Package 'CPC'

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

x	a numeric vector.
y	a numeric vector.
...	arguments passed to cor.test().

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])

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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

data	a numeric vector or n x k matrix or data frame. If type = "manual", data must be a matrix containing a vector identifying cluster membership for each observation, to be passed to clusters argument.
type	a character string giving the type of clustering method to be used. See Details.
k	the desired number of clusters. Required if type is one of "hclust", "diana", "kmeans", or "pam".
epsilon	radius of epsilon neighborhood. Required if type = "dbscan".
model	a logical indicating whether clustering model output should be returned. Defaults to FALSE.
adjust	a logical indicating whether the adjusted CPC should be calculated. Defaults to FALSE. Note that both CPC and adjusted CPC are automatically calculated and returned if model = TRUE.
cols	columns of data to be used in CPC calculation. Only used if type = "manual".
clusters	column of data indicating cluster membership for each observation. Only used if type = "manual".
...	arguments passed to other functions.

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

data	a numeric n x k matrix or data frame.
cols	columns in data to be used for calculating CPC().
clusters	column in data giving cluster membership.

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)

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Multidimensional Difference-in-Means

Description

Calculates average Euclidean distance between means in arbitrary dimensions.

Usage

diff_multidim(data, cols, clusters)

Arguments

data	a numeric vector or n x k matrix or data frame containing a vector identifying cluster membership for each observation, to be passed to clusters argument.
cols	columns of data to be used in difference-in-means calculation.
clusters	column of data indicating cluster membership for each observation.

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)

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Euclidean Distance from Dimension Means

Description

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

Usage

Euclidean(data)

Arguments

data	an n x 2 matrix or data frame.

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)

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Sum-of-Squares Calculation

Description

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

Usage

SS(data, ...)

Arguments

data	a numeric vector or n x k matrix or data frame.
...	arguments passed to dist().

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)

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