Description Usage Arguments Details Value References See Also Examples

generate temperatures
`temperatureSample`

generate tempatures based on either random or fixed intervals

1 2 | ```
temperatureSample(start = 0.01, end = 20, n = 20,
method = "random")
``` |

`start` |
a numeric vector of length 1, indicating the lowest temperature |

`end` |
a numeric vector of length 1, indicating the highest temperature |

`n` |
an integer between 10 to 30, indicating the number of temperatures (more explanations on what temperatures are). |

`method` |
a character vector indicating the method used in selecting temperatures. It should take either 'random' or 'fixedInterval', case-sensitive. |

In using random walks to find community structure, each normalized similarity matrix is evaluated at different temperatures. This allows greater variations in the normalized similarity matrices. It is recommended to try out 20 - 30 temperatures to allow for a thorough exploration of the matrices. A range of temperatures which lead to stable community structures should be considered as reliable. The temperature in the middle of the range should be selected.

a numeric vector of length n representing temperatures sampled.

Fushing, H., & McAssey, M. P. (2010). Time, temperature, and data cloud geometry. Physical Review E, 82(6), 061110.

Chen, C., & Fushing, H. (2012). Multiscale community geometry in a network and its application. Physical Review E, 86(4), 041120.

Fushing, H., Wang, H., VanderWaal, K., McCowan, B., & Koehl, P. (2013). Multi-scale clustering by building a robust and self correcting ultrametric topology on data points. PloS one, 8(2), e56259.

1 2 3 | ```
symmetricMatrix <- as.symmetricAdjacencyMatrix(monkeyGrooming, weighted = TRUE, rule = "weak")
Sim <- as.SimilarityMatrix(symmetricMatrix)
temperatures <- temperatureSample(start = 0.01, end = 20, n = 20, method = 'random')
``` |

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