plotCLUSTERS {DCG}

generate tree plots for each ensemble matrix plotCLUSTERS plot all cluster trees

Description

generate tree plots for each ensemble matrix plotCLUSTERS plot all cluster trees

Usage

plotCLUSTERS(EnsList, mfrow, mar = c(1, 1, 1, 1), line = -1.5,
  cex = 0.5, ...)

Arguments

EnsList

a list in which elements are ensemble matrices.

mfrow

A vector of the form c(nr, nc) passed to par.

mar

plotting parameters with useful defaults (par)

line

plotting parameters with useful defaults (par)

cex

plotting parameters with useful defaults (par)

...

further plotting parameters

Details

plotCLUSTERS plots all cluster trees with each tree corresponding to each ensemble matrix in the list of ens_list. EnsList is the output from getEnsList.

mfrow determines the arrangement of multiple plots. It takes the form of c(nr, nc) with the first parameter being the number of rows and the second parameter being the number of columns. When deciding parameters for mfrow, one should take into considerations size of the plotting device and number of cluster plots. For example, there are 20 cluster plots, mfrow can be set to c(4, 5) or c(2, 10) depending on the size and shape of the plotting area.

Value

a graph containing all tree plots with each tree plot corresponding to the community structure from each of the ensemble matrix.

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

symmetricMatrix <- as.symmetricAdjacencyMatrix(monkeyGrooming, weighted = TRUE, rule = "weak")
Sim <- as.SimilarityMatrix(symmetricMatrix)
temperatures <- temperatureSample(start = 0.01, end = 20, n = 20, method = 'random')
## Not run: 
# for illustration only. skip CRAN check because it ran forever.
Ens_list <- getEnsList(Sim, temperatures, MaxIt = 1000, m = 5)

## End(Not run)

plotCLUSTERS(EnsList = Ens_list, mfrow = c(2, 10), mar = c(1, 1, 1, 1))