gmwm_imu: GMWM for (Robust) Inertial Measurement Units (IMUs)

In simts: Time Series Analysis Tools

GMWM for (Robust) Inertial Measurement Units (IMUs)

Description

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

Usage

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

Arguments

model	A ts.model object containing one of the allowed models.
data	A matrix or data.frame object with only column (e.g. N \times 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

Details

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.

Value

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

simts documentation built on Nov. 5, 2025, 7:17 p.m.