| runBayesNetApp {BayesNetBP} | R Documentation |
Launch the BayesNetBP Shiny App
Description
Launch the BayesNetBP Shiny App
Usage
runBayesNetApp(launch.browser = TRUE)
Arguments
launch.browser |
|
Details
The function runBayesNetApp lauches the Shiny App accompanied
with this package. The app loads the toytree example by default and allows users
to load customized ClusterTree object. In order to use this feature, a
ClusterTree object should be built, propagated and named tree.init.p, and
then saved as a .RDATA file. This file can be read in by the app.
The console of BayesNetBP Shiny App comprises three panels. The first
part controls the model loading, visualization and subnetwork selection. The Fit function fits
the entire graph in the window. The Fit Selected function fits the selected subnetwork to the window.
The user can subset the network for visualization. The Expand function can trace the one hop neighbor of
selected nodes in a stepwise manner.
After selecting desired node sets, the user can subset the graph by the Subset function.
The second panel is used for absorption of fixed and hard evidences.
The users can add multiple pieces of evidence to a list and absorb them into the model simultaneously.
Marginals of other nodes can be quried as
density or bar plots by node types. If a set of evidence has been absorbed, the marginals both
before and after absorption will be returned to facilitate comparison. To query the marginals, the user can
select the node of interest in the graph, and then click Marginal of Selected. The Shift in Marginals
function computes the signed and symmetric Kullback-Liebler divergence for all applicable nodes
in the network, and colors the nodes by their divergence and change in directions.
The function for systematic assessment of variable marginal shifts is provided in the third panel.
It allows user to specify which node to absorb the spectrum of evidence in the select menu and click Select Observed, and to select whose
divergence to be calculated by selecting the node in the menu and then clicking Add to Plot.
Alternatively, the user can use Add All function to select all applicable nodes into the plotting list.
The result is visualized in an interactive plot. The Min, Max and Step controls the range of values
of the evidence to be absorbed.
Author(s)
Han Yu
References
Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.
Yu H, Moharil J, Blair RH (2020). BayesNetBP: An R Package for Probabilistic Reasoning in Bayesian Networks. Journal of Statistical Software, 94(3), 1-31. <doi:10.18637/jss.v094.i03>.
Examples
## Not run:
# load or install required packages to run App
library("shiny")
library("googleVis")
library("devtools")
devtools::install_github("cytoscape/cyjShiny")
library("cyjShiny")
# run the App in browser
runBayesNetApp(launch.browser=TRUE)
## End(Not run)