mixture.model: Mixture model
In antagomir/netresponse: Functional Network Analysis

mixture.model

R Documentation

Mixture model

Description

Fit Gaussian mixture model

Usage

mixture.model(
  x,
  mixture.method = "vdp",
  max.responses = 10,
  implicit.noise = 0,
  prior.alpha = 1,
  prior.alphaKsi = 0.01,
  prior.betaKsi = 0.01,
  vdp.threshold = 1e-05,
  initial.responses = 1,
  ite = Inf,
  speedup = TRUE,
  bic.threshold = 0,
  pca.basis = FALSE,
  min.responses = 1,
  ...
)

Arguments

`x`	data matrix (samples x features, for multivariate analysis) or a vector (for univariate analysis)
`mixture.method`	Specify the approach to use in mixture modeling. Options. vdp (nonparametric Variational Dirichlet process mixture model); bic (based on Gaussian mixture modeling with EM, using BIC to select the optimal number of components)
`max.responses`	Maximum number of responses for each subnetwork. Can be used to limit the potential number of network states.
`implicit.noise`	Implicit noise parameter. Add implicit noise to vdp mixture model. Can help to avoid overfitting to local optima, if this appears to be a problem.
`prior.alpha, prior.alphaKsi, prior.betaKsi`	Prior parameters for Gaussian mixture model that is calculated for each subnetwork (normal-inverse-Gamma prior). alpha tunes the mean; alphaKsi and betaKsi are the shape and scale parameters of the inverse Gamma function, respectively.
`vdp.threshold`	Minimal free energy improvement after which the variational Gaussian mixture algorithm is deemed converged.
`initial.responses`	Initial number of components for each subnetwork model. Used to initialize calculations.
`ite`	Maximum number of iterations on posterior update (updatePosterior). Increasing this can potentially lead to more accurate results, but computation may take longer.
`speedup`	Takes advantage of approximations to PCA, mutual information etc in various places to speed up calculations. Particularly useful with large and densely connected networks and/or large sample size.
`bic.threshold`	BIC threshold which needs to be exceeded before a new mode is added to the mixture with mixture.method = "bic"
`pca.basis`	pca.basis
`min.responses`	minimum number of responses
`...`	Further optional arguments to be passed.