Title: Generalized Application of Co-Occurrence Matrices and Haralick Texture
Version: 1.0.0
Description: Generalizes application of gray-level co-occurrence matrix (GLCM) metrics to objects outside of images. The current focus is to apply GLCM metrics to the study of biological networks and fitness landscapes that are used in studying evolutionary medicine and biology, particularly the evolution of cancer resistance. The package was developed as part of the author's publication in Physics in Medicine and Biology Barker-Clarke et al. (2023) <doi:10.1088/1361-6560/ace305>. A general reference to learn more about mathematical oncology can be found at Rockne et al. (2019) <doi:10.1088/1478-3975/ab1a09>.
License: MIT + file LICENSE
URL: <https://rbarkerclarke.github.io/gtexture/>
BugReports: https://github.com/rbarkerclarke/gtexture/issues
Imports: dlookr, dplyr (≥ 1.0), fitscape (≥ 0.1), igraph, magrittr (≥ 2.0), rlang, tidyr
Suggests: stats, testthat
Encoding: UTF-8
RoxygenNote: 7.3.1
NeedsCompilation: no
Packaged: 2024-04-08 03:55:44 UTC; stephbc
Author: Rowan Barker-Clarke ORCID iD [aut, cre], Raoul Wadhwa ORCID iD [aut], Davis Weaver [aut], Jacob Scott ORCID iD [aut]
Maintainer: Rowan Barker-Clarke <rowanbarkerclarke@gmail.com>
Repository: CRAN
Date/Publication: 2024-04-08 15:30:05 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

lhs

A value or the magrittr placeholder.

rhs

A function call using the magrittr semantics.

Value

The result of calling rhs(lhs).

Autocorrelation Metric for a GLCM or GLCM equivalent matrix

Description

Calculate the autocorrelation 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.

Usage

autocorrelation(x, ...)

## Default S3 method:
autocorrelation(x, ...)

## S3 method for class 'matrix'
autocorrelation(x, ...)

## S3 method for class 'FitLandDF'
autocorrelation(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate autocorrelation
autocorrelation(n_x)

## calculate autocorrelation 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 autocorrelation of fitness landscape, assuming 2 discrete gray levels
autocorrelation(my_landscape, nlevels = 2)

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

# calculate autocorrelation of extracted GLCM
autocorrelation(my_glcm)  # should match value of above autocorrelation function call

Cluster Prominence Metric for a GLCM

Description

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.

Usage

cluster_prom(x, ...)

## Default S3 method:
cluster_prom(x, ...)

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

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

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

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)

## 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)

## 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

Cluster Shade Metric for a GLCM

Description

Calculate the cluster shade 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.

Usage

cluster_shade(x, ...)

## Default S3 method:
cluster_shade(x, ...)

## S3 method for class 'matrix'
cluster_shade(x, ...)

## S3 method for class 'FitLandDF'
cluster_shade(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate cluster shade
cluster_shade(n_x)

## calculate cluster shade 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 shade of fitness landscape, assuming 2 discrete gray levels
cluster_shade(my_landscape, nlevels = 2)

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

# calculate cluster shade of extracted GLCM
cluster_shade(my_glcm)  # should match value of above cluster_shade function call

Calculate Co-Occurrence Matrix

Description

Calculate generalized co-occurrence matrix from a variety of objects, currently including fitness landscapes stored as a FitLandDF instance from the fitscape package.

Usage

get_comatrix(x, ...)

## Default S3 method:
get_comatrix(x, ...)

## S3 method for class 'FitLandDF'
get_comatrix(
  x,
  discrete = equal_discrete(2),
  neighbor = manhattan(1),
  normalize = normalize_glcm,
  ...
)

## S3 method for class 'igraph'
get_comatrix(
  x,
  values,
  nlevels = length(unique(values)),
  normalize = normalize_glcm,
  verbose = TRUE,
  ...
)

Arguments

x

object upon which co-occurrence matrix will be calculated

...

additional arguments

discrete

function that discretizes object

neighbor

function that returns TRUE if two numeric vectors are within acceptable distance of one another or a single-element character vector that describes how to identify acceptable neighbors/offsets

normalize

function that normalizes the co-occurrence matrix

values

named numeric with values corresponding to the nodes in x.

nlevels

int number of levels to discretize into

verbose

bool

Value

matrix (co-occurrence matrix)

Examples

# create fitness landscape as instance of FitLandDF object
a <- round(runif(64))
a <- array(a, dim = rep(4, 3))
my_landscape <- fitscape::FitLandDF(a)

# calculate co-occurrence matrix using:
#   Manhattan distance of 1
#   discretization into 2 equal-sized buckets
#   normalization: multiply all elements so that sum of matrix equals unity
comat <- get_comatrix(my_landscape,
                      discrete = equal_discrete(2),
                      neighbor = manhattan(1))

# print co-occurrence matrix
print(comat)

Convenience function to compute all haralick texture features

Description

Calculate set of the following Haralick texture features; contrast, entropy, energy, autocorrelation, correlation, cluster prominence, cluster shade, homogeneity, inverse difference, max probability, and sum of squares.

Usage

compute_all_metrics(x)

Arguments

x

Input glcm matrix

Value

data.frame with 11 columns

Contrast Metric for a GLCM

Description

Calculate the contrast 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.

Usage

contrast(x, ...)

## Default S3 method:
contrast(x, ...)

## S3 method for class 'matrix'
contrast(x, ...)

## S3 method for class 'FitLandDF'
contrast(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate contrast
contrast(n_x)

# calculate contrast of fitness landscape, assuming 2 discrete gray levels
vals <- runif(64)
vals <- array(vals, dim = rep(4, 3))
my_landscape <- fitscape::FitLandDF(vals)

my_glcm <- get_comatrix(my_landscape, discrete = equal_discrete(2))
contrast(my_landscape, nlevels = 2)

## confirm value of contrast for fitness landscape
# extract normalized GLCM from fitness landscape

contrast(my_glcm)  # should match value of above contrast function call

Correlation Metric for a GLCM

Description

Calculate the correlation 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.

Usage

correlation(x, ...)

## Default S3 method:
correlation(x, ...)

## S3 method for class 'matrix'
correlation(x, ...)

## S3 method for class 'FitLandDF'
correlation(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate correlation
correlation(n_x)

## calculate autocorrelation 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 correlation of fitness landscape, assuming 2 discrete gray levels
correlation(my_landscape, nlevels = 2)

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

# calculate correlation of extracted GLCM
correlation(my_glcm)  # should match value of above correlation function call

Difference entropy is the entropy of marginal distribution of the difference in gray-level value equivalents x-y

Description

Difference entropy is the entropy of marginal distribution of the difference in gray-level value equivalents x-y

Usage

differenceEntropy.matrix(glcm, base = 2)

Arguments

glcm

gray level co-occurrence matrix

base

Base of the logarithm in differenceEntropy.

Value

float (single value: the entropy of the marginal distribution)

Examples

# Calculate difference entropy of a given glcm (e.g. uniform matrix)
differenceEntropy.matrix(matrix(1,3,3))

Discretize Numeric Variable Into Categories

Description

Takes a numeric variable (could be of class numeric or integer) and returns a discretized version, in which each element has been replaced by a single integer between 1 and nlevels, inclusive.

Usage

discretize(x, ...)

## S3 method for class 'numeric'
discretize(x, nlevels, method = "equal", ...)

## S3 method for class 'list'
discretize(x, nlevels, ...)

## S3 method for class 'integer'
discretize(x, nlevels, ...)

## S3 method for class 'FitLandDF'
discretize(x, nlevels, ...)

Arguments

x

either a vector (numeric or integer) or FitLandDF object

...

potential additional arguments, currently unnecessary

nlevels

positive integer indicating number of discrete categories

method

method by which to discretize; split into equal sections by default ("equal" value for parameter)

Value

discretized form of x

Examples


## discretize a numeric vector
vec <- 1:10
discretize(vec, nlevels = 5) # discretize into 5 categories
discretize(vec, 2)           # discretize into 2 categories

## discretize a fitness landscape
# create a 3x3x3 fitness landscape with values 1 through 27
fl_data <- array(1:27, dim = rep(3, 3))
my_fl <- fitscape::FitLandDF(fl_data)
discretize(my_fl, nlevels = 2) # discretize landscape into 2 categories
discretize(my_fl, 5)           # discretize landscape into 5 categories

Dissimilarity of a co-occurrence matrix

Description

Dissimilarity is the weighted sum of all of the absolute differences in the gray-levels assigned to neighboring nodes across the network or graph. For example, a diagonal matrix would represent only identical neighbors and no dissimilarity.

Usage

dissimilarity.matrix(glcm)

Arguments

glcm

gray-level co-occurrence matrix

Value

int or double (the weighted sum of differences)

Examples

# Calculate dissimilarity of a 2x2 uniform matrix
dissimilarity.matrix(matrix(1,2,2))

# Calculate dissimilarity of a diagonal matrix
dissimilarity.matrix(diag(1,5,5))

# Calculate dissimilarity of a sequential matrix
dissimilarity.matrix(matrix(1:16,4,4))

Energy Metric for a GLCM

Description

Calculate the energy 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.

Usage

energy(x, ...)

## Default S3 method:
energy(x, ...)

## S3 method for class 'matrix'
energy(x, ...)

## S3 method for class 'FitLandDF'
energy(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate energy
energy(n_x)

## calculate energy 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 energy of fitness landscape, assuming 2 discrete gray levels
energy(my_landscape, nlevels = 2)

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

# calculate energy of extracted GLCM
energy(my_glcm)  # should match value of above energy function call

Entropy Metric for a GLCM

Description

Calculate the entropy 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.

Usage

entropy(x, ...)

## Default S3 method:
entropy(x, ...)

## S3 method for class 'matrix'
entropy(x, ...)

## S3 method for class 'FitLandDF'
entropy(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate entropy
entropy(n_x)

## calculate entropy 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 entropy of fitness landscape, assuming 2 discrete gray levels
entropy(my_landscape, nlevels = 2)

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

# calculate entropy of extracted GLCM
entropy(my_glcm)  # should match value of above entropy function call

Function Factory for Even Discretization Functions

Description

Returns a function that converts a continuous numeric vector into an integer vector with discrete levels.

Usage

equal_discrete(nlevels)

Arguments

nlevels

number of levels to split continuous vector into

Value

function that makes a numeric vector discrete

Examples

# test data
x <- 1:10

# create and apply function to split x into 2 discrete levels
split_2 <- equal_discrete(2)
split_2(x)

# create and apply function to split x into 5 discrete levels
split_5 <- equal_discrete(5)
split_5(x)

Euclidean Distance Function Factory

Description

Returns a function that checks whether the Euclidean distance between two numeric vectors is less than or equal to a given threshold.

Usage

euclidean(dist = 1)

Arguments

dist

threshold above which the function will return FALSE

Value

function that checks if Euclidean distance between two vectors exceeds dist

Examples

# test data: Euclidean distance equals sqrt(2) ~ 1.414
x <- rep(0, 5)
y <- c(0, 1, 0, 0, 1)

# should return TRUE when checking Manhattan distance <= 2
dist_2 <- euclidean(2)
dist_2(x, y)

# should return FALSE when checking Manhattan distance <= 1
dist_1 <- euclidean(1)
dist_1(x, y)

Marginal distributions of the GLCM

Description

Functions for the calculation of marginal distributions from the GLCM matrix.

The partial sum of the matrix is used to determine the distribution over the sum of neighbor pairs, returns a value for a given sum k.

The partial sum of the matrix is used to determine the distribution over the sum of neighbor pairs, returns a value for a given difference of k.

Usage

xplusy_k(glcm, k)

xminusy_k(glcm, k)

Arguments

glcm

square co-occurrence matrix

k

real integer (given difference)

Value

int or double (xplusy_k: sum of matrix entries with given index sum)

int or double (sum of matrix entries with given index difference)

Statistics of GLCM

Description

Functions for the calculation of summary statistics upon the GLCM matrix.

glcm_mean: GLCM mean of a symmetric GLCM matrix. The GLCM Mean is not simply the average of all the original node values in the network/graph. It is expressed in terms of the GLCM. The node value is weighted not by its frequency of occurrence by itself (as in a "regular" or familiar mean but by its frequency of its occurrence in combination with a certain neighbour node value; see https://prism.ucalgary.ca/server/api/core/bitstreams/8f9de234-cc94-401d-b701-f08ceee6cfdf/content

glcm_variance: The variance of the GLCM values

Usage

glcm_mean(glcm)

mu_x.matrix(glcm)

mu_y.matrix(glcm)

glcm_variance(glcm)

Arguments

glcm

gray level co-occurrence matrix

Value

int or double (glcm_mean: single value, mean of symmetric glcm)

int or double (mu_x: weighted mean of reference node values)

int or double (mu_y: weighted mean of neighbor node values)

int or double (glcm_variance: glcm variance)

Homogeneity Metric for a GLCM

Description

Calculate the homogeneity 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.

Usage

homogeneity(x, ...)

## Default S3 method:
homogeneity(x, ...)

## S3 method for class 'matrix'
homogeneity(x, ...)

## S3 method for class 'FitLandDF'
homogeneity(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate homogeneity
homogeneity(n_x)

## calculate homogeneity 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 homogeneity of fitness landscape, assuming 2 discrete gray levels
homogeneity(my_landscape, nlevels = 2)

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

# calculate homogeneity of extracted GLCM
homogeneity(my_glcm)  # should match value of above homogeneity function call

Inverse Difference Metric for a GLCM

Description

Calculate the inverse difference 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.

Usage

inv_diff(x, ...)

## Default S3 method:
inv_diff(x, ...)

## S3 method for class 'matrix'
inv_diff(x, ...)

## S3 method for class 'FitLandDF'
inv_diff(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate inverse difference
inv_diff(n_x)

## calculate inverse difference 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 inverse difference of fitness landscape, assuming 2 discrete gray levels
inv_diff(my_landscape, nlevels = 2)

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

# calculate inverse difference of extracted GLCM
inv_diff(my_glcm)  # should match value of above inv_diff function call

Kmeans clustering discretization

Description

Splitting of a vector of continuous values into k groups using kmeans. Used to discretize node labels/weights of a fitness landscape or node-weighted graph.

Usage

kmeans_discrete(nlevels)

Arguments

nlevels

number of levels to split continuous vector into

Value

function that makes a numeric vector discrete

Manhattan Distance Function Factory

Description

Returns a function that checks whether the Manhattan distance between two numeric vectors is less than or equal to a given threshold.

Usage

manhattan(dist = 1)

Arguments

dist

threshold above which the function will return FALSE

Value

function that checks if Manhattan distance between two vectors exceeds dist

Examples

# test data: Manhattan distance equals 2
x <- rep(0, 5)
y <- c(0, 1, 0, 0, 1)

# should return TRUE when checking Manhattan distance <= 3
dist_3 <- manhattan(3)
dist_3(x, y)

# should return FALSE when checking Manhattan distance <= 1
dist_1 <- manhattan(1)
dist_1(x, y)

Maximum Probability Metric for a GLCM

Description

Calculate the maximum probability 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.

Usage

max_prob(x, ...)

## Default S3 method:
max_prob(x, ...)

## S3 method for class 'matrix'
max_prob(x, ...)

## S3 method for class 'FitLandDF'
max_prob(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate maximum probability
max_prob(n_x)

## calculate maximum probability 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 maximum probability of fitness landscape, assuming 2 discrete gray levels
max_prob(my_landscape, nlevels = 2)

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

# calculate maximum probability of extracted GLCM
max_prob(my_glcm)  # should match value of above max_prob function call

Normalize a GLCM

Description

Function that normalizes a gray-level co-occurrence matrix (GLCM) so that the sum of all the elements equals unity. This has the added benefit of converting the GLCM to a probability distribution.

Usage

normalize_glcm(mat)

Arguments

mat

gray-level co-occurrence matrix

Value

numeric matrix (same dimensions as input GLCM)

Examples

# normalize an arbitrary matrix
a <- matrix(1:9, nrow = 3)
n_a <- normalize_glcm(a)

print(a)
print(n_a)

Function to discretize based on quantiles

Description

Function to discretize based on quantiles

Usage

quantile_discrete(nlevels)

Arguments

nlevels

number of levels to split continuous vector into

Value

function that makes a numeric vector discrete

Examples

# test data

Sum of Squares Metric for a GLCM

Description

Calculate the sum of squares 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.

Usage

sum_squares(x, ...)

## Default S3 method:
sum_squares(x, ...)

## S3 method for class 'matrix'
sum_squares(x, ...)

## S3 method for class 'FitLandDF'
sum_squares(x, nlevels, ...)

Arguments

x

gray-level co-occurrence matrix

...

additional parameters

nlevels

desired number of discrete gray levels

Value

double

Examples

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

# normalize
n_x <- normalize_glcm(x)

# calculate sum of squares
sum_squares(n_x)

## calculate sum of squares 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 sum of squares of fitness landscape, assuming 2 discrete gray levels
sum_squares(my_landscape, nlevels = 2)

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

# calculate sum of squares of extracted GLCM
sum_squares(my_glcm)  # should match value of above sum_squares function call

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