Description Usage Arguments Details Value References Examples

`generateFMM()`

simulates data from a FMM model defined by parameters `M`

, `A`

, *α*, *β* and *ω*.

1 2 3 |

`M` |
A numeric vector which contains the value of the intercept parameter |

`A` |
A positive numeric vector which contains the value of the FMM wave amplitude parameter |

`alpha` |
A numeric vector which contains the value of the FMM wave phase translation parameter |

`beta` |
A numeric vector which contains the value of the FMM wave skewness parameter |

`omega` |
A numeric vector which contains the value of the FMM wave kurtosis parameter |

`from` |
A numeric value which contains the initial time point of the simulated data. By default, it is 0. |

`to` |
A numeric value which contains the final time point of the simulated data. By default, it is 2*pi. |

`length.out` |
A non-negative number wich contains the desired length of the simulation. By default, it is 100. |

`timePoints` |
A numeric vector which contains the time points at which the data will be simulated.
By default, it is sequence of equally spaced values from |

`plot` |
A logical value indicating whether the simulated data should be drawn on a plot.
By default, it is |

`outvalues` |
A logical value indicating whether the numerical simulation should be return. By default, it is |

`sigmaNoise` |
A non-negative number which contains the standard deviation of the gaussian noise to be added. Its default value is zero equivalent to a simulation set-up without noise. |

To simulate a multicomponent FMM model, arguments `A`

, `alpha`

, `beta`

and `omega`

are vectors of length *m*, where *m* represents the number of FMM waves. With different lengths, the smaller vectors will be replicate until thay are the same length as the longest vector.

With `sigmaNoise = s`

, `s>0`

, the `generateFMM`

function uses `rnorm(length.out, 0, sigmaNoise)`

to create the normally distributed noise and adds it to the simulated values.

When `outvalues = TRUE`

a list of with the following components is returned:

`input` |
a list with the input parameters |

`t` |
a numeric vector with the time points at each data is simulated. |

`y` |
a numeric vector with the data simulated. |

When `plot = TRUE`

a scatter plot of `y`

vs `t`

is drawn.

Rueda C, Larriba Y, Peddada SD (2019).
Frequency Modulated Moebius Model Accurately Predicts Rhythmic Signals in Biological and Physical Sciences.
*Scientific reports*, **9** (1), 18701. https://www.nature.com/articles/s41598-019-54569-1

1 2 3 4 5 6 7 8 9 10 | ```
# Simulate data from a monocomponent FMM model. A plot with the simulated model is shown
generateFMM(M = 2,A = 3,alpha = 1.5,beta = 2.3, omega = 0.1, outvalues = FALSE)
# Add a gaussian noise with standard deviation 0.3. The numeric results are returned
generateFMM(M = 2, A = 3, alpha = 1.5, beta = 2.3, omega = 0.1,
sigmaNoise = 0.3, plot = FALSE, outvalues = TRUE)
# Simulate data from a multicomponent FMM model with two FMM waves
# both with amplitude parameter = 2
generateFMM(M = 0, A = 2, alpha = c(1.5, 3.4), beta = c(0.2, 2.3), omega = c(0.1, 0.2))
``` |

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