plots: Plot utilities for bayesvl objects

In bayesvl: Visually Learning the Graphical Structure of Bayesian Networks and Performing MCMC with 'Stan'

Plot utilities for bayesvl objects

Description

Provides plot methods and the interface to the MCMC module in the bayesplot package for plotting MCMC draws and diagnostics for an object of class bayesvl.

Usage

# Plot network diagram to visualize the model
bvl_bnPlot(dag, ...)

# Plots historgram of regression parameters computed from posterior draws in grid layout
bvl_plotParams (dag, row = 2, col = 2, credMass = 0.95, params = NULL)

# The interface to mcmc_intervals for plotting uncertainty intervals
# computed from posterior draws
bvl_plotIntervals (dag, params = NULL, fun = "mean", prob = 0.8, 
  prob_outer = 0.95, color_scheme = "blue", labels = NULL)

# The interface to mcmc_intervals for plotting density computed from posterior draws
bvl_plotAreas (dag, params = NULL, fun = "mean", 
  prob = 0.8, prob_outer = 0.95, color_scheme = "blue", labels = NULL)

bvl_plotPairs (dag, params = NULL, size = 1, color_scheme = "blue", labels = NULL)

bvl_plotDensity (dag, params = NULL, size = 1, labels = NULL)

bvl_plotDensity2d(dag, x, y, color = NULL, color_scheme = "red", labels = NULL)

bvl_plotTrace (dag, params = NULL)

bvl_plotDiag (dag)

bvl_plotGelman (dag, params = NULL)

bvl_plotGelmans (dag, params = NULL, row = 2, col = 2)

bvl_plotAc ( dag, params = NULL)

bvl_plotAcf ( dag, params = NULL)

bvl_plotAcfs ( dag, params = NULL, row = 2, col = 2)

bvl_plotAcf_Bar ( dag, params = NULL, color_scheme="pink",labels=NULL)

bvl_plotDensOverlay (dag, n = 200, color_scheme = "blue")

bvl_plotMCMCDiag ( dag, parName, saveName=NULL , saveType="jpg")

bvl_plotPPC (dag, fun = "stat", stat = "mean", color_scheme = "blue")

bvl_plotTest (dag, y_name, test_name, n = 200, color_scheme = "blue")

Arguments

dag	an object of class bayesvl
params	Optional: character vector of parameter names.
fun	Optional: statistic function.
stat	Optional: the plotting function to call.
prob	Optional: the probability mass to include in the inner interval. Default is 0.8.
prob_outer	Optional: the probability mass to include in the outer interval. Default is 0.95.
row	Optional: number of rows of grid layout.
col	Optional: number of columns of grid layout.
credMass	Optional: specifying the mass within the credible interval. Default is 0.89.
size	Optional: the size of line width.
color_scheme	Optional: color scheme. Default is "blue"
...	extra arguments from the generic method
y_name	a character string. Name of outcome variable
test_name	a character string. Name of test variable and test value
n	number of yrep values to plot
x	a character string. Name of x parameter to pair with
y	a character string. Name of y parameter to pair with
color	a character string. Variable for color of points on density plot
labels	Optional: character vector of parameter labels.
parName	parameter name for plotting.
saveName	file name for exporting plot.
saveType	type of file name for exporting plot (default is 'jpg').

Value

bvl_plotIntervals(), bvl_plotPairs() return a ggplot object that can be further customized using the ggplot2 package.

Author(s)

La Viet-Phuong, Vuong Quan-Hoang

References

For documentation, case studies, worked examples, and other tutorial materials, visit the References section on our GitHub:

• https://github.com/sshpa/bayesvl/tree/master/References

For case studies using the package in research articles, see:

• https://www.nature.com/articles/s41599-020-0442-3

Examples

## create network model
model <- bayesvl()
## add the observed data nodes
model <- bvl_addNode(model, "O", "binom")
model <- bvl_addNode(model, "Lie", "binom")
model <- bvl_addNode(model, "Viol", "binom")
model <- bvl_addNode(model, "VB", "binom")
model <- bvl_addNode(model, "VC", "binom")
model <- bvl_addNode(model, "VT", "binom")
model <- bvl_addNode(model, "Int1", "binom")
model <- bvl_addNode(model, "Int2", "binom")

## add the tranform data nodes and arcs as part of the model
model <- bvl_addNode(model, "B_and_Viol", "trans")
model <- bvl_addNode(model, "C_and_Viol", "trans")
model <- bvl_addNode(model, "T_and_Viol", "trans")
model <- bvl_addArc(model, "VB",        "B_and_Viol", "*")
model <- bvl_addArc(model, "Viol",      "B_and_Viol", "*")
model <- bvl_addArc(model, "VC",        "C_and_Viol", "*")
model <- bvl_addArc(model, "Viol",      "C_and_Viol", "*")
model <- bvl_addArc(model, "VT",        "T_and_Viol", "*")
model <- bvl_addArc(model, "Viol",      "T_and_Viol", "*")
model <- bvl_addArc(model, "B_and_Viol",  "O", "slope")
model <- bvl_addArc(model, "C_and_Viol",  "O", "slope")
model <- bvl_addArc(model, "T_and_Viol",  "O", "slope")

model <- bvl_addArc(model, "Viol",   "O", "slope")

model <- bvl_addNode(model, "B_and_Lie", "trans")
model <- bvl_addNode(model, "C_and_Lie", "trans")
model <- bvl_addNode(model, "T_and_Lie", "trans")
model <- bvl_addArc(model, "VB",       "B_and_Lie", "*")
model <- bvl_addArc(model, "Lie",      "B_and_Lie", "*")
model <- bvl_addArc(model, "VC",       "C_and_Lie", "*")
model <- bvl_addArc(model, "Lie",      "C_and_Lie", "*")
model <- bvl_addArc(model, "VT",       "T_and_Lie", "*")
model <- bvl_addArc(model, "Lie",      "T_and_Lie", "*")
model <- bvl_addArc(model, "B_and_Lie",  "O", "slope")
model <- bvl_addArc(model, "C_and_Lie",  "O", "slope")
model <- bvl_addArc(model, "T_and_Lie",  "O", "slope")

model <- bvl_addArc(model, "Lie",   "O", "slope")

model <- bvl_addNode(model, "Int1_or_Int2", "trans")
model <- bvl_addArc(model, "Int1", "Int1_or_Int2", "+")
model <- bvl_addArc(model, "Int2", "Int1_or_Int2", "+")

model <- bvl_addArc(model, "Int1_or_Int2", "O", "varint")

## Plot network diagram to visualize the model
bvl_bnPlot(model)

bayesvl documentation built on June 8, 2025, 10:38 a.m.