Likefun: Likelihood Function of the NB-Beta Model

In EBSeq: An R package for gene and isoform differential expression analysis of RNA-seq data

Description

'Likefun' specifies the Likelihood Function of the NB-Beta Model.

Usage

Likefun(ParamPool, InputPool)

Arguments

ParamPool	The parameters that will be estimated in EM.
InputPool	The control parameters that will not be estimated in EM.

Value

The function will return the log-likelihood.

Author(s)

Ning Leng

References

Ning Leng, John A. Dawson, James A. Thomson, Victor Ruotti, Anna I. Rissman, Bart M.G. Smits, Jill D. Haag, Michael N. Gould, Ron M. Stewart, and Christina Kendziorski. EBSeq: An empirical Bayes hierarchical model for inference in RNA-seq experiments. Bioinformatics (2013)

Examples

#x1 = c(.6,.7,.3)
#Input = matrix(rnorm(100,100,1), ncol=10)
#RIn = matrix(rnorm(100,200,1), ncol=10)
#InputPool = list(Input[,1:5], Input[,6:10], Input,
#	rep(.1,100), 1, RIn, RIn[,1:5], RIn[,6:10], 100) 
#Likefun(x1, InputPool)

Example output

Loading required package: blockmodeling
To cite package 'blockmodeling' in publications please use package
citation and (at least) one of the articles:

  Žiberna, Aleš (2007). Generalized blockmodeling of valued networks.
  Social Networks 29(1), 105-126.

  Žiberna, Aleš (2008). Direct and indirect approaches to blockmodeling
  of valued networks in terms of regular equivalence. Journal of
  Mathematical Sociology 32(1), 57–84.

  Žiberna, Aleš (2014). Blockmodeling of multilevel networks. Social
  Networks 39, 46–61. https://doi.org/10.1016/j.socnet.2014.04.002.

  Žiberna, Aleš (2020).  Generalized and Classical Blockmodeling of
  Valued Networks, R package version 1.0.0.

To see these entries in BibTeX format, use 'print(<citation>,
bibtex=TRUE)', 'toBibtex(.)', or set
'options(citation.bibtex.max=999)'.
Loading required package: gplots

Attaching package: ‘gplots’

The following object is masked from ‘package:stats’:

    lowess

Loading required package: testthat

EBSeq documentation built on Nov. 8, 2020, 6:52 p.m.