predict
From rEMM v1.011
by Michael Hahsler
Predict a Future State
Predict a state or the probability distribution over states in $n$ time steps.
 Keywords
 models
Usage
"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 tiebreaking is used.
Value

The name of the predicted state or a vector with the probability
distribution over all states.
See Also
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)
Community examples
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