disparity | R Documentation |

`disparity`

extracts the backbone of a weighted network using the Disparity Filter.

```
disparity(
W,
alpha = 0.05,
missing.as.zero = FALSE,
signed = FALSE,
mtc = "none",
class = "original",
narrative = FALSE
)
```

`W` |
A positively-weighted unipartite graph, as: (1) an adjacency matrix in the form of a matrix or sparse |

`alpha` |
real: significance level of hypothesis test(s) |

`missing.as.zero` |
boolean: should missing edges be treated as edges with zero weight and tested for significance |

`signed` |
boolean: TRUE for a signed backbone, FALSE for a binary backbone (see details) |

`mtc` |
string: type of Multiple Test Correction to be applied; can be any method allowed by |

`class` |
string: the class of the returned backbone graph, one of c("original", "matrix", "Matrix", "igraph", "edgelist").
If "original", the backbone graph returned is of the same class as |

`narrative` |
boolean: TRUE if suggested text & citations should be displayed. |

The `disparity`

function applies the disparity filter (Serrano et al., 2009), which compares an edge's weight to
its expected weight if a node's total degree was uniformly distributed across all its edges. The graph may be
directed or undirected, however the edge weights must be positive.

When `signed = FALSE`

, a one-tailed test (is the weight stronger?) is performed for each edge. The resulting backbone
contains edges whose weights are significantly *stronger* than expected in the null model. When `signed = TRUE`

, a
two-tailed test (is the weight stronger or weaker?) is performed for each edge. The resulting backbone contains
positive edges for those whose weights are significantly *stronger*, and negative edges for those whose weights are
significantly *weaker*, than expected in the null model.

If `W`

is an unweighted bipartite graph, then the disparity filter is applied to its weighted bipartite projection.

If `alpha`

!= NULL: Binary or signed backbone graph of class `class`

.

If `alpha`

== NULL: An S3 backbone object containing (1) the weighted graph as a matrix, (2) upper-tail p-values as a
matrix, (3, if `signed = TRUE`

) lower-tail p-values as a matrix, (4, if present) node attributes as a dataframe, and
(5) several properties of the original graph and backbone model, from which a backbone can subsequently be extracted
using `backbone.extract()`

.

package: Neal, Z. P. (2022). backbone: An R Package to Extract Network Backbones. *PLOS ONE, 17*, e0269137. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0269137")}

disparity filter: Serrano, M. A., Boguna, M., & Vespignani, A. (2009). Extracting the multiscale backbone of complex weighted networks. *Proceedings of the National Academy of Sciences, 106*, 6483-6488. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1073/pnas.0808904106")}

```
#A network with heterogeneous (i.e. multiscale) weights
net <- matrix(c(0,10,10,10,10,75,0,0,0,0,
10,0,1,1,1,0,0,0,0,0,
10,1,0,1,1,0,0,0,0,0,
10,1,1,0,1,0,0,0,0,0,
10,1,1,1,0,0,0,0,0,0,
75,0,0,0,0,0,100,100,100,100,
0,0,0,0,0,100,0,10,10,10,
0,0,0,0,0,100,10,0,10,10,
0,0,0,0,0,100,10,10,0,10,
0,0,0,0,0,100,10,10,10,0),10)
net <- igraph::graph_from_adjacency_matrix(net, mode = "undirected", weighted = TRUE)
plot(net, edge.width = sqrt(igraph::E(net)$weight)) #A stronger clique & a weaker clique
strong <- igraph::delete_edges(net, which(igraph::E(net)$weight < mean(igraph::E(net)$weight)))
plot(strong) #A backbone of stronger-than-average edges ignores the weaker clique
bb <- disparity(net, alpha = 0.05, narrative = TRUE) #A disparity backbone...
plot(bb) #...preserves edges at multiple scales
```

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