Temporal Bayesian Dirichlet equivalent uniform (Score criterion)

A reference class for categorical observational data Scoring with Tiered Background Knowledge. This class represents a score for causal discovery using tiered background knowledge from observational categorical data; it is used in the causal discovery function tges().

Arguments

data: A numeric matrix with \(n\) rows and \(p\) columns. Each row corresponds to one observational realization.
nodes: A character vector of variable names corresponding to the columns of the data.
iss: Imaginary Sample Size (ISS), also referred to as Equivalent Sample Size (ESS), determines how much weight is assigned to the prior in terms of the size of an imaginary sample supporting it. Increasing the ISS will increase the density of the estimated graph.
knowledge: A Knowledge object.
debug: Logical; indicates whether to perform validation of the vertex and parents in every local score calculation. Setting this to TRUE will slow down the algorithm, but may be useful for debugging.

Details

The class implements a score which scores all edges contradicting the ordering (edge going from a later tier to an earlier) to minus \(\infty\). If the the edges does not contradict, the score is equal to that of the standard BDeu.

Extends

Class Score-class from pcalg, directly.

All reference classes extend and inherit methods from envRefClass.

Constructor


new(
 "TemporalBdeu",
 data = matrix(1, 1, 1),
 order =  rep(1,ncol(data)),
 iss = 1
 ...
)

Author

Tobias Ellegaard Larsen

Examples

# For reproducibility
set.seed(1405)

# Number of samples
n <- 1000

# Define probabilities for A
p_A <- c(0.4, 0.35, 0.25) # Probabilities for A = {1, 2, 3}

# Simulate A from a categorical distribution
A <- sample(1:3, n, replace = TRUE, prob = p_A)

# Define conditional probabilities for B given A
p_B_given_A <- list(
  c(0.7, 0.3), # P(B | A=1)
  c(0.4, 0.6), # P(B | A=2)
  c(0.2, 0.8) # P(B | A=3)
)

# Sample B based on A
B <- sapply(A, function(a) sample(1:2, 1, prob = p_B_given_A[[a]]))

# Define conditional probabilities for C given A and B
p_C_given_A_B <- list(
  "1_1" = c(0.6, 0.4), # P(C | A=1, B=1)
  "1_2" = c(0.3, 0.7), # P(C | A=1, B=2)
  "2_1" = c(0.5, 0.5), # P(C | A=2, B=1)
  "2_2" = c(0.2, 0.8), # P(C | A=2, B=2)
  "3_1" = c(0.7, 0.3), # P(C | A=3, B=1)
  "3_2" = c(0.4, 0.6) # P(C | A=3, B=2)
)

# Sample C based on A and B
C <- mapply(
  function(a, b) sample(1:2, 1, prob = p_C_given_A_B[[paste(a, b, sep = "_")]]),
  A,
  B
)

# Create dataset
simdata <- data.frame(as.factor(A), as.factor(B), as.factor(C))

# Define order in prefix way
colnames(simdata) <- c("child_A", "child_B", "adult_C")
prefix_order <- c("child", "adult")

# Define Knowledge object
kn <- knowledge(
  simdata,
  tier(
    child ~ tidyselect::starts_with("child"),
    adult ~ tidyselect::starts_with("adult")
  )
)

# Define TemporalBDeu score
t_score <- new("TemporalBDeu", knowledge = kn, data = simdata)
# Run tges
tges_pre <- tges_run(t_score)

# Plot MPDAG
# plot(tges_pre)