DCG (version 0.9.3)

temperatureSample: generate temperatures temperatureSample generate tempatures based on either random or fixed intervals

Description

generate temperatures temperatureSample generate tempatures based on either random or fixed intervals

Usage

temperatureSample(start = 0.01, end = 20, n = 20,
  method = "random")

Arguments

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.

Value

a numeric vector of length n representing temperatures sampled.

Details

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.

References

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.

See Also

getEnsList

Examples

# NOT RUN {
symmetricMatrix <- as.symmetricAdjacencyMatrix(monkeyGrooming, weighted = TRUE, rule = "weak")
Sim <- as.SimilarityMatrix(symmetricMatrix)
temperatures <- temperatureSample(start = 0.01, end = 20, n = 20, method = 'random')
# }