# randindx: Generates random indexes with a specified probability... In RIPSeeker: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments

## Description

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

## Usage

 `1` ```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.

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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21``` ```# 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 <- 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) ```

RIPSeeker documentation built on Oct. 31, 2019, 7:29 a.m.