# R/GaussianMixtures.R In Lucaweihs/sBIC: Computing the Singular BIC for Multiple Models

```#' @include MixtureModels.R
NULL
#' Construct a poset of gaussian mixture models.
#'
#' Creates an object representing a collection of gaussian mixture models. There
#' is one model for each fixed number of components from 1 to some specified
#' maximum. In particular each model is identified by a single number
#' specifiying the number of components in the model. Models are naturally
#' ordered by inclusion so that, for example, a model with 2 components comes
#' before a model with 3 or more components.
#'
#' @name GaussianMixtures
#' @export
#'
#' @param maxNumComponents the maximum number of gaussian components to
#'                         consider in a mixture.
#' @param dim the ambient dimension in which the gaussian mixtures reside.
#'        Default is 1, corresponding to gaussian mixtures on the real line.
#' @param phi parameter controlling the strength of the sBIC penalty.
#'
#' @param restarts the number of random restarts to perform when computing the
#'        MLE.
#'
#' @return An object representing the collection.
R.oo::setConstructorS3("GaussianMixtures",
function(maxNumComponents = 1, dim = 1, phi = "default",
restarts = 50) {
numModels = maxNumComponents
prior = rep(1, numModels)

# Generate the partial order of the models
if (maxNumComponents == 1) {
E = matrix(numeric(0), ncol = 2)
g = igraph::graph.empty(1)
} else {
E = cbind(seq(1, numModels - 1), seq(2, numModels))
g = igraph::graph.edgelist(E, directed = TRUE)
}
topOrder = as.numeric(igraph::topological.sort(g))

dimension = rep(1, numModels)
for (j in topOrder) {
dimension[j] = choose(dim + 2, 2) * j - 1
}

if (phi == "default") {
phi = (dimension[1] + 1) / 2
}

extend(
MixtureModels(),
"GaussianMixtures",
.numModels = numModels,
.prior = prior,
.E = E,
.posetAsGraph = g,
.topOrder = topOrder,
.ambientDim = dim,
.dimension = dimension,
.phi = phi,
.restarts = restarts
)
})

#' @rdname   getTopOrder
#' @name     getTopOrder.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getTopOrder", "GaussianMixtures", function(this) {
return(this\$.topOrder)
}, appendVarArgs = F)

#' @rdname   getPrior
#' @name     getPrior.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getPrior", "GaussianMixtures", function(this) {
return(this\$.prior)
}, appendVarArgs = F)

#' @rdname   getNumModels
#' @name     getNumModels.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getNumModels", "GaussianMixtures", function(this) {
return(this\$.numModels)
}, appendVarArgs = F)

#' Set data for the gaussian mixture models.
#'
#' Sets the data to be used by the gaussian mixture models when computing MLEs.
#'
#' @name     setData.GaussianMixtures
#' @export
#'
#' @param this the GaussianMixtures object.
#' @param data the data to be set, a matrix where each row corresponds to a single
#'        multivariate observation. If the corresponding GaussianMixtures object
#'        has ambient dimension 1, then data may be a numeric vector of
#'        observations.
R.methodsS3::setMethodS3("setData", "GaussianMixtures", function(this, data) {
X = data
if (is.vector(X)) {
X = matrix(as.numeric(X), ncol = 1)
}
if (this\$.ambientDim != ncol(X)) {
stop(paste("Attempting to set data in GaussianMixtures whose ambient",
"dimesion does not agree with the number of columns in the data."
))
}
this\$.X = X
this\$.logLikes = rep(NA, this\$getNumModels())
this\$.mles = rep(list(NA), this\$getNumModels())
}, appendVarArgs = F)

#' @rdname   getData
#' @name     getData.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getData", "GaussianMixtures", function(this) {
if (is.null(this\$.X)) {
throw("Data has not yet been set")
}
return(this\$.X)
}, appendVarArgs = F)

#' @rdname   getNumSamples
#' @name     getNumSamples.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getNumSamples", "GaussianMixtures", function(this) {
return(length(this\$getData()))
}, appendVarArgs = F)

#' @rdname   logLikeMle
#' @name     logLikeMle.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("logLikeMle", "GaussianMixtures", function(this, model, ...) {
if (!is.na(this\$.logLikes[model])) {
return(this\$.logLikes[model])
}
X = this\$getData()
N = nrow(X)

if (ncol(X) == 1) {
mclustModelName = "V"
} else {
mclustModelName = "VVV"
}
fit = mclust::Mclust(X, G = model, model = mclustModelName)
logLike = fit\$loglik
mle = fit\$parameters
for (i in 1:this\$.restarts) {
my.z = matrix(rexp(N * model), N, model)
my.z = my.z / rowSums(my.z)
temp.fit = mclust::me(modelName = mclustModelName, data = X, z = my.z)
if (!is.na(temp.fit\$loglik)) {
if (temp.fit\$loglik > logLike) {
logLike = temp.fit\$loglik
mle = temp.fit\$parameters
}
}
}
this\$.logLikes[model] = logLike
this\$.mles[[model]] = list(mixWeights = mle\$pro, means = mle\$mean,
vars = mle\$vars)
return(this\$.logLikes[model])
}, appendVarArgs = F)

#' @rdname   mle
#' @name     mle.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("mle", "GaussianMixtures", function(this, model) {
if (!is.na(this\$.mle[[model]])) {
return(this\$.mle[[model]])
}
this\$logLikeMle(model)
return(this\$.mle[[model]])
}, appendVarArgs = F)

#' @rdname   getDimension
#' @name     getDimension.GaussianMixtures
#' @export
R.methodsS3::setMethodS3("getDimension", "GaussianMixtures", function(this, model) {
return(this\$.dimension[model])
}, appendVarArgs = F)
```
Lucaweihs/sBIC documentation built on June 3, 2017, 3:34 a.m.