nbh_gen: Simulate data from a negative binomial HMM.

In RIPSeeker: RIPSeeker: a statistical package for identifying protein-associated transcripts from RIP-seq experiments

Description

Generate count data and the hidden states based on the user-supplied HMM paramters.

Usage

nbh_gen(TRANS, alpha, beta, Total)

Arguments

TRANS	Expected a squared matrix of probabilities (0 ≤ p ≤ 1) with row and column length equal to that of alpha and beta and row sum and column sum both equal to 1 (within some numerical deviation of 1e-6).
alpha	Expected a vector of positive values with length equal to that of beta and the row/column of TRANS.
beta	Expected a vector of positive values with length equal to that of alpha and the row/column of TRANS.
Total	Total number of data points to generate.

Value

A list containing:

count	Simulation count data.
label	Hidden states associated with the simulated data.

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_em, nbm_chk, randindx

Examples

# Simulate data using user-supplied transition prob, alpha and beta for the NB HMM parameters
TRANS_s <- matrix(c(0.9, 0.1, 0.3, 0.7), nrow=2, byrow=TRUE)
alpha_s <- c(2, 4)
beta_s  <- c(1, 0.25)
Total <- 100
x <- nbh_gen(TRANS_s, alpha_s, beta_s, Total)

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