predict: Predict a Future State

In rEMM: Extensible Markov Model for Modelling Temporal Relationships Between Clusters

Predict a Future State

Description

Predict a state or the probability distribution over states in n time steps.

Usage

## S4 method for signature 'TRACDS'
predict(object, current_state = NULL, n=1, 
	probabilities = FALSE, randomized = FALSE, prior=FALSE)

Arguments

object	an "EMM"/"TRACDS" object.
current_state	use a specified current state. If NULL, the EMM's current state is used.
n	number of time steps.
probabilities	if TRUE, instead of the predicted state, the probability distribution is returned.
randomized	if TRUE, the predicted state is choosen randomly with a selection probability proportional to its transition probability
prior	add one to each transition count. This is equal to starting with a uniform prior for the transition count distribution, i.e. initially all transitions are equally likely. It also prevents the product of probabilities to be zero if a transition was never observed.

Details

Prediction is done using A^n where A is the transition probability matrix maintained by the EMM. Random tie-breaking is used.

Value

The name of the predicted state or a vector with the probability distribution over all states.

See Also

transition_matrix

Examples

data("EMMTraffic")
emm <- EMM(measure="eJaccard", threshold=0.2)
emm <- build(emm, EMMTraffic)

#plot(emm) ## plot graph

## Predict state starting an state 1 after 1, 2 and 100 time intervals
## Note, state 7 is an absorbing state.
predict(emm, n=1, current_state="1")
predict(emm, n=2, current_state="1")
predict(emm, n=100, current_state="1")

## Get probability distribution
predict(emm, n=2, current_state="1", probabilities = TRUE)

rEMM documentation built on June 26, 2022, 1:06 a.m.