randindx {RIPSeeker} | R Documentation

## Generates random indexes with a specified probability distribution

### Description

Returns an array of T indexes distributed as specified by p (which should be a normalized probability vector).

### Usage

    randindx(p, Total, NO_CHK)

### Arguments

`p` | A row vector of normalized probabilities that dictate the transition probability from the current state to the next state. For example, p = [0.2, 0.8] indicates that the current state transitoins to state 1 at 0.2 and 2 at 0.8. The current state itself can either be the state 1 or 2.
`Total` | Total number of states needed to be generated using the input transition vector.
`NO_CHK` | Check whether the first argument is a valid row vector of normalized probabilities.

### Details

The function is used by `nbh_gen` to generate random data point based on the user-supplied transition probability matrix.

### Value

`I` | Index/Indices or state(s) sampled following the transition.

probability.

### Author(s)

Yue Li

### References

Capp\'e, O. (2001). H2M : A set of MATLAB/OCTAVE functions for the EM estimation of mixtures and hidden Markov models. (http://perso.telecom-paristech.fr/cappe/h2m/)

### See Also

`nbh_gen`

### Examples

    # Total contains the length of data to simulate
    Total <- 100

    # number of states
    N <- 2

    # transition probabilities between states
    TRANS <- matrix(c(0.9, 0.1, 0.3, 0.7), nrow=2, byrow=TRUE)

    label <- matrix(0, Total, 1)

    # Simulate initial state
    label[1] <- randindx(matrix(1,ncol=N)/N, 1, 1)

    # Use Markov property for the following time index
    for(t in 2:Total) {

    	label[t] <- randindx(TRANS[label[t-1],], 1, 1)
    }

    plot(label)

[Package RIPSeeker version 1.22.0 Index]