noisy_or: Calculating the probability of transition from multiple nodes...

In dagHMM: Directed Acyclic Graph HMM with TAN Structured Emissions

Calculating the probability of transition from multiple nodes to given node in the dag

Description

Calculating the probability of transition from multiple nodes to given node in the dag

Usage

noisy_or(hmm, prev_state, cur_state)

Arguments

hmm	Object of class List given as output by initHMM,
prev_state	vector containing state variable values for the previous nodes
cur_state	character denoting the state variable value for current node

Value

The Noisy_OR probability for the transition

Examples

library(bnlearn)

tmat = matrix(c(0,0,1,0,0,0,0,1,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0),
               5,5, byrow= TRUE ) #for "X" (5 nodes) shaped dag
states = c("P","N") #"P" represent cases(or positive) and "N" represent controls(or negative)
bnet = model2network("[A][C|A:B][D|A:C][B|A]") #A is the target variable while
                                               #B, C and D are covariates.
obsvA=data.frame(list(B=c("L","H","H","L","L"),C=c("H","H","L","L","H"),D=c("L","L","L","H","H")))
hmmA = initHMM(States=states, dagmat= tmat, net=bnet, observation=obsvA)
Transprob = noisy_or(hmm=hmmA,prev_state=c("P","N"),cur_state="P") #for transition from P & N
                                                                   #simultaneously to P

dagHMM documentation built on Jan. 11, 2023, 1:13 a.m.