student.mixture: Student Mixture Model

In ArnaudDroitLab/RJMCMC: Bayesian hierarchical model for genome-wide profiling of nucleosome positions based on high-throughput short-read data (MNase-Seq data)

Description

Generation a value from a Student Mixture distribution.

Usage

student.mixture(i, k, weight, mu, sigma, dfr)

Arguments

i	an integer, a count parameter.
k	a positive integer value, the number of nucleosomes in the analyzed region.
weight	a vector of positive numerical of length k, the weight for each nucleosome. The sum of all weight values must be equal to 1. The length of weight must be equal to k.
mu	a vector of positive integer of length k, the positions of all the nucleosomes in the analyzed region. The length of weight must be equal to k.
sigma	a vector of numeric of length k, the variance for each nucleosome. The length of sigma must be equal to k.
dfr	a vector of numeric of length k, the degrees of freedom for each nucleosome. The length of dfr must be equal to k.

Value

a numerical, the value generated from a Student Mixture distribution.

Author(s)

Rawane Samb, Astrid Deschenes

Examples

## Return a value generated from a student mixture
RJMCMC:::student.mixture(i = 1L, k = 4L, weight = c(0.1, 0.3, 0.34, 0.26),
mu = c(12L, 15L, 25L, 44L), sigma = c(4, 7, 6, 5), dfr = c(5L, 3L, 12L, 4L))

ArnaudDroitLab/RJMCMC documentation built on May 5, 2019, 7:06 a.m.