#' @include ModelPoset.R
NULL
#' Construct a poset of latent class analysis models.
#'
#' Creates an object representing a collection of latent class analysis models.
#' There is one model for each fixed number of latent classes from 1 to some
#' specified maximum. In particular each model is identified by a single number
#' specifiying the number of latent classes in the model. Models are naturally
#' ordered by inclusion so that, for example, a model with 2 latent classes
#' comes before a model with 3 or more latent classes.
#'
#' @name LCAs
#' @export
#'
#' @param maxNumClasses the number of classes in the largest LCA model to
#' considered.
#' @param numVariables the number of observed variables.
#' @param numStatesForVariables the number of states for each observed variable,
#' at the moment these must all be equal.
#' @param phi parameter controlling the strength of the sBIC penalty.
#'
#' @return An object representing the collection.
R.oo::setConstructorS3("LCAs",
function(maxNumClasses = 1, numVariables = 2,
numStatesForVariables = 2, phi = "default") {
numModels = maxNumClasses
prior = rep(1, numModels)
# Generate the partial order of the models
if (maxNumClasses == 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 1:numModels) {
dimension[j] = min(
(j - 1) + numVariables * (numStatesForVariables - 1) * j,
numStatesForVariables ^ numVariables - 1
)
}
if (phi == "default") {
phi = (dimension[1] + 1) / 2
}
extend(
MixtureModels(),
"LCAs",
.numModels = numModels,
.prior = prior,
.E = E,
.posetAsGraph = g,
.topOrder = topOrder,
.dimension = dimension,
.maxNumClasses = maxNumClasses,
.numVariables = numVariables,
.numStatesForVariables = numStatesForVariables,
.phi = phi
)
})
#' @rdname getTopOrder
#' @name getTopOrder.LCAs
#' @export
R.methodsS3::setMethodS3("getTopOrder", "LCAs", function(this) {
return(this$.topOrder)
}, appendVarArgs = F)
#' @rdname getPrior
#' @name getPrior.LCAs
#' @export
R.methodsS3::setMethodS3("getPrior", "LCAs", function(this) {
return(this$.prior)
}, appendVarArgs = F)
#' @rdname getNumModels
#' @name getNumModels.LCAs
#' @export
R.methodsS3::setMethodS3("getNumModels", "LCAs", function(this) {
return(this$.numModels)
}, appendVarArgs = F)
#' Set data for the LCA models.
#'
#' Sets the data to be used by the LCA models when computing MLEs.
#'
#' @name setData.LCAs
#' @export
#'
#' @param this the LCAs object.
#' @param data the data to be set, should be an integer valued matrix where each
#' row represents a single sample from the observed variables.
R.methodsS3::setMethodS3("setData", "LCAs", function(this, data) {
if (ncol(data) != this$.numVariables) {
throw("Input data has incorrect number of columns.")
}
if (!is.data.frame(data)) {
data = as.data.frame(data)
}
this$.X = data
this$.logLikes = rep(NA, this$getNumModels())
}, appendVarArgs = F)
#' @rdname getData
#' @name getData.LCAs
#' @export
R.methodsS3::setMethodS3("getData", "LCAs", function(this) {
if (is.null(this$.X)) {
throw("Data has not yet been set")
}
return(this$.X)
}, appendVarArgs = F)
#' @rdname getNumSamples
#' @name getNumSamples.LCAs
#' @export
R.methodsS3::setMethodS3("getNumSamples", "LCAs", function(this) {
return(nrow(this$getData()))
}, appendVarArgs = F)
#' @rdname logLikeMle
#' @name logLikeMle.LCAs
#' @export
R.methodsS3::setMethodS3("logLikeMle", "LCAs", function(this, model, ...) {
if (!is.na(this$.logLikes[model])) {
return(this$.logLikes[model])
}
X = this$getData()
f = as.formula(paste("cbind(", paste(names(X), collapse = ","), ")~1"))
fit = poLCA::poLCA(f, X, nclass = model, nrep = 50, maxiter = 8000,
verbose = FALSE)
this$.logLikes[model] = fit$llik
this$.mles[[model]] = fit$probs
return(this$.logLikes[model])
}, appendVarArgs = F)
#' @rdname mle
#' @name mle.LCAs
#' @export
R.methodsS3::setMethodS3("mle", "LCAs", 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.LCAs
#' @export
R.methodsS3::setMethodS3("getDimension", "LCAs", function(this, model) {
return(this$.dimension[model])
}, appendVarArgs = F)
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