bayesvl plot utilities {bayesvl}

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 = 0.89, params = NULL)

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

bvl_plotPairs (dag, params = NULL, fun = "stat", stat = "mean", 
  prob = 0.8, prob_outer = 0.95, 
  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_plotAcfs ( dag, params = NULL, row = 2, col = 2)

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.

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 and worked examples, and other tutorial information visit the References section on our Github:

• github.com/sshpa/bayesvl/master/References/bvl_ug_en08.pdf

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)