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"
min.responses
minimum number of responses
...
Further optional arguments to be passed.