Title:	Implementation of Cluster-Polarization Coefficient
Version:	2.6.0
Description:	Implements cluster-polarization coefficient for measuring distributional polarization in single or multiple dimensions, as well as associated functions. Contains support for hierarchical clustering, k-means, partitioning around medoids, density-based spatial clustering with noise, and manually imposed cluster membership. Mehlhaff (forthcoming) <doi:10.1017/S0003055423001041>.
License:	CC0
Encoding:	UTF-8
RoxygenNote:	7.2.0
URL:	https://imehlhaff.net/CPC/
BugReports:	https://github.com/imehlhaff/CPC/issues
Imports:	stats, cluster, dbscan, Rfast
NeedsCompilation:	no
Packaged:	2024-04-08 15:55:56 UTC; IsaacMehlhaff
Author:	Isaac Mehlhaff [aut, cre, cph]
Maintainer:	Isaac Mehlhaff <isaac.mehlhaff@gmail.com>
Repository:	CRAN
Date/Publication:	2024-04-08 16:20:06 UTC

Cluster-Polarization Coefficient

Description

Implements clustering algorithms and calculates cluster-polarization coefficient. Contains support for hierarchical clustering, k-means clustering, partitioning around medoids, density-based spatial clustering with noise, and manual assignment of cluster membership.

Usage

CPC(
  data,
  type,
  k = NULL,
  epsilon = NULL,
  model = FALSE,
  adjust = FALSE,
  cols = NULL,
  clusters = NULL,
  ...
)

Arguments

Details

type must take one of six values:
"hclust": agglomerative hierarchical clustering with hclust(),
"diana": divisive hierarchical clustering with diana(),
"kmeans": k-means clustering with kmeans(),
"pam": k-medoids clustering with pam(),
"dbscan": density-based clustering with dbscan(),
"manual": no clustering is necessary, researcher has specified cluster assignments.

For all clustering methods, additional arguments to fine-tune clustering performance, such as the specific algorithm to be used, should be passed to CPC() and will be inherited by the specified clustering function. In particular, if type = "kmeans", using a large number of random starts is recommended. This can be specified with the nstart argument to kmeans(), passed directly to CPC().

If type = "manual", data must contain a vector identifying cluster membership for each observation, and cols and clusters must be defined.

Value

If model = TRUE, CPC() returns a list with components containing output from the specified clustering function, all sums of squares, the CPC, the adjusted CPC, and associated standard errors. If model = FALSE, CPC() returns a numeric vector of length 1 giving the CPC (if adjust = FALSE) or adjusted CPC (if adjust = TRUE).

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)
clusters <- matrix(c(rep(1, 25), rep(2, 25)), ncol = 1)
data <- cbind(data, clusters)

CPC(data[,c(1:2)], "kmeans", k = 2)
CPC(data, "manual", cols = 1:2, clusters = 3)

Data Manipulation for CPC Calculation

Description

Converts numeric matrix to data frame with necessary format for "manual" CPC() calculation.

Usage

CPCdata.frame(data, cols, clusters)

Arguments

Value

Returns a data frame with dimensions identical to those of data.

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)
clusters <- matrix(c(rep(1, 25), rep(2, 25)), ncol = 1)
data <- cbind(data, clusters)
CPCdata.frame(data, 1:2, 3)

Euclidean Distance from Dimension Means

Description

Calculates two-dimensional Euclidean distance between all points and dimension means.

Usage

Euclidean(data)

Arguments

Value

Returns a numeric vector of length 1.

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)

Euclidean(data)

Sum-of-Squares Calculation

Description

Calculates sums of squares for uni- or multi-dimensional numeric data using the distance matrix.

Usage

SS(data, ...)

Arguments

Value

Returns a numeric vector of length 1.

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)
SS(data)

Test for Bivariate Correlation

Description

Calculates correlation coefficient between two variables and returns a list containing the correlation estimate, its standard error, the p-value of a null-hypothesis significance test, and the number of observations used.

Usage

correlate(x, y, ...)

Arguments

Details

Additional arguments to alter the type of null hypothesis significance test, the method used to calculate the correlation coefficient, the confidence level, or other options should be passed to correlate() and will be inherited by cor.test(). Note that unlike cor.test(), both arguments x and y are required.

Value

Returns a list with elements containing the correlation coefficient estimate, its associated standard error, the p-value of a null-hypothesis significance test, and the number of observations used, all as numeric vectors of length 1.

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)

correlate(data[, 1], data[, 2])

Multidimensional Difference-in-Means

Description

Calculates average Euclidean distance between means in arbitrary dimensions.

Usage

diff_multidim(data, cols, clusters)

Arguments

Value

Returns a numeric vector of length 1.

Examples

data <- matrix(c(rnorm(50, 0, 1), rnorm(50, 5, 1)), ncol = 2, byrow = TRUE)
clusters <- matrix(c(rep(1, 25), rep(2, 25)), ncol = 1)
data <- cbind(data, clusters)

diff_multidim(data, 1:2, 3)