make_bnlearn_evidence: Create evidence for Bayesain network query

In Issoufou-Liman/decisionSupportExtra: Miscellaneous Functions for Quantitative Support of Decision Making under Uncertainty

Description

Take a fully specified bayesian network, a list nodes states names after the node names or character vector of node names, and return a list of query formated following the bnlearn package specifications.

Usage

make_bnlearn_evidence(bn, evidence)

Arguments

bn	an object of class bn.fit or grain-main.
evidence	list or character vector specify the states or nodes based on which the evidences are to be formated.

Author(s)

Issoufou Liman

Examples

library (gRain)
library(bnlearn)
## setting a bayesian network with gRain
Soil_type <- cptable (~Soil_type, values = c(0.05, 0.55, 0.4),
levels = c('Sandy', 'Loamy', 'Clayey'))
Manure_application <- cptable(~Manure_application, values = c(0.3, 0.7),
levels = c('FALSE', 'TRUE'))
Soil_water_holding_capacity_tmp <- make_gRain_CPT(
 parent_effects = list(c(0, 2.5, 3), c(0, 2)),
 parent_weights = c(2,1),
 b = 3,
 child_prior = c(0.2,0.5,0.3),
 child_states = c('Low', 'Medium', 'High'),
 parent_states = list(c('Sandy', 'Loamy', 'Clayey'), c('FALSE', 'TRUE'))
)
Soil_water_holding_capacity_values <- Soil_water_holding_capacity_tmp$values
Soil_water_holding_capacity_levels <- Soil_water_holding_capacity_tmp$levels
Soil_water_holding_capacity <- cptable (
~Soil_water_holding_capacity|Soil_type:Manure_application,
values = Soil_water_holding_capacity_values,
levels = Soil_water_holding_capacity_levels)
## Compile conditional probability tables
network <- compileCPT(list(Soil_type, Manure_application, Soil_water_holding_capacity))
## Graphical Independence Network ####
network <- grain(network)
## Use grain object (gRain package)
make_bnlearn_evidence (bn = network, evidence = c('Soil_type', 'Manure_application'))
make_bnlearn_evidence (bn = network,
evidence = list(Soil_type = c('Sandy', 'Loamy', 'Clayey'), Manure_application = c('FALSE', 'TRUE')))
make_bnlearn_evidence (bn = network,
evidence = list(Soil_type = 'Sandy', Manure_application = c('FALSE', 'TRUE')))

## converting the grain bayesian network to bn.fit
network_bn_fit <- as.bn.fit(network)
## Use bn.fit object (bnlearn package)
make_bnlearn_evidence (bn = network_bn_fit, evidence = c('Soil_type', 'Manure_application'))
make_bnlearn_evidence (bn = network_bn_fit,
evidence = list(Soil_type = c('Sandy', 'Loamy', 'Clayey'), Manure_application = c('FALSE', 'TRUE')))
make_bnlearn_evidence (bn = network_bn_fit,
evidence = list(Soil_type = 'Sandy', Manure_application = c('FALSE', 'TRUE')))

Issoufou-Liman/decisionSupportExtra documentation built on Dec. 21, 2020, 6:28 p.m.