fixedrow {backbone} | R Documentation |

## Extract backbone using the Fixed Row Model

### Description

`fixedrow`

extracts the backbone of a bipartite projection using the Fixed Row Model.

### Usage

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

### Arguments

`B` |
An unweighted bipartite graph, as: (1) an incidence 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. |

### Details

The `fixedrow`

function compares an edge's observed weight in the projection `B*t(B)`

to the
distribution of weights expected in a projection obtained from a random bipartite graph where
the *row* vertex degrees are fixed but the column vertex degrees are allowed to vary.

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.

### Value

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()`

.

### References

package: Neal, Z. P. (2022). backbone: An R Package to Extract Network Backbones. *PLOS ONE, 17*, e0269137. doi:10.1371/journal.pone.0269137

Neal, Z. P., Domagalski, R., and Sagan, B. (2021). Comparing Alternatives to the Fixed Degree Sequence Model for Extracting the Backbone of Bipartite Projections. *Scientific Reports, 11*, 23929. doi:10.1038/s41598-021-03238-3

### Examples

```
#A binary bipartite network of 30 agents & 75 artifacts; agents form three communities
B <- rbind(cbind(matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10),
matrix(rbinom(250,1,.2),10)),
cbind(matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.2),10),
matrix(rbinom(250,1,.8),10)))
P <- B%*%t(B) #An ordinary weighted projection...
plot(igraph::graph_from_adjacency_matrix(P, mode = "undirected",
weighted = TRUE, diag = FALSE)) #...is a dense hairball
bb <- fixedrow(B, alpha = 0.05, narrative = TRUE, class = "igraph") #A fixedrow backbone...
plot(bb) #...is sparse with clear communities
```

*backbone*version 2.1.4 Index]