randlsp | R Documentation |

randlsp is used to obtain robust p-values for the significance of the largest peak in a Lomb-Scargle periodogram by randomisation. The data sequence is scrambled repeatedly and the probability of random peaks reaching or exceeding the peak in the original (unscrambled) periodogram is computed.

```
randlsp(repeats=1000,x, times = NULL, from = NULL, to = NULL,
type = c("frequency", "period"), ofac = 1, alpha = 0.01,
plot = TRUE, trace = TRUE, ...)
```

`repeats` |
An integer determining the number of repeated randomisations. Large numbers (>=1000) are better but can make the procedure time-consuming. |

`x` |
The data to be analysed. x can be either a two-column numerical dataframe or matrix, with sampling times in columnn 1 and measurements in column 2, a single numerical vector containing measurements, or a single vector |

`times` |
If x is a single vector, times can be provided as a numerical vector of equal length containing sampling times. If x is a vector and times is NULL, the data are assumed to be equally sampled and times is set to 1:length(x). |

`from` |
The starting frequency (or period, depending on type) to begin scanning for periodic components. |

`to` |
The highest frequency (or period, depending on type) to scan. |

`type` |
Either “frequency” (the default) or “period”. Determines the type of the periodogram x-axis. |

`ofac` |
The oversampling factor. Must be an integer >=1. Larger values of ofac lead to finer scanning of frequencies but may be time-consuming for large datasets and/or large frequency ranges (from...to). |

`alpha` |
The significance level. The periodogram plot shows a horizontal dashed line. Periodogram peaks exceeding this line can be considered significant at alpha. Defaults to 0.01. Only used if plot=TRUE. |

`plot` |
Logical. If TRUE, two plots are displayed (i) The periodogram of the original (unscrambled) data (ii) A histogram of peaks occurring by chance during sequence randomisation. A vertical line is drawn at the height of the peak in a periodogram of the original data. |

`trace` |
Logical. If TRUE, information about the progress of the randomisation procedure is printed during the running of randlsp. |

`...` |
Additional graphical parameters affecting the histogram plot. |

Function randlsp preserves the actual measurement intervals, which may affect the periodogram (see Nemec & Nemec 1985, below). Hence, this is a conservative randomisation procedure.

P-values from both randlsp and `lsp`

increase with the number of frequencies inspected. Therefore, if the frequency-range of interest can be narrowed down *a priori*, use arguments “from” and “to” to do so.

A named list with the following items:

`scanned` |
A vector containing the frequencies/periods scanned. |

`power` |
A vector containing the normalised power corresponding to scanned frequencies/periods. |

`data` |
Names of the data vectors analysed. |

`n` |
The length of the data vector. |

`type` |
The periodogram type used, either “frequency” or “period”. |

`ofac` |
The oversampling factor used. |

`n.out` |
The length of the output (powers). This can be >n if ofac >1. |

`peak` |
The maximum power in the frequency/period interval inspected. |

`peak.at` |
The frequency/period at which the maximum peak occurred. |

`random.peaks` |
A vector of peaks (with length=repeats) of maximum power values computed from randomised data. |

`repeats` |
The number of randomisations. |

`p.value` |
The probability that the peak in the original data occurred by chance, computed from randomising the data sequence. |

Thomas Ruf thomas.p.ruf@me.com

Nemec A.F.L, Nemec J.M. (1985) A test of significance for periods derived using phase-dispersion-miminimization techniques. *The Astronomical Journal* **90**:2317–2320

`lsp`

```
data(lynx)
set.seed(444)
rand.times <- sample(1:length(lynx),30) # select a random vector of sampling times
randlsp(repeats=1000,lynx[rand.times],times=rand.times)
```

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