Cluster Prominence Metric for a GLCM — cluster

Calculate the cluster prominence feature or metric for a gray-level co-occurrence matrix. For definition and application, see Lofstedt et al. (2019) doi:10.1371/journal.pone.0212110 .

cluster_prom(x, ...)

# S3 method for default
cluster_prom(x, ...)

# S3 method for matrix
cluster_prom(x, ...)

# S3 method for FitLandDF
cluster_prom(x, nlevels, ...)

Arguments

x: gray-level co-occurrence matrix
...: additional parameters
nlevels: desired number of discrete gray levels

Value

cluster prominence metric of x

Examples

## calculate cluster prominence of arbitrary GLCM
# define arbitrary GLCM
x <- matrix(1:16, nrow = 4)

# normalize
n_x <- normalize_glcm(x)

# calculate cluster prominence
cluster_prom(n_x)
#> [1] 209.1088

## calculate cluster prominence of arbitrary fitness landscape
# create fitness landscape using FitLandDF object
vals <- runif(64)
vals <- array(vals, dim = rep(4, 3))
my_landscape <- fitscape::FitLandDF(vals)

# calculate cluster prominence of fitness landscape, assuming 2 discrete gray levels
cluster_prom(my_landscape, nlevels = 2)
#> [1] 19.47379

## confirm value of cluster prominence for fitness landscape
# extract normalized GLCM from fitness landscape
my_glcm <- get_comatrix(my_landscape, discrete = equal_discrete(2))

# calculate cluster prominence of extracted GLCM
cluster_prom(my_glcm)  # should match value of above cluster_prom function call
#> [1] 19.47379