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.

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.

Value

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

# \donttest{
####################################
###### 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
#> BGGM: Bayesian Gaussian Graphical Models 
#> --- 
#> Network Stats: Roll Your Own
#> Posterior Samples: 250 
#> --- 
#> Estimates: 
#> 
#>  Post.mean Post.sd Cred.lb Cred.ub
#>      0.351   0.109    0.15   0.547
#> --- 


############################################
###### example 2: expected influence #######
############################################
# expected influence from this package
library(networktools)
#> Registered S3 methods overwritten by 'car':
#>   method                          from
#>   influence.merMod                lme4
#>   cooks.distance.influence.merMod lme4
#>   dfbeta.influence.merMod         lme4
#>   dfbetas.influence.merMod        lme4

# data
Y <- depression

# fit model
fit <- estimate(Y = Y, iter = 250)
#> BGGM: Posterior Sampling 
#> BGGM: Finished

# 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)
#> BGGM: Posterior Sampling 
#> BGGM: Finished

# 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)

# }