gmwm_imu: GMWM for (Robust) Inertial Measurement Units (IMUs)
In SMAC-Group/gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Examples

Performs the GMWM estimation procedure using a parameter transform and sampling scheme specific to IMUs.

1	gmwm_imu(model, data, compute.v = "fast", robust = F, eff = 0.6, ...)

`model`	A `ts.model` object containing one of the allowed models.
`data`	A `matrix` or `data.frame` object with only column (e.g. N x 1 ), or a `lts` object, or a `gts` object.
`compute.v`	A `string` indicating the type of covariance matrix solver. "fast", "bootstrap", "asymp.diag", "asymp.comp", "fft"
`robust`	A `boolean` indicating whether to use the robust computation (TRUE) or not (FALSE).
`eff`	A `double` between 0 and 1 that indicates the efficiency.
`...`	Other arguments passed to the main `gmwm` function

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.

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

## Not run: 
# Example data generation
data = gen_gts(10000, GM(beta = 0.25, sigma2_gm = 1), freq = 5)
results = gmwm_imu(GM(),data)
inference = summary(results)

## End(Not run)