bbn.visualise {bbnet}

Visualise Bayesian Belief Network Time Series Predictions

Description

bbn.visualise() visualises the outcomes of a Bayesian Belief Network (BBN) model over time, given a single prior scenario. It highlights the changes in network parameters across specified timesteps and visualises the strength and direction of interactions among nodes based on the specified disturbance and threshold parameters.

Usage

bbn.visualise(
  bbn.model,
  priors1,
  timesteps = 5,
  disturbance = 1,
  threshold = 0.2,
  font.size = 0.7,
  arrow.size = 4
)

Arguments

bbn.model

A matrix or dataframe of interactions between different model nodes.

priors1

An X by 2 array of initial changes to the system under investigation. The first column should be a -4 to 4 (including 0) integer value for each node in the network with negative values indicating a decrease and positive values representing an increase. 0 represents no change.

timesteps

This is the number of timesteps the model performs. Default = 5. Note, timesteps are arbitrary and non-linear. However, something occurring in timestep 2, should occur before timestep 3.

disturbance

Default = 1. 1 creates a prolonged or press disturbance as per bbn.predict Essentially prior values for each manipulated node are at least maintained (if not increased through reinforcement in the model) over all timesteps. 2 shows a brief pulse disturbance, which can be useful to visualise changes as peaks and troughs in increase and decrease of nodes can propagate through the network.

threshold

Nodes which deviate from 0 by more than this threshold value will display interactions with other nodes. Default = 0.2. Values in these visualisation functions don’t directly correspond to those in bbn.predict. This value can be tweaked from 0 to 4 to create the most useful visualisations.

font.size

Changes the font in the figure produced. Default = 0.7. The value here is a multiplier of the default font size used in the igraph package and does not correspond to the font.size argument in the bbn.timeseries.

arrow.size

Changes the size of the arrows. Default = 4. Note, sizes do vary based on interaction strength, so this is a multiplier for visualisation purposes.

Value

A plot of the BBN, illustrating the dynamic interactions between nodes over the specified timesteps.

Examples

data(my_BBN, combined)
bbn.visualise(bbn.model = my_BBN, priors1 = combined,
  timesteps=6, disturbance=1, threshold=0.2, font.size=0.7, arrow.size=4)