easybgm {easybgm}

Fit a Bayesian analysis of networks

Description

Easy estimation of a Bayesian analysis of networks to obtain conditional (in)dependence relations between variables in a network.

Usage

easybgm(
  data,
  type,
  package = NULL,
  not_cont = NULL,
  iter = 10000,
  save = FALSE,
  centrality = FALSE,
  progress = TRUE,
  posterior_method = "model-averaged",
  ...
)

Arguments

data

An n x p matrix or dataframe containing the variables for n independent observations on p variables.

type

What is the data type? Options: continuous, mixed, ordinal, binary

package

The R-package that should be used for fitting the network model; supports BGGM, BDgraph, and bgms. Optional argument; default values are specified depending on the datatype.

not_cont

If data-type is mixed, a vector of length p, specifying the not-continuous variables (1 = not continuous, 0 = continuous).

iter

number of iterations for the sampler.

save

Logical. Should the posterior samples be obtained (default = FALSE)?

centrality

Logical. Should the centrality measures be extracted (default = FALSE)? Note, that it will significantly increase the computation time.

progress

Logical. Should a progress bar be shown (default = TRUE)?

posterior_method

Determines how the posterior samples of the edge weight parameters are obtained for models fit with BDgraph. The argument can be either MAP for the maximum-a-posteriori or model-averaged. If MAP, samples are obtained for the edge weights only for the most likely structure. If model-averaged, samples are obtained for all plausible structures weighted by their posterior probability. Default is model-averaged.

...

Additional arguments that are handed to the fitting functions of the packages, e.g., informed prior specifications.

Details

Users may oftentimes wish to deviate from the default, usually uninformative, prior specifications of the packages to informed priors. This can be done by simply adding additional arguments to the easybgm function. Depending on the package that is running the underlying network estimation, researcher can specify different prior arguments. We give an overview of the prior arguments per package below.

bgms:

• interaction_prior prior distribution of the interaction parameters, can be either "UnitInfo" for the Unit Information prior, or "Cauchy" for the Cauchy distribution. The default is set to "UnitInfo".

• edge_prior prior on the graph structure, which can be either "Bernoulli" or "Beta-Bernoulli". The default is "Bernoulli".

• inclusion_prior prior edge inclusion probability for the "Bernoulli" distribution. The default is 0.5.

• beta_bernoulli_alpha and beta_bernoulli_alpha the parameters of the "Beta-Bernoulli" distribution. The default is 1 for both.

• threshold_alpha and threshold_beta the parameters of the beta-prime distribution for the threshold parameters. The defaults are both set to 1.

BDgraph:

• df.prior prior on the parameters (i.e., inverse covariance matrix), degrees of freedom of the prior G-Wishart distribution. The default is set to 2.5.

• g.prior prior probability of edge inclusion. This can be either a scalar, if it is the same for all edges, or a matrix, if it should be different among the edges. The default is set to 0.5.

BGGM:

• prior_sd the standard deviation of the prior distribution of the interaction parameters, approximately the scale of a beta distribution. The default is 0.25.

We would always encourage researcher to conduct prior robustness checks.

Value

The returned object of easybgm contains several elements:

• parameters A p x p matrix containing partial associations.

• inc_probs A p x p matrix containing the posterior inclusion probabilities.

• BF A p x p matrix containing the posterior inclusion Bayes factors.

• structure Adjacency matrix of the median probability model (i.e., edges with a posterior probability larger 0.5).

In addition, for BDgraph and bgms, the function returns:

• structure_probabilities A vector containing the posterior probabilities of all visited structures, between 0 and 1.

• graph_weights A vector containing the number of times a particular structure was visited.

• sample_graphs A vector containing the indexes of a particular structure.

For all packages, when setting save = TRUE and centrality = TRUE, the function will return the following objects respectively:

• samples_posterior A k x iter matrix containing the posterior samples for each parameter (i.e., k = (p/(p-1))/2) at each iteration (i.e., iter) of the sampler.

• centrality A p x iter matrix containing the centrality of a node at each iteration of the sampler.

Examples

library(easybgm)
library(bgms)

data <- na.omit(Wenchuan)

# Fitting the Wenchuan PTSD data

fit <- easybgm(data, type = "continuous",
                iter = 1000 # for demonstration only (> 5e4 recommended)
                )

summary(fit)


# To extract the posterior parameter distribution
# and centrality measures

fit <- easybgm(data, type = "continuous",
                iter = 1000, # for demonstrative purposes, generally, 1e5 iterations are recommended
                save = TRUE,
                centrality = TRUE)