# predict: Predict a Future State In rEMM: Extensible Markov Model for Modelling Temporal Relationships Between Clusters

 predict R Documentation

## 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.

`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.