https://github.com/smac-group/mgmwm
:chart_with_upwards_trend: :chart_with_downwards_trend: :chart_with_upwards_trend: :chart_with_upwards_trend: :chart_with_downwards_trend: Multisignal GMWM estimation and model selection for IMU
https://github.com/smac-group/mgmwm
estimation gmwm imu inertial-sensors multivariate-timeseries stochastic-processes
Last synced: 8 months ago
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:chart_with_upwards_trend: :chart_with_downwards_trend: :chart_with_upwards_trend: :chart_with_upwards_trend: :chart_with_downwards_trend: Multisignal GMWM estimation and model selection for IMU
- Host: GitHub
- URL: https://github.com/smac-group/mgmwm
- Owner: SMAC-Group
- Created: 2018-04-05T03:47:01.000Z (about 8 years ago)
- Default Branch: master
- Last Pushed: 2021-07-12T18:29:27.000Z (almost 5 years ago)
- Last Synced: 2025-04-22T10:22:59.239Z (about 1 year ago)
- Topics: estimation, gmwm, imu, inertial-sensors, multivariate-timeseries, stochastic-processes
- Language: R
- Homepage: https://smac-group.github.io/mgmwm/
- Size: 5.37 MB
- Stars: 4
- Watchers: 3
- Forks: 6
- Open Issues: 2
-
Metadata Files:
- Readme: README.Rmd
Awesome Lists containing this project
README
---
output: github_document
---
```{r, echo = FALSE, warning=FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
library(mgmwm)
```
[](https://travis-ci.org/SMAC-Group/classimu)
[](http://www.repostatus.org/#active)
[](https://www.gnu.org/licenses/gpl-3.0.en.html)
[](https://cran.r-project.org/)
[](https://cran.r-project.org/package=classimu)
[](commits/develop)
[)`-yellowgreen.svg)](/commits/master)
# `mgmwm` Overview 
Multisignal GMWM (`mgmwm`) R package allows to estimate the parameters from multiple replicates coming from an IMU error signal, apply the near-stationarity test and select the nost approriate model.
To see what `mgmwm` is capable of, please refer to the "Vignettes" tabs above.
## IEEE/ION PLANS Monterey 2018 presentation
You'll find hereunder the presentation of the `mgmwm` package and its related theory:
[A Two-Step Computationally Efficient Procedure for IMU Classification and Calibration](https://smac-group.com/pdfs/ieee_ion_plans_2018_slides.pdf).
## Install Instructions
### Installing the package through GitHub
For users who are interested in having the latest developments, this option is ideal. Though, more dependancies are required to run a stable version of the package. Most importantly, users **must** have a compiler installed on their machine that is compatible with R (e.g. Clang).
*The setup to obtain the development version of `mgmwm` is platform dependent.*
### Requirements and Dependencies
**OS X**
Some users report the need to use X11 to suppress shared library errors. To install X11, visit [xquartz.org](http://www.xquartz.org/).
**Linux**
Both curl and libxml are required.
For **Debian** systems, enter the following in terminal:
```{r, eval = F, engine='bash'}
sudo apt-get install curl libcurl3 libcurl3-dev libxml2 libxml2-dev
```
For **RHEL** systems, enter the following in terminal:
```{r, eval = F, engine='bash'}
sudo yum install curl curl-devel libxml2 libxml2-dev
```
**All Systems**
The following R packages are also required. If you have made it this far, run the following code in an R session and you will be ready to use the devlopment version of `mgmwm`.
```{r, eval = F}
# Install dependencies
install.packages(c("RcppArmadillo","devtools","knitr","rmarkdown", "iterpc", "progress", "shinyjs"))
# Install dependencies from github
devtools::install_github(c("SMAC-Group/simts", "SMAC-Group/wv", "SMAC-Group/gmwm"))
# Install the package from GitHub without Vignettes/User Guides
devtools::install_github("SMAC-Group/mgmwm")
# Install the package with Vignettes/User Guides
devtools::install_github("SMAC-Group/mgmwm", build_vignettes = TRUE)
```
### Package capabilities example
**Create a M-IMU object**
In order to use the `mgmwm` package, one need to create a mimu object through the function `make_mimu`. An example on how to use this function is provided hereunder with simulated data:
```{r, eval = T, warning = F, message = F, fig.width = 8, fig.height = 6}
library(simts)
library(wv)
library(gmwm)
library(mgmwm)
# Set seed for reproducibility
set.seed(2710)
# Define the differente sample size for simulated data
n1 = 10000
n2 = 500000
n3 = 100000
n4 = 50000
# Define the model for simulated data
model1 = AR1(.995, sigma2 = 1e-6) + WN(.005) + RW (1e-7)
model2 = AR1(.990, sigma2 = 1e-6) + WN(.007) + RW (1e-7)
# Generate 4 replicates coming from the above models
Wt = gen_gts(n1, model1)
Xt = gen_gts(n2, model1)
Yt = gen_gts(n3, model2)
Zt = gen_gts(n4, model2)
# Create the mimu object
mimu = make_mimu(Wt ,Xt, Yt, Zt, freq = 100, unit = "s", sensor.name = "Simulated Data sets",
exp.name = c("today", "yesterday", "last friday","last tuesday" ))
# Plot the data at hand
plot(mimu)
```
**Estimates parameters values and plot function**
```{r, eval = T, warning = F, message = F, fig.width = 8, fig.height = 6}
# Specify the model which you want to estimate
model = 3*AR1() + WN() + RW ()
# Estimate the model with the mgmwm function
fit_1 = mgmwm(mimu, model, CI = T)
# Print summary of estimation (Parameters values and respective confidence intervals if computed)
summary(fit_1)
# Plot the Empirical Wavelet Variance with the one Implied by the parameters
plot(fit_1)
# Plot the Empirical Wavelet Variance with the one Implied by the parameters with the contribution
# of each individual processes
plot(fit_1, decomp = T)
```
** Select model and compare the selection criteria **
```{r, eval = T, warning = F, message = F, fig.width = 8, fig.height = 6}
# Compute the Wavelet Variance Information Criterion (WVIC) on all nested models
model_selection_1 = model_selection(mimu, model)
# Plot the selected model Implied WV
plot(model_selection_1)
# Plot the equivalent model WV
plot(model_selection_1, type = "equivalent")
# Plot the value of the WVIC for every nested model with their respective confidence intervals
plot(model_selection_1, type = "wvic_all")
# Plot the value of the WVIC for equivalent model(s) with their respective confidence intervals
plot(model_selection_1, type = "wvic_equivalent")
```
The models in green are the equivalent models with the same or less models complexity, with respect to the number of parameters to estimate.