ts.model object containing one of the allowed models
Additional parameters (not used)
A gmwm object with the structure:
Estimated Parameters Values from the GMWM Procedure
Initial Starting Values given to the Optimization Algorithm
The data's empirical wavelet variance
Lower Confidence Interval
Upper Confidence Interval
Original V matrix
Updated V matrix (if bootstrapped)
The V matrix inversed
Value of the objective function at Estimated Parameter Values
Summed Theoretical Wavelet Variance
Decomposed Theoretical Wavelet Variance by Process
Scales of the GMWM Object
Indicates if parameter estimation was done under robust or classical
Level of efficiency of robust estimation
Models being guessed
Type of V matrix computation
Indicates moments have been augmented
Alpha level used to generate confidence intervals
Mean of the First Difference of the Signal
Length of the Signal
Number of Guesses Performed
Number of Bootstrap replications
Number of V matrix bootstraps
ts.model supplied to gmwm
A new value of ts.model object supplied to gmwm
Indicates whether the procedure used the initial guessing approach
Randomization seed used to generate the guessing values
Frequency of data