Performs the GMWM estimation procedure using a parameter transform and sampling scheme specific to IMUs.
gmwm_imu(model, data, compute.v = "fast", robust = F, eff = 0.6, ...)A ts.model object containing one of the allowed models.
A matrix or data.frame object with only column (e.g. \(N \times 1\)), or a lts object, or a gts object.
A string indicating the type of covariance matrix solver. "fast", "bootstrap", "asymp.diag", "asymp.comp", "fft"
A boolean indicating whether to use the robust computation (TRUE) or not (FALSE).
A double between 0 and 1 that indicates the efficiency.
Other arguments passed to the main gmwm function
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
This version of the gmwm function has customized settings
ideal for modeling with an IMU object. If you seek to model with an Gauss
Markov, GM, object. Please note results depend on the
freq specified in the data construction step within the
imu. If you wish for results to be stable but lose the
ability to interpret with respect to freq, then use
AR1 terms.