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Minor MoE_stepwise speed-ups by avoiding duplication of initialisation for certain steps involving only the potential addition of a gating covariate.
Minor fix to MoE_stepwise for univariate data without covariates.
Prettier axis labels for MoE_Uncertainty plots.
Minor CRAN compliance edits to the vignette. \doi edits. CRAN 1.3.3 release.
Description: Clustering via parsimonious Gaussian Mixtures of Experts using the MoEClust models introduced by Murphy and Murphy (2020) <doi:10.1007/s11634-019-00373-8>. This package fits finite Gaussian mixture models with a formula interface for supplying gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated. A greedy forward stepwise search algorithm is provided for identifying the optimal model in terms of the number of components, the GPCM covariance parameterisation, and the subsets of gating/expert network covariates.
Copy file name to clipboardExpand all lines: R/MoEClust.R
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#' MoEClust: Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
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#'
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#' Clustering via parsimonious Gaussian Mixtures of Experts using the \emph{MoEClust} models introduced by Murphy and Murphy (2020) <\href{https://doi.org/10.1007/s11634-019-00373-8}{doi:10.1007/s11634-019-00373-8}>. This package fits finite Gaussian mixture models with gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated.
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#' Clustering via parsimonious Gaussian Mixtures of Experts using the \emph{MoEClust} models introduced by Murphy and Murphy (2020) <\doi{10.1007/s11634-019-00373-8}>. This package fits finite Gaussian mixture models with gating and/or expert network covariates using a range of parsimonious covariance parameterisations from the GPCM family via the EM/CEM algorithm. Visualisation of the results of such models using generalised pairs plots and the inclusion of an additional noise component is also facilitated.
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#' @section Usage:
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#' The most important function in the \pkg{MoEClust} package is: \code{\link{MoE_clust}}, for fitting the model via EM/CEM with gating and/or expert network covariates, supplied via formula interfaces.
#' @references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\href{https://doi.org/10.1007/s11634-019-00373-8}{doi:10.1007/s11634-019-00373-8}>.
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#' @references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\doi{10.1007/s11634-019-00373-8}>.
Copy file name to clipboardExpand all lines: R/Plotting_Functions.R
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#' The subsetting must include at least two variables, whether they be the MAP, a response variable, or a covariate, in order to be valid for plotting purposes. The arguments \code{data.ind} and \code{cov.ind} can also be used to simply reorder the panels, without actually subsetting.
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#' @param response.type The type of plot desired for the scatter plots comparing continuous response variables. Defaults to \code{"points"}.
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#'
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#' Points can also be sized according to their associated clustering uncertainty with the option \code{"uncertainty"}. In so doing, the transparency of the points will also be proportional to their clustering uncertainty, provided the device supports transparency. See also \code{\link{MoE_Uncertainty}} for an alternative means of visualising observation-specific cluster uncertainties (especially for univariate data).
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#' Points can also be sized according to their associated clustering uncertainty with the option \code{"uncertainty"}. In doing so, the transparency of the points will also be proportional to their clustering uncertainty, provided the device supports transparency. See also \code{\link{MoE_Uncertainty}} for an alternative means of visualising observation-specific cluster uncertainties (especially for univariate data).
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#'
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#' Alternatively, the bivariate \code{"density"} contours can be displayed (see \code{density.pars}), provided there is at least one Gaussian component in the model. Caution is advised when producing density plots for models with covariates in the expert network; the required number of evaluations of the (multivariate) Gaussian density for each panel (\code{res$G * prod(density.pars$grid.size)}) increases by a factor of \code{res$n}, thus plotting may be slow (particularly for large data sets).
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#' @param scatter.type A vector of length 2 (or 1) giving the plot type for the upper and lower triangular portions of the plot, respectively, pertaining to the associated covariates. Defaults to \code{"lm"} for covariate vs. response panels and \code{"points"} otherwise. Only relevant for models with continuous covariates in the gating &/or expert network. \code{"ci"} and \code{"lm"} type plots are only produced for plots pairing covariates with response, and never response vs. response or covariate vs. covariate. Note that lines &/or confidence intervals will only be drawn for continuous covariates included in the expert network; to include covariates included only in the gating network also, the options \code{"lm2"} or \code{"ci2"} can be used but this is not generally advisable.
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#' \code{\link{plot.MoEClust}} is a wrapper to \code{\link{MoE_gpairs}} which accepts the default arguments, and also produces other types of plots. Caution is advised producing generalised pairs plots when the dimension of the data is large.
#' @references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\href{https://doi.org/10.1007/s11634-019-00373-8}{doi:10.1007/s11634-019-00373-8}>.
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#' @references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\doi{10.1007/s11634-019-00373-8}>.
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#'
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#' Emerson, J. W., Green, W. A., Schloerke, B., Crowley, J., Cook, D., Hofmann, H. and Wickham, H. (2013). The generalized pairs plot. \emph{Journal of Computational and Graphical Statistics}, 22(1): 79-91.
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#' Other types of plots are available by first calling \code{\link{as.Mclust}} on the fitted object, and then calling \code{\link[mclust]{plot.Mclust}} on the results. These can be especially useful for univariate data.
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#' @return The visualisation according to \code{what} of the results of a fitted \code{MoEClust} model.
#'@references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\href{https://doi.org/10.1007/s11634-019-00373-8}{doi:10.1007/s11634-019-00373-8}>.
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#'@references Murphy, K. and Murphy, T. B. (2020). Gaussian parsimonious clustering models with covariates and a noise component. \emph{Advances in Data Analysis and Classification}, 14(2): 293-325. <\doi{10.1007/s11634-019-00373-8}>.
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