nbh.GRanges {RIPSeeker} | R Documentation

## Optimize HMM parameters based on the read counts on a chromosome

### Description

Inheritance function from `nbh` that receives an object of GRanges class with additional column of read counts (for each strand) and call `nbh.integer` to derive the most probable sequence of hidden states

### Usage

    ## S3 method for class 'GRanges'
    nbh(x, K, ...)

### Arguments

`x` | GRanges with 'values' slot used for bin counts in 1D vector of integers.
`K` | Number of hidden states.
`...` | Extra arguments passed to nbh.integer for the actual HMM computation.

### Value

`binGR` | GRanges of bin counts with metadata slot saved for the optimized HMM parameters including alpha, beta for the K negative binomial mixture components and TRANS (the transition probabilities)

### Author(s)

Yue Li

### References

Rabiner, L. R. (1989). A tutorial on hidden Markov models and selected applications in speech recognition (Vol. 77, pp. 257-286). Presented at the Proceedings of the IEEE. doi:10.1109/5.18626

Bishop, Christopher. Pattern recognition and machine learning. Number 605-631 in Information Science and Statisitcs. Springer Science, 2006.

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

`mainSeekSingleChrom, nbh.integer`

### Examples

    if(interactive()) ?nbh # see nbh for example of nbh running on GRanges object

[Package RIPSeeker version 1.22.0 Index]