vbgmm: Variational Bayesian Gaussian mixture model (VB-GMM)

In TargetScore: TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information

Description

Given a N x D matrix of N observations and D variables, compute VB-GMM via VB-EM.

Usage

vbgmm(data, init = 2, prior, tol = 1e-20, maxiter = 2000, mirprior = TRUE, expectedTargetFreq = 0.01, verbose = FALSE)

Arguments

data	N x D numeric vector or matrix of N observations (rows) and D variables (columns)
init	Based on the dimension, init is expected to be one of the followings: scalar: number of components; vector: intial class labels; matrix: initialize with a D x K matrix for D variables and K components.
prior	A list containing the hyperparameters including alpha (Dirichlet), m (Gaussian mean), kappa (Gaussian variance), v (Wishart degree of freedom), M (Wishart precision matrix).
tol	Threshold that defines termination/convergence of VB-EM when abs(L[t] - L[t-1])/abs(L[t]) < tol
maxiter	Scalar for maximum number of EM iterations
mirprior	Boolean to indicate whether to use expectedTargetFreq to initialize alpha0 for the hyperparameters of Dirichlet.
expectedTargetFreq	Expected target frequence within the gene population. By default, it is set to 0.01, which is consistent with the widely accepted prior knoweldge that 200/20000 targets per miRNA.
verbose	Boolean indicating whether to show progress in terms of lower bound (vbound) of VB-EM (default: FALSE)

Details

The function implements variation Bayesian multivariate GMM described in Bishop (2006). Please refer to the reference below for more details. This is the workhorse of targetScore. Alternatively, user can choose to apply this function to other problems other than miRNA target prediction.

Value

A list containing:

label	a vector of maximum-a-posteriori (MAP) assignments of latent discrete values based on the posteriors of latent variables.
R	N x D matrix of posteriors of latent variables
mu	Gaussian means of the latent components
full.model	A list containing posteriors R, logR, and the model parameters including alpha (Dirichlet), m (Gaussian mean), kappa (Gaussian variance), v (Wishart degree of freedom), M (Wishart precision matrix)
L	A vector of variational lower bound at each EM iterations (should be strictly increasing)

Author(s)

Yue Li

References

Mo Chen (2012). Matlab code for Variational Bayesian Inference for Gaussian Mixture Model. http://www.mathworks.com/matlabcentral/fileexchange/35362-variational-bayesian-inference-for-gaussian-mixture-model

Bishop, C. M. (2006). Pattern recognition and machine learning. Springer, Information Science and Statistics. NY, USA. (p474-486)

See Also

targetScore

Examples

X <- c(rnorm(100,mean=2), rnorm(100,mean=3))
tmp <- vbgmm(X, tol=1e-3)
names(tmp)

Example output

Loading required package: pracma
Loading required package: Matrix

Attaching package: 'Matrix'

The following objects are masked from 'package:pracma':

    expm, lu, tril, triu

Running VB-GMM on a 200-by-1 data ...

Converged in 11 steps.

[1] "label"      "R"          "mu"         "full.model" "L"         

TargetScore documentation built on Nov. 8, 2020, 6:56 p.m.