mlMixModel: Fits a mixture model using the maximum likelihood principle.

In epigenomix: Epigenetic and gene transcription data normalization and integration with mixture models

Description

This method calculates the maximum likelihood estimations of a mixture model using the expectation-maximization (EM) algorithm. The mixture model may consists of normal components (with a fixed expectation of 0) and exponential components, which may be mirrored in order to model negative values.

Usage

mlMixModel(z, normNull = c(), expNeg = c(), expPos = c(), sdNormNullInit = c(), rateExpNegInit = c(), rateExpPosInit = c(), piInit = c(), maxIter = 500, tol = 0.001)

Arguments

z	Observed values.
normNull	Indices of the normal components (that have mu=0).
expNeg	Indices of the mirrored exponential components.
expPos	Indices of the exponential components.
sdNormNullInit	Initial standard deviations of the normal components.
rateExpNegInit	Initial rates ("lambda") of the exponential components.
rateExpPosInit	Initial rates ("lambda") of the exponential components.
piInit	Initial weights of the components.
maxIter	Maximum number of iterations.
tol	Threshold for convergence. The minimum log likelihood gain between two iterations that must be achieved to continue.

Details

The EM algorithm is known to converge slowly in some cases and local maxima may avoid finding the optimal solution. Users should try different initial values and different convergence criteria.

The components' indices do not influence the result, but may influence the order in which components are listed or ploted by downstream methods. Indices must be successive integers from 1 to n.

Value

An object of MixModelML-class storing results, data, initial values and information about the convergence.

Author(s)

Hans-Ulrich Klein ([e-mail])

See Also

MixModelML-class

Examples

z <- c(rnorm(1000, 0, 0.5), rnorm(1000, 0, 1))
mm <- mlMixModel(z, normNull=1:2, sdNormNullInit=c(0.1, 0.2),
   pi=c(1/2, 1/2), maxIter=500, tol=0.001)
mm

z <- c(rnorm(1000, 0, 3), rnorm(1000, 0, 5), rexp(1000, 5), -rexp(1000, 5))
mm <- mlMixModel(z, normNull=1:2, expNeg=3, expPos=4,
   sdNormNullInit=c(1, 2), rateExpNegInit=8, rateExpPosInit=8,
   pi=c(1/4, 1/4, 1/4, 1/4), maxIter=500, tol=0.001)
mm

epigenomix documentation built on Nov. 8, 2020, 5:24 p.m.