inst/doc/model_specification.R

In abn: Modelling Multivariate Data with Additive Bayesian Networks

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

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

## -----------------------------------------------------------------------------
#  # Load only a subset of the example data for illustration
#  mydat <- ex1.dag.data[, c("b1", "p1", "g1", "b2", "p2", "b3", "g2")]
#  mydists <- list(b1="binomial",
#                  p1="poisson",
#                  g1="gaussian",
#                  b2="binomial",
#                  p2="poisson",
#                  b3="binomial",
#                  g2="gaussian")
#  
#  # Estimate model score for different maximum numbers of parent nodes
#  num.vars <- ncol(mydat) # number of variables
#  max.pars <- 1:(num.vars-1) # vector of possible maximum number of parent nodes
#  
#  npars_scores <- data.frame(max.pars = max.pars, score = rep(NA, length(max.pars))) # data frame to store scores
#  
#  # loop over maximum number of parent nodes
#  for (i in max.pars) {
#    mycache <- buildScoreCache(data.df = mydat,
#                               data.dists = mydists,
#                               method = "bayes",
#                               max.parents = i)
#    mp.dag <- mostProbable(mycache)
#    myfit <- fitAbn(mp.dag)
#  
#    npars_scores[i, "score"] <- myfit$mlik # store score
#  }
#  
#  # Plot the scores for different maximum numbers of parent nodes
#  library(ggplot2)
#  ggplot(npars_scores, aes(x = max.pars, y = score)) +
#    geom_point() +
#    geom_line() +
#    labs(x = "Maximum number of parent nodes", y = "Model score") +
#    # set x-axis labels to integers
#    scale_x_continuous(breaks = seq(0, num.vars, 1))

## -----------------------------------------------------------------------------
#  # Load the example data
#  mydat <- ex1.dag.data
#  mydists <- list(b1="binomial",
#                  p1="poisson",
#                  g1="gaussian",
#                  b2="binomial",
#                  p2="poisson",
#                  b3="binomial",
#                  g2="gaussian",
#                  b4="binomial",
#                  b5="binomial",
#                  g3="gaussian")
#  
#  # Define edges and their directions as fixed or forbidden
#  dag.banned <- matrix(0, nrow = 10, ncol = 10, dimnames = list(names(mydat), names(mydat)))
#  
#  # Define edges and their directions as forbidden
#  dag.banned["b1", "b2"] <- 1
#  dag.banned["b1", "b3"] <- 1
#  dag.banned["b1", "b4"] <- 1
#  
#  # Display the matrix
#  dag.banned
#  
#  # Plot the forbidden edges
#  plotAbn(dag = dag.banned, data.dists = mydists)