predict: Predict a Future State

Description

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

Usage

# S4 method for 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.

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.

Value

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

Details

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

Examples

Run this code

# NOT RUN {
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)
# }