coin | R Documentation |

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)

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

`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. |

Produce a matrix of coincidences from a matrix of incidences.

An object of `coin`

class

`n` |
Number of scenarios (rows of the incidence matrix) |

`f` |
Coincidence matrix |

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

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")}.

```
## 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)
```

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