Fitting the Model

Single-thread run

The BayesianNetworkRegression.jl package implements the statistical inference method in Ozminkowski & Solís-Lemus (2022) in its main function Fit! which takes three parameters:

  • X: data matrix from microbial networks (networked covariates)
  • y: response vector
  • R: dimension of latent variables

Quality of inference will increase with increasing R up to a point, after which it will approximately plateau. The time it takes to fit the model will increase (worse than linearly) with increasing R. For our simulations, R=7 worked best.

We will use the data read in the Input Data section:

  • X_a: vector of adjacency matrices
  • X_v: matrix of vectorized adjacency matrices

The type of input data matrix X will inform the argument x_transform so that:

  • x_transform=true means that the input matrix X needs to be vectorized (as for X_a),
  • x_transform=false means that the input matrix X has already been vectorized (as for X_v).

To fit the model, we type this in julia:

using BayesianNetworkRegression

result = Fit!(X_a, y_a, 5, # we set R=5
    nburn=200, nsamples=100, x_transform=true, 

Note that we are running a very small chain (300 generations: 200 burnin and 100 post burnin). For a real analysis, these number should be much larger (see the Simulation in the manuscript for more details).

The result variable is a Result type which contains five attributes:

  • state::Table: Table with all the sampled parameters for all generations.
  • rhatξ::Table: Table with convergence information ($\hat{R}$ values) for the parameters $\xi$ representing whether specific nodes are influential or not.
  • rhatγ::Table: Table with convergence information ($\hat{R}$ values) for the parameters $\gamma$ representing the regression coefficients.
  • burn_in::Int: number of burnin samples.
  • sampled::Int: number of post burnin samples.

Each of these objects can be accessed with a ., for example, results.state will produce the table with all the samples.

We use the convergence criteria proposed in Vehtari et al (2019). Values close to 1 indicate convergence. Vehtari et al. suggest using a cutoff of $\hat{R} < 1.01$ to indicate convergence. Values are provided for all $\xi$ (in rhatξ) and $\gamma$ (in rhatγ) variables.

These attributes are not readily interpretable, and thus, they can be summarized with the Summary function:

out = Summary(result)

Note that the show function for a Results object is already calling Summary(result) internally, and thus, if you simply call:


you observe the same output as when calling

out = Summary(result)

The out object is now a BNRSummary object with two main data frames:

  1. DataFrame with edge coefficient point estimates and endpoints of credible intervals (default 95%):
julia> out.edge_coef
435×5 DataFrame
 Row │ node1  node2  estimate  lower_bound  upper_bound 
     │ Int64  Int64  Float64   Float64      Float64     
   1 │     1      2     2.385       -1.976        6.157
   2 │     1      3     1.823       -5.257        7.595
   3 │     1      4     1.763       -4.514        6.009
  ⋮  │   ⋮      ⋮       ⋮           ⋮            ⋮
 433 │    28     29     3.51        -1.251        7.376
 434 │    28     30     1.949       -3.425        6.135
 435 │    29     30     2.785       -0.772        6.042
                                        429 rows omitted
  1. DataFrame with probabilities of being influencial for each node
julia> out.prob_nodes
30×1 DataFrame
 Row │ probability 
     │ Float64     
   1 │        0.86
   2 │        0.92
   3 │        0.78
  ⋮  │      ⋮
  28 │        0.93
  29 │        0.89
  30 │        0.87
    24 rows omitted

The BNRSummary object also keeps the level for the credible interval, set at 95% by default:

julia> out.ci_level

The level can be changed when running the Summary function:


We will use these summary data frames in the Interpretation section.

Note that we ran the case when we need to transform the data matrix. If we already have the adjacency matrices vectorized, we simply need to set x_transform=false:

result2 = Fit!(X_v, y_v, 5, # we set R=5
    nburn=200, nsamples=100, x_transform=false, 

Multi-thread run

You can run multiple chains in parallel by setting up multiple processors as shown next.

using Distributed

@everywhere begin
    using BayesianNetworkRegression,CSV,DataFrames,StaticArrays

    matrix_networks = joinpath(dirname(pathof(BayesianNetworkRegression)), "..","examples","matrix_networks.csv")
    data_in = DataFrame(CSV.File(matrix_networks))

    X = Matrix(data_in[:,names(data_in,Not("y"))])
    y = Vector{Float64}(data_in[:,:y])

Compared to the single-thread run, we only need to change num_chains=2:

result3 = Fit!(X, y, 5,
    nburn=200, nsamples=100, x_transform=false, 

Error reporting

Please report any bugs and errors by opening an issue.