fixedcol {backbone} | R Documentation |
fixedcol
extracts the backbone of a bipartite projection using the Fixed Column Model.
fixedcol(
B,
alpha = 0.05,
signed = FALSE,
mtc = "none",
class = "original",
narrative = FALSE
)
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) |
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. |
This fixedcol
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 column vertex degrees are fixed but the row vertex degrees are allowed to vary.
When signed = FALSE
, a one-tailed test (is the weight stronger) is performed for each edge with a non-zero weight. It
yields a backbone that perserves edges whose weights are significantly stronger than expected under the null
model. When signed = TRUE
, a two-tailed test (is the weight stronger or weaker) is performed for each every pair of nodes.
It yields a backbone that contains positive edges for edges whose weights are significantly stronger, and
negative edges for edges whose weights are significantly weaker, than expected in the chosen null model.
NOTE: Before v2.0.0, all significance tests were two-tailed and zero-weight edges were evaluated.
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. doi: 10.1371/journal.pone.0269137
fixedcol: 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
#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 <- fixedcol(B, alpha = 0.05, narrative = TRUE, class = "igraph") #A fixedcol backbone...
plot(bb) #...is sparse with clear communities