coin: Coincidence matrix.

In netCoin: Interactive Analytic Networks

Coincidence matrix.

Description

A coincidence object consists of a list with two elements: 1) the number of scenarios ($n), and 2) a coincidence matrix of events, whose main diagonal figures are the frequency of events and outside this diagonal there are conjoint frequencies of these events ($f)

Usage

coin(incidences, minimum = 1, maximum = nrow(incidences),
     sort = FALSE, decreasing = TRUE, 
     total = FALSE, subsample = FALSE,
     weight = NULL, pairwise = FALSE)

Arguments

incidences	an incidence matrix or data frame with only 0/1 variables
minimum	minimum frequency to be considered
maximum	maximum frequency to be considered
sort	sort the coincidence matrix according to frequency of events
decreasing	decreasing or increasing sort of the matrix
total	add one first row and column with total
subsample	retrict the analysis to scenarios with at least one event
weight	a vector of weights. Optimal for data.framed tables
pairwise	Pairwise mode of handling missing values if TRUE. Listwise by default.

Details

Produce a matrix of coincidences from a matrix of incidences.

Value

An object of coin class

n	Number of scenarios (rows of the incidence matrix)
f	Coincidence matrix

Author(s)

Modesto Escobar, Department of Sociology and Communication, University of Salamanca. See https://sociocav.usal.es/blog/modesto-escobar/

References

Escobar, M. and Martinez-Uribe, L. (2020) Network Coincidence Analysis: The netCoin R Package. Journal of Statistical Software, 93, 1-32. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v093.i11")}.

Examples

## Random incidence matrix: 25 scenarios, 4 events.
I <- matrix(rbinom(100, 1, .5), nrow = 25, ncol = 4,
            dimnames = list(NULL, c("A", "B", "C", "D")))
coin(I, sort = TRUE)

## Hair by Eye by Sex table from M. Friendly (2000)
data(HairEyeColor)
H<-as.data.frame(HairEyeColor)
W<-H$Freq
I<-dichotomize(H,c("Hair","Eye","Sex"),add=FALSE)
coin(I,w=W)

netCoin documentation built on March 31, 2023, 7:34 p.m.