stan: Build RStan models from directed acyclic graph

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

Build RStan models from directed acyclic graph

Description

Build Stan models from directed acyclic graph of an object of class bayesvl.

Usage

# build Stan models from directed acyclic graph.
bvl_model2Stan(dag, ppc = "")

# compile and simulate samples from the model.
bvl_modelFit(dag, data, warmup = 1000, iter = 5000, chains = 2, ppc = "", ...)

# summarize the stan priors used for the model.
bvl_stanPriors(dag)

# summarize the stan parameters used for the model.
bvl_stanParams(dag)

# summarize the generated formula at the node.
bvl_formula(dag, nodeName, outcome = T, re = F)

Arguments

dag	an object of class bayesvl
data	a data frame or list containing the data
warmup	Optional: Number of warmup iterations. By default, half of iter
iter	Optional: Number of iterations of sampling. Default is 5000
chains	Optional: Number of independent chains to sample from. Default is 2
ppc	Optional: a character string contains posterior predictive check scripts
...	extra arguments from the generic method
nodeName	A character string contains the node name
outcome	Optional: Whether show out distribution
re	Optional: Whether run recursive for all up-level nodes

Value

bvl_model2Stan() return character string of rstan code generated from the model.

bvl_modelFit() return an object class bayesvl which contains result with the following slots.

model	Stan model code
stanfit	stanfit object returned by stan
standata	The data
pars	Parameter names monitored in samples
formula	Generated formula from the model

bvl_stanPriors() return character string of rstan priors generated from the model.

bvl_stanParams() return character string of rstan parameters generated from the model.

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

# Design the model in directed acyclic graph
model <- bayesvl()
model <- bvl_addNode(model, "Lie", "binom")
model <- bvl_addNode(model, "B", "binom")
model <- bvl_addNode(model, "C", "binom")
model <- bvl_addNode(model, "T", "binom")

model <- bvl_addArc(model, "B", "Lie", "slope")
model <- bvl_addArc(model, "C", "Lie", "slope")
model <- bvl_addArc(model, "T", "Lie", "slope")

# Generate the Stan model's code
model_string <- bvl_model2Stan(model)
cat(model_string)

# Show priors in generated Stan model
bvl_stanPriors(model)

sshpa/bayesvl documentation built on June 1, 2022, 12:50 a.m.