auto_imu: Automatically select appropriate model for IMU

In schoi355/gmwm: Generalized Method of Wavelet Moments

Automatically select appropriate model for IMU

Description

Runs through a model selection algorithm to determine the best model

Usage

auto_imu(data, model = 3 * AR1() + WN() + RW() + QN() + DR(),
  bootstrap = F, alpha = 0.05, robust = F, eff = 0.6, B = 50,
  G = 1e+06, seed = 1337)

Arguments

data	A vector, matrix, data.frame, or imu object with either 1, 3, or 6 columns.
model	A ts.model object that is the largest model to be tested.
bootstrap	A bool that is either true or false to indicate whether we use bootstrap or asymptotic By default, we use asymptotic.
alpha	A double that indicates the level of confidence for the WV CI.
robust	A boolean that indicates whether to use robust estimation.
eff	A double between 0 and 1 that indicates the efficiency for the robust estimation.
B	A integer that contains the amount of bootstrap replications
G	A integer that indicates the amount of guesses for caliberating the startup.
seed	A integer that controls the reproducibility of the auto model selection phase.

Details

The auto.imu object stores two important features for each signal:

• [[1]]A matrix containing model output

• [[2]]The best gmwm object.

To access it for each signal use: object[[i]][[1]] or object[[i]][[2]], where i denotes the signal.

Value

A auto.imu object.

Author(s)

JJB

Examples

## Not run: 
if(!require("imudata")){
install_imudata()
library("imudata")
}

data(imu6)

# Example 1
test1 = imu(imu6, gyros = 1:3, accels = NULL, axis = c('X', 'Y', 'Z'), freq = 100)

m = auto_imu(test1)

# Process 1's model table
m[[1]][[1]]

# Process 1's best fitting gmwm object
m[[1]][[2]]


## End(Not run)

schoi355/gmwm documentation built on April 11, 2022, 1:21 a.m.