Description Usage Arguments Details Value Author(s) Examples

Concept-level indices are calculated, including the out-degree, in-degree, centrality, and whether it is a transmitter, reveiver, ordinary or unconnected concept.

1 | ```
concept.indices(matrix, concept.names)
``` |

`matrix` |
A quantitative fuzzy cognitive map. |

`concept.names` |
A character vector. |

The fuzzy cognitive map should be in the form of a quantitative adjacency matrix. The concept.names input is the names of the concepts in the fuzzy cognitive map.

A dataframe containing the concept name, out-degree, in-degree, centrality, and whether it is a transmitter, reveiver, ordinary or unconnected concept.

Shaun Turney

1 2 3 4 5 6 7 8 9 10 11 | ```
matrix = matrix(nrow=7,ncol=7)
matrix[1,] = c(0,-0.5,0,0,1,0,1)
matrix[2,] = c(1,0,1,0.2,0,0,0.6)
matrix[3,] = c(0,1,0,0,0,0,0)
matrix[4,] = c(0.6,0,0,1,0,0,0.1)
matrix[5,] = c(0,0.5,0,0,1,0,-0.6)
matrix[6,] = c(0,0,-1,0,0,0,0)
matrix[7,] = c(0,0,0,-0.5,0,0,1)
concept.names = c("A","B","C","D","E","F","G")
concept.indices(matrix,concept.names)
``` |

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