Create a predict function for use of a bnlearn Bayesian network in the search algorithms.

Create a predict function for use of a bnlearn Bayesian network in the search algorithms.

Usage

bnlearn_predict_wrapper(
  bn,
  node,
  method,
  threshold = NULL,
  default = NULL,
  n = 500
)

Arguments

bn

Fitted Bayesian network

node

Name of target node to be predicted

method

Same options as in parameter 'method' in predict function of bnlearn

threshold

Minimum value of posterior probability of the MLE to be the output of the algorithm. If parameter 'default' is null, the MLE will be the output even if it doesn't surpass the threshold.

default

Default predicted target value when threshold isn't exceeded by the MLE

n

Same options as in parameter 'n' in predict function of bnlearn

Value

Function to be used in algorithms such as bfs_sfx

Examples

library(bnlearn)
set.seed(40)
download.file("https://www.bnlearn.com/bnrepository/child/child.rda",
  "child.rda", "auto",
  quiet = TRUE
)
load("child.rda") # Load CHILD Bayesian Network
evidence <- data.frame(
  LVHReport = factor(x = "yes", levels = dimnames(bn$LVHreport$prob)[[1]]),
  LowerBodyO2 = factor(x = "5-12", levels = dimnames(bn$LowerBodyO2$prob)[[1]]),
  CO2Report = factor(x = "<7.5", levels = dimnames(bn$CO2Report$prob)[[1]]),
  XrayReport = factor(x = "Oligaemic", levels = dimnames(bn$XrayReport$prob)[[1]])
)
outcome <- predict(
  object = bn, node = bn$Disease$node, data = evidence,
  method = "bayes-lw"
)
expected <- factor("PAIVS", levels = levels(outcome))
predict_f <- bnlearn_predict_wrapper(bn, bn$Disease$node, "bayes-lw",
  threshold = 0.20, default = "TGA"
)
print(bfs_sfx(predict_f, evidence, outcome, expected))
#> [[1]]
#> [[1]]$LVHReport
#> [1] yes
#> Levels: yes no
#> 
#> [[1]]$XrayReport
#> [1] Oligaemic
#> Levels: Normal Oligaemic Plethoric Grd_Glass Asy/Patchy
#> 
#>