autoGPoMoTest | R Documentation |

Tests the numerical integrability of
provided models (these may have been obtained with
function `autoGPoMoSearch`

),
and classify these models as Divergent, Fixed Points,
Periodic or not Unclassified (potentially chaotic).

```
autoGPoMoTest(
data,
nVar,
dMax,
dMin = 0,
tin = NULL,
dt = NULL,
show = 1,
verbose = 1,
allKL = allKL,
numValidIC = 1,
weight = NULL,
IstepMin = 10,
IstepMax = 10000,
tooFarThr = 4,
FxPtThr = 1e-08,
LimCyclThr = 1e-06,
method = "rk4"
)
```

`data` |
Input Time series: Each column is one time series that corresponds to one variable. |

`nVar` |
Number of variables considered in the polynomial formulation. |

`dMax` |
Maximum degree of the polynomial formulation. |

`dMin` |
The minimum negative degree of the polynomial formulation (0 by default). |

`tin` |
Input date vector which length should correspond to the input time series. |

`dt` |
Sampling time of the input time series. |

`show` |
Provide (2) or not (0-1) visual output during the running process. |

`verbose` |
Gives information (if set to 1) about the algorithm progress and keeps silent if set to 0. |

`allKL` |
A list of all the models |

`numValidIC` |
Line number of the first valid initial conditions, that is, such as weight is not equal to zero. |

`weight` |
A vector providing the binary weighting function of the input data series (0 or 1). By default, all the values are set to 1. |

`IstepMin` |
The minimum number of integration step to start
of the analysis (by default |

`IstepMax` |
The maximum number of integration steps for
stopping the analysis (by default |

`tooFarThr` |
Divergence threshold, maximum value of the model trajectory compared to the data standard deviation. By default a trjactory is too far if the distance to the center is larger than four times the variance of the input data. |

`FxPtThr` |
Threshold used to detect fixed points. |

`LimCyclThr` |
Threshold used to detect the limit cycle. |

`method` |
The integration technique used for the numerical
integration. By default, the fourth-order Runge-Kutta method
( |

A list containing:

`$okMod`

A vector classifying the models: diverging models (0),
periodic models of period-1 (-1), unclassified models (1).

`$okMod`

A matrix classifying the model variables: diverging variable (0),
period-1 variable (-1), period-2 variable (-2), fixed point variable (2), unclassified models (1).

`$coeff`

A matrix with the coefficients of one selected model

`$models`

A list of all the models to be tested `$mToTest1`

,
`$mToTest2`

, etc. and of all selected models `$model1`

, `$model2`

, etc.

`$tout`

The time vector of the output time series (vector length
corresponding to the longest numerical integration duration)

`$stockoutreg`

A list of matrices with the integrated trajectories
(variable `X1`

in column 1, `X2`

in 2, etc.) for all the models
`$model1`

, `$model2`

, etc.

Sylvain Mangiarotti, Flavie Le Jean

`autoGPoMoSearch`

, `gPoMo`

, `poLabs`

```
#Example
# Load data:
data('RosYco')
# Structure choice
data('allToTest')
# Test the models
outGPT <- autoGPoMoTest(RosYco, nVar= 3, dMax = 2, dt = 1/125, show=1,
allKL = allToTest, IstepMax = 60)
```

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