decisionSupport (version 1.114)

sample_CPT: Sample a Conditional Probability Table

Description

This function randomly chooses a state of a categorical variable, based on a Conditional Probability Table (CPT; a component of Bayesian Network models) that expresses the probability of each possible state in relation to the states of other categorical variables. Given information on the state of all parent variables, the function uses the appropriate probability distribution to draw a random sample for the state of the variable of interest.

Usage

sample_CPT(CPT, states)

Value

one of the states of the child node belonging to the CPT.

Arguments

CPT

list of two data.frames: 1) Conditional Probability Table (CPT); 2) legend table specifying which states of the parent nodes belong to which column in the CPT. This can be generated with the make_CPT function, or specified manually (which can be cumbersome).

states

character vector containing (in the right sequence) state values for all parent variables.

Author

Eike Luedeling

Examples

test_CPT<-make_CPT(parent_effects=list(c(-1,3),c(-4,2),c(-2,3,4),c(1,2,3)),
                  parent_weights=c(1,1,1,1),b=2,child_prior=c(1,2,3,4,5),
                  child_states=c("a","b","c","d","e"),
                  parent_states=list(c("low","high"),c("A","B"),c(1,2,3),
                  c("Left","Right","Center")))

sample_CPT(CPT=test_CPT,states=c("low","A","2","Left"))