This function is similar to what ergm::summary_formula does, but it provides a fast wrapper suited for matrix class objects (see benchmark in the examples).

count_stats(X, ...) AVAILABLE_STATS() # S3 method for formula count_stats(X, ...) # S3 method for list count_stats(X, terms, attrs = NULL, ...)

X | List of square matrices. (networks) |
---|---|

... | Passed to the method. |

terms | Character vector with the names of the statistics to calculate. Currently, the only available statistics are: 'mutual', 'edges', 'ttriad', 'ctriad', 'ctriple', 'nodeicov', 'nodeocov', 'nodematch', 'triangle', 'balance', 't300', 't102', 'absdiff', 'idegree1.5', 'odegree1.5', 'ostar1', 'ostar2', 'ostar3', 'ostar4', 'istar1', 'istar2', 'istar3', 'istar4 '. |

attrs | A list of vectors. This is used when |

A matrix of size `length(X) * length(terms)`

with the corresponding
counts of statistics.

# DGP set.seed(123199) x <- rbernoulli(rep(5, 10)) ans0 <- count_stats(x, c("mutual", "edges")) # Calculating using summary_formula ans1 <- lapply(x, function(i) { ergm::summary_formula(i ~ mutual + edges) }) ans1 <- do.call(rbind, ans1) # Comparing all.equal(unname(ans0), unname(ans1))#> [1] TRUE# count_stats is vectorized (and so faster) bm <- benchmarkito( count_stats = count_stats(x, c("mutual", "edges")), lapply = lapply(x, function(i) { ergm::summary_formula(i ~ mutual + edges) }), times = 50 )#> Error in lapply(x, function(i) { ergm::summary_formula(i ~ mutual + edges)}): object 'x' not foundplot(bm)#> Error in plot(bm): object 'bm' not found