gmwm_imu {simts}

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:

• estimateEstimated Parameters Values from the GMWM Procedure

• init.guessInitial Starting Values given to the Optimization Algorithm

• wv.empirThe data's empirical wavelet variance

• ci_lowLower Confidence Interval

• ci_highUpper Confidence Interval

• orgVOriginal V matrix

• VUpdated V matrix (if bootstrapped)

• omegaThe V matrix inversed

• obj.funValue of the objective function at Estimated Parameter Values

• theoSummed Theoretical Wavelet Variance

• decomp.theoDecomposed Theoretical Wavelet Variance by Process

• scalesScales of the GMWM Object

• robustIndicates if parameter estimation was done under robust or classical

• effLevel of efficiency of robust estimation

• model.typeModels being guessed

• compute.vType of V matrix computation

• augmentedIndicates moments have been augmented

• alphaAlpha level used to generate confidence intervals

• expect.diffMean of the First Difference of the Signal

• NLength of the Signal

• GNumber of Guesses Performed

• HNumber of Bootstrap replications

• KNumber of V matrix bootstraps

• modelts.model supplied to gmwm

• model.hatA new value of ts.model object supplied to gmwm

• startingIndicates whether the procedure used the initial guessing approach

• seedRandomization seed used to generate the guessing values

• freqFrequency of data