Description Usage Arguments Details Value Author(s) References Examples
Matrix-level indices are calculated, including the number of connections, connection density, number of concepts, number of transmitters, number of receivers, number of no connections, number of ordinary, number of self-loops, connections per variable, complexity, and hierarchy.
1 |
matrix |
A quantitative fuzzy cognitive matrix. |
The fuzzy cognitive maps should be in the form of a quantitative adjacency matrix.
A dataframe containing the number of connections, connection density, number of concepts, number of transmitters, number of receivers, number of no connections, number of ordinary, number of self-loops, connections per variable, complexity, and hierarchy.
Shaun Turney
Ozesmi, U., & Ozesmi, S. L. (2004). Ecological models based on people's knowledge: a multi-step fuzzy cognitive mapping approach. Ecological Modelling, 176(1), 43-64.
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")
matrix.indices(matrix)
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Index Value
1 Number of connections 17.00000000
2 Connection density 0.34693878
3 Number of concepts 7.00000000
4 Number of transmitters 1.00000000
5 Number of receivers 0.00000000
6 Number of no connections 0.00000000
7 Number of ordinary 6.00000000
8 Number of self loops 3.00000000
9 Connections/variable 2.42857143
10 Complexity (R/T) 0.00000000
11 Hierarchy 0.01321429
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