inst/doc/quick_start_example.R

In abn: Modelling Multivariate Data with Additive Bayesian Networks

## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  eval = FALSE
)

## ----setup--------------------------------------------------------------------
#  library(abn)

## -----------------------------------------------------------------------------
#  mydat <- ex1.dag.data
#  str(mydat)

## -----------------------------------------------------------------------------
#  mydists <- list(b1="binomial",
#                  p1="poisson",
#                  g1="gaussian",
#                  b2="binomial",
#                  p2="poisson",
#                  b3="binomial",
#                  g2="gaussian",
#                  b4="binomial",
#                  b5="binomial",
#                  g3="gaussian")

## -----------------------------------------------------------------------------
#  # max.par <- list("b1"=1,"p1"=2,"g1"=3,"b2"=4,"p2"=1,"b3"=2,"g2"=3,"b4"=4,"b5"=1,"g3"=2) # set different max parents for each node
#  max.par <- 4 # set the same max parents for all nodes

## -----------------------------------------------------------------------------
#  mycache <- buildScoreCache(data.df = mydat,
#                             data.dists = mydists,
#                             method = "bayes", # the default method is "bayes"
#                             max.parents = max.par)

## -----------------------------------------------------------------------------
#  mp.dag <- mostProbable(score.cache = mycache)

## -----------------------------------------------------------------------------
#  plot(mp.dag)

## -----------------------------------------------------------------------------
#  myfit <- fitAbn(object = mp.dag)

## -----------------------------------------------------------------------------
#  summary(myfit)
#  plot(myfit)

## -----------------------------------------------------------------------------
#  simdat <- simulateAbn(object = myfit,
#                        n.iter = 10000L)
#  summary(simdat)