matrix.indices: Matrix indices

Description Usage Arguments Details Value Author(s) References Examples

Description

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.

Usage

1

Arguments

matrix

A quantitative fuzzy cognitive matrix.

Details

The fuzzy cognitive maps should be in the form of a quantitative adjacency matrix.

Value

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.

Author(s)

Shaun Turney

References

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.

Examples

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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)

Example output

                      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

FCMapper documentation built on May 2, 2019, 3:17 p.m.