# modular.complexity: Compute a measure of modular complexity of patent documents In PABalland/EconGeo: Computing Key Indicators of the Spatial Distribution of Economic Activities

## Description

This function computes a measure of modular complexity of patent documents from technological classes - patents (incidence) matrices

## Usage

 `1` ```modular.complexity(mat, sparse = FALSE, list = FALSE) ```

## Arguments

 `mat` A bipartite adjacency matrix (can be a sparse matrix) `sparse` Logical; is the input matrix a sparse matrix? Defaults to FALSE, but can be set to TRUE if the input matrix is a sparse matrix `list` Logical; is the input a list? Defaults to FALSE (input = adjacency matrix), but can be set to TRUE if the input is an edge list

## Author(s)

Pierre-Alexandre Balland p.balland@uu.nl

## References

Fleming, L. and Sorenson, O. (2001) Technology as a complex adaptive system: evidence from patent data, Research Policy 30: 1019-1039

`ease.recombination`, `TCI`, `MORt`
 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```## generate a technology - patent matrix set.seed(31) mat <- matrix(sample(0:1,30,replace=T), ncol = 5) rownames(mat) <- c ("T1", "T2", "T3", "T4", "T5", "T6") colnames(mat) <- c ("US1", "US2", "US3", "US4", "US5") ## run the function modular.complexity (mat) ## generate a technology - patent sparse matrix library (Matrix) ## run the function smat <- Matrix(mat,sparse=TRUE) modular.complexity (smat, sparse = TRUE) ## generate a regular data frame (list) list <- get.list (mat) ## run the function modular.complexity (list, list = TRUE) ```