MBASIC.binary.all: Bayesian clustering model for binary state matrix with prior...

In chandlerzuo/mbasic: Matrix Based Analysis for State-space Inference and Clustering.

Description

Bayesian clustering model for binary state matrix with prior estimated background means.

Usage

MBASIC.binary.fitall(Y, Mu0, fac, allJ = NULL, allStruct = NULL,
  zeta = 0.2, maxitr = 1000, burnin = 100, family = "negbin",
  tol = 1e-04, nsig, min.count = 1, parent.id, outdir, ncores = 1)

Arguments

Y	An N by I matrix containing the data from N experiments across I observation units (loci).
Mu0	An N by I matrix for the prior estimated mean for the background state, for N experiments across the I observation units (loci).
fac	A vector of length N denoting the experimental condition for each replicate.
allJ	A list for the numbers of clusters for each candidate model.
allStruct	A list of matrices indicating the levels of the signal matrix.
zeta	The initial value of the proportion of unclustered units. Default: 0.2.
maxitr	The maximum number of iterations in the E-M algorithm. Default: 100.
burnin	An integer value for the number of iterations in initialization. Default: 20.
family	The distribution of family to be used. Either "lognormal" or "negbin". See details for more information.
tol	Tolerance for error in checking the E-M algorithm's convergence. Default: 1e-04.
nsig	The number of mixture components for the distribution of the signal state.
parent.id	A vector for the identifier of the parent model for each candidate model. The parent model has a more generalized structure compared to the child model, therefore, its likelihood should be smaller.
outdir	The file directory for writing the intermediate results every 10 E-M iterations. This can be useful when the running time until final convergence is long. Default: NULL (no intermediate result is saved).
ncores	The number of parallel sessions used.

Details

TODO

Value

A 'MBASICFit' class object.

Author(s)

Chandler Zuo [e-mail]

chandlerzuo/mbasic documentation built on May 13, 2019, 3:24 p.m.