auto.imu: Automatically select appropriate model for IMU
In gmwm: Generalized Method of Wavelet Moments

Description Usage Arguments Details Value Author(s) Examples

Runs through a model selection algorithm to determine the best model

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auto.imu(data, model = 3 * AR1() + WN() + RW() + QN() + DR(), bootstrap = F,
  alpha = 0.05, robust = F, eff = 0.6, B = 50, G = 1e+05,
  seed = 1337)

`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.

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.

A auto.imu object.

JJB

## 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)