# Compute Custom Network Statistics This function allows for computing custom network statistics for weighted adjacency matrices (partial correlations). The statistics are computed for each of the sampled matrices, resulting in a distribution. ```r roll_your_own( object, FUN, iter = NULL, select = FALSE, cred = 0.95, progress = TRUE, ... ) ``` ## Arguments - `object`: An object of class `estimate`. - `FUN`: A custom function for computing the statistic. The first argument must be a partial correlation matrix. - `iter`: Number of iterations (posterior samples; defaults to the number in the object). - `select`: Logical. Should the graph be selected ? The default is currently `FALSE`. - `cred`: Numeric. Credible interval between 0 and 1 (default is 0.95) that is used for selecting the graph. - `progress`: Logical. Should a progress bar be included (defaults to `TRUE`) ? - `...`: Arguments passed to the function. ## Returns An object defined by `FUN`. ## Details The user has complete control of this function. Hence, care must be taken as to what `FUN` returns and in what format. The function should return a single number (one for the entire GGM) or a vector (one for each node). This ensures that the print and `plot.roll_your_own` will work. When `select = TRUE`, the graph is selected and then the network statistics are computed based on the weigthed adjacency matrix. This is accomplished internally by multiplying each of the sampled partial correlation matrices by the adjacency matrix. ## Examples ```r #################################### ###### example 1: assortment ####### #################################### # assortment library(assortnet) Y <- BGGM::bfi[,1:10] membership <- c(rep("a", 5), rep("c", 5)) # fit model fit <- estimate(Y = Y, iter = 250, progress = FALSE) # membership membership <- c(rep("a", 5), rep("c", 5)) # define function f <- function(x,...){ assortment.discrete(x, ...)$r } net_stat <- roll_your_own(object = fit, FUN = f, types = membership, weighted = TRUE, SE = FALSE, M = 1, progress = FALSE) # print net_stat ############################################ ###### example 2: expected influence ####### ############################################ # expected influence from this package library(networktools) # data Y <- depression # fit model fit <- estimate(Y = Y, iter = 250) # define function f <- function(x,...){ expectedInf(x,...)$step1 } # compute net_stat <- roll_your_own(object = fit, FUN = f, progress = FALSE) ####################################### ### example 3: mixed data & bridge #### ####################################### # bridge from this package library(networktools) # data Y <- ptsd[,1:7] fit <- estimate(Y, type = "mixed", iter = 250) # clusters communities <- substring(colnames(Y), 1, 1) # function is slow f <- function(x, ...){ bridge(x, ...)$`Bridge Strength` } net_stat <- roll_your_own(fit, FUN = f, select = TRUE, communities = communities, progress = FALSE) ```

roll_your_own function