MoEClust: Gaussian Parsimonious Clustering Models

with Gating and Expert Network Covariates

and a Noise Component

MoEClust v1.3.1 - (9^th release [patch update]: 2020-05-12)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• Maintenance release for compatibility with R 4.0.0 - minor edits.
• summary.MoEClust gains the printing-related arguments classification=TRUE,
  parameters=FALSE, and networks=FALSE (thanks to a request from Prof. Kamel Gana).
• Related improvements to print/summary methods for MoE_gating & MoE_expert objects.
• Minor speed-up for G=1 models with expert network covariates.
• Improvements to MoE_plotGate, with new type, pch, and xlab defaults.
• Added informative dimnames to returned parameters from MoE_clust().
• Documentation, vignette, examples, and references improvements.

MoEClust v1.3.0 - (8^th release [minor update]: 2020-03-30)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• Various fixes and improvements to initialisation when there are expert network covariates:
  • MoE_mahala now correctly uses the covariance of resids rather than the response.
  • New MoE_mahala arg. identity allow use of Euclidean distance instead:
    this argument can also be passed via exp.init$identity to MoE_control.
  • Convergence of the initialisation procedure now explictly monitored & sped-up.
  • Values of the criterion being minimised are now returned as an attribute.
  • The number of iterations of the initialisation algorithm are also returned as an attribute.
  • MoE_control arg. exp.init$max.init now defaults to .Machine$integer.max.
  • Improved checks on the resids arg. to MoE_mahala.
  • Greatly expanded the MoE_mahala examples.
• Improvements to predict.MoEClust:
  • Now returns the predicted values of the gating and expert networks.
  • Now returns the predictions from the expert network of the most probable component
    (MAPy), in addition to the (aggregated) predicted responses (y).
  • New arg. MAPresids governs whether residuals are computed against MAPy or y.
  • New arg. use.y (see documentation for details).
  • Now properly allows empty newdata for models with no covariates of any kind.
  • Fixed prediction for equal mixing proportion models when discard.noise=FALSE.
• Fixed small MoE_stepwise bugs when
  • only one of gating or expert are supplied.
  • univariate response data are supplied.
  • moving from G=1 to G=2 with equal mixing proportions and no covariates.
  • discarding covariates present in the response data.
• Odds ratios now returned (and printed) when calling summary on x$gating.
• noise_vol now returns correction location for univariate data when reciprocal=TRUE.
• Spell-checking of documentation and fixes to donttest examples.

MoEClust v1.2.4 - (7^th release [patch update]: 2019-12-11)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• Fixed small bugs in MoE_stepwise:
  • Improved checks on network.data and data.
  • Prevented z.list from being suppliable.
    
  • Fixes whenequalPro="yes" & noise=TRUE.
  • Fixes for supplying optional MoE_control arguments (also for MoE_clust).
  • Prevented termination if adding a component fails,
    provided at least one other step doesn’t fail.
• Fixed discard.noise=TRUE behaviour for MoE_clust, predict.MoEClust, &
  residuals.MoEClust for models with a noise component fitted via "CEM".
• Minor fixes to noise_vol function and handling of noise.meth arg. to MoE_control.
• Slight speed-up to E-step/C-step for models with a noise component.
• Initial allocation matrices now stored as attributes to MoE_clust output (see ?MoE_control).
• Anti-aliasing of vignette images.
• Updated citation info after publication in Advances in Data Analysis and Classification.

MoEClust v1.2.3 - (6^th release [patch update]: 2019-07-29)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• Exported function MoE_stepwise for conducting a greedy forward stepwise
  search to find the optimal model in terms of the number of components, GPCM
  covariance parameterisation, and the subsets of gating/expert network covariates.
• MoE_control & predict.MoEClust gain the arg. discard.noise:
  Default of FALSE retains old behaviour (see documentation for details).
• MoE_control gains the arg. z.list and the init.z arg. gets the option "list":
  this allows manually supplying (soft or hard) initial cluster allocation matrices.
• New args. and small fixes added to MoE_gpairs:
  • uncert.cov arg. added to control uncertainty point-size in panels with covariates.
  • density.pars gains arg. label.style.
  • scatter.pars & stripplot.pars gain args. noise.size & size.noise.
  • barcode.pars$bar.col slightly fixed from previous update.
  • Colours for "violin" type plots now accurate for MAP panels.
• Slight speed-up to noise_vol when method="ellipsoidhull".
• Small fix to predict.MoEClust when resid=TRUE for models with expert covariates.
• Small fix related to ... construct for residuals.MoEClust.
• All printing related to noise-only models no longer shows the model name (there is none!).
• Other small fixes to print.MoEClust, print.summary_MoEClust, & print.MoECompare.
• Cosmetic fix to returned gating objects for equalPro=TRUE models.
• Removed parallel package from Suggests:.

MoEClust v1.2.2 - (5^th release [patch update]: 2019-05-15)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• noise_vol now also returns the location of the centre of mass of the region
  used to estimate the hypervolume, regardless of the method employed. This fixes:
  • predict.MoEClust for any models with a noise component (see below).
  • The summary of means for models with expert covariates and a noise component.
  • The location of the MVN ellipses for such models in MoE_gpairs (see below).
• Furthermore, calculation of the hypervolume in noise_vol for data with >2 dimensions
  is now correct when method="ellipsoidhull", owing to a bug in the cluster package.
• Other fixes and speed-ups for the MoE_gpairs plotting function:
  • Added arg. expert.covar (& also to as.Mclust function).
  • Fixed location of MVN ellipses for models with noise & expert covariates (see above).
  • Fixes when response.type="density" for all models with a noise component.
  • Speed-up when response.type="density" for models with covariates of any kind.
  • Fixes to labelling for models with a noise component.
  • Fixed handling of subset$data.ind & subset$cov.ind arguments.
  • Barcode type plots now have colour for panels involving the MAP classification.
  • Barcode type plots now respect the arg. buffer.
  • Use of colour in MoE_plotGate is now consistent with MoE_gpairs.
• Fixes to how gating & expert formulas are handled:
  • Allowed specification of formulas with dropped variables of the form ~.-a-b.
  • Allowed formulas with no intercept of the form ~c-1.
  • Allowed interaction effects, transformations and higher-order terms using I().
  • Small related fixes to drop_levels & drop_constants functions.
    
• MoE_compare gains arg. noise.vol for overriding the noise.meth arg.:
  this allows specifying an improper uniform density directly via the (hyper)volume,
  & hence adding noise to models for high-dimensional data for which noise_vol() fails.
• Fixed bug for equalPro models with noise component, and also added equalNoise arg.
  to MoE_control, further controlling equalPro in the presence of a noise component.
• Fixes to predict.MoEClust for the following special cases:
  • Fixes for any models with a noise component (see noise_vol comment above).
  • Accounted for predictions of single observations for models with a noise component.
  • Accounted for models with equal mixing proportions.
• Accounted for categorical covariates in the x.axis arg. to MoE_plotGate.
• tau0 can now also be supplied as a vector in the presence of gating covariates.
• Fix to expert_covar for univariate models.
• Slight MoE_estep speed-up due to removal of unnecessary sweep().
• Small fixes for when clustMD is invoked, and added snow package to Suggests:.
• The nnet arg. MaxNWts now passable to gating network multinom call via MoE_control.
• Improved printing of output and handling of ties, especially for MoE_compare.
• Many documentation and vignette improvements.

MoEClust v1.2.1 - (4^th release [patch update]: 2018-12-11)

New Features, Improvements, Bug Fixes & Miscellaneous Edits

• New MoE_control arg. algo allows model fitting using the "EM" or "CEM" algorithm:
  • Related new function MoE_cstep added.
  • Extra algo option "cemEM" allows running EM starting from convergence of CEM.
• Added LOGLIK to MoE_clust output, giving maximal log-likelihood values for all fitted models.
  • Behaves exactly as per DF/ITERS, etc., with associated printing/plotting functions.
  • Edited MoE_compare, summary.MoEClust, & MoE_plotCrit accordingly.
• New MoE_control arg. nstarts allows for multiple random starts when init.z="random".
• New MoE_control arg. tau0 provides another means of initialising the noise component.
• If clustMD is invoked for initialisation, models are now run more quickly in parallel.
• MoE_plotGate now allows a user-specified x-axis against which mixing proportions are plotted.
• Fixed bug in checking for strictly increasing log-likelihood estimates.

MoEClust v1.2.0 - (3^rd release [minor update]: 2018-08-24)

New Features & Improvements

• New predict.MoEClust function added: predicts cluster membership probability,
  MAP classification, and fitted response, using only new covariates or new covariates &
  new response data, with noise components (and the noise.gate option) accounted for.
• New plotting function MoE_Uncertainty added (callable within plot.MoEClust):
  visualises clustering uncertainty in the form of a barplot or an ordered profile plot,
  allowing reference to be made to the true labels, or not, in both cases.
• Specifying response.type="density" to MoE_gpairs now works properly for models with
  gating &/or expert network covariates. Previous approach which evaluated the density using
  averaged gates &/or averaged means replaced by more computationally expensive but correct
  approach, which evaluates MVN density for every observation individually and then averages.
• Added clustMD package to Suggests:. New MoE_control argument exp.init$clustMD
  governs whether categorical/ordinal covariates are also incorporated into the initialisation
  when isTRUE(exp.init$joint) & clustMD is loaded (defaults to FALSE, works with noise).
• Added drop.break arg. to MoE_control for further control over the extra initialisation
  step invoked in the presence of expert covariates (see Documentation for details).
• Sped-up MoE_dens for the EEE & VVV models by using already available Cholesky factors.
• Other new MoE_control arguments:
  • km.args specifies kstarts & kiters when init.z="kmeans".
  • Consolidated args. related to init.z="hc" & noise into hc.args & noise.args.
  • hc.args now also passed to call to mclust when init.z="mclust".
  • init.crit ("bic"/"icl") controls selection of optimal mclust/clustMD
    model type to initialise with (if init.z="mclust" or isTRUE(exp.init$clustMD));
    relatedly, initialisation now sped-up when init.z="mclust".

Bug Fixes & Miscellaneous Edits

• ITERS replaces iters as the matrix of the number of EM iterations in MoE_clust output:
  • iters now gives this number for the optimal model.
    • ITERS now behaves like BIC/ICL etc. in inheriting the "MoECriterion" class.
      
    • iters now filters down to summary.MoEClust and the associated printing function.
      
    • ITERS now filters down to MoE_compare and the associated printing function.
• Fixed point-size, transparency, & plotting symbols when response.type="uncertainty"
  within MoE_gpairs to better conform to mclust: previously no transparency.
• subset arg. to MoE_gpairs now allows data.ind=0 or cov.ind=0, allowing plotting of
  response variables or plotting of the covariates to be suppressed entirely.
• Clarified MVN ellipses in MoE_gpairs plots.
• sigs arg. to MoE_dens & MoE_estep must now be a variance object, as per variance
  in the parameters list from MoE_clust & mclust output, the number of clusters G,
  variables d & modelName is inferred from this object: the arg. modelName was removed.
• MoE_clust no longer returns an error if init.z="mclust" when no gating/expert network
  covariates are supplied; instead, init.z="hc" is used to better reproduce mclust output.
• resid.data now returned by MoE_clust as a list, to better conform to MoE_dens.
• Renamed functions MoE_aitken & MoE_qclass to aitken & quant_clust, respectively.
• Rows of data w/ missing values now dropped for gating/expert covariates too (MoE_clust).
• Logical covariates in gating/expert networks now coerced to factors.
• Fixed small bug calculating linf within aitken & the associated stopping criterion.
• Final linf estimate now returned for optimal model when stopping="aitken" & G > 1.
• Removed redundant extra M-step after convergence for models without expert covariates.
• Removed redundant & erroneous resid & residuals args. to as.Mclust & MoE_gpairs.
• MoE_plotCrit, MoE_plotGate & MoE_plotLogLik now invisibly return revelant quantities.
• Corrected degrees of freedom calculation for G=0 models when noise.init is not supplied.
• Fixed drop_levels to handle alphanumeric variable names and ordinal variables.
• Fixed MoE_compare when a mix of models with and without a noise component are supplied.
• Fixed MoE_compare when optimal model has to be re-fit due to mismatched criterion.
• Fixed y-axis labelling of MoE_Uncertainty plots.
• print.MoECompare now has a digits arg. to control rounding of printed output.
• Better handling of tied model-selection criteria values in MoE_clust & MoE_compare.
• Interactions and higher-order terms are now accounted for within drop_constants.
• Replaced certain instances of is.list(x) with inherits(x, "list") for stricter checking.
• Added extra checks for invalid gating &/or expert covariates within MoE_clust.
• Added mclust::clustCombi/clustCombiOptim examples to as.Mclust documentation.
• Added extra precautions for empty clusters: during initialisation & during EM.
• Added utility function MoE_news for accessing this NEWS file.
• Added message if optimum G is at either end of the range considered.
• Tidied indentation/line-breaks for cat/message/warning calls for printing clarity.
• Added line-breaks to usage sections of multi-argument functions.
• Corrected MoEClust-package help file (formerly just MoEClust).
• Many documentation clarifications.

MoEClust v1.1.0 - (2^nd release [minor update]: 2018-02-06)

New Features & Improvements

• MoE_control gains the noise.gate argument (defaults to TRUE): when FALSE,
  the noise component’s mixing proportion isn’t influenced by gating network covariates.
• x$parameters$mean is now reported as the posterior mean of the fitted values when
  there are expert network covariates: when there are no expert covariates, the posterior
  mean of the response is reported, as before. This effects the centres of the MVN ellipses
  in response vs. response panels of MoE_gpairs plots when there are expert covariates.
• New function expert_covar used to account for variability in the means, in the presence
  of expert covariates, in order to modify shape & size of MVN ellipses in visualisations.
• MoE_control gains the hcUse argument (defaults to "VARS" as per old mclust versions).
• MoE_mahala gains the squared argument + speedup/matrix-inversion improvements.
• Speed-ups, incl. functions from matrixStats (on which MoEClust already depended).
• The MoE_gpairs argument addEllipses gains the option "both".

Bug Fixes & Miscellaneous Edits

• Fixed bug when equalPro=TRUE in the presence of a noise component when there are
  no gating covariates: now only the mixing proportions of the non-noise components
  are constrained to be equal, after accounting for the noise component.
• MoE_gpairs argument scatter.type gains the options lm2 & ci2 for further control
  over gating covariates. Fixed related bug whereby lm & ci type plots were being
  erroneously produced for panels involving pairs of continuous covariates only.
• Fixed bugs in MoE_mahala and in expert network estimation with a noise component.
• G=0 models w/ noise component only can now be fitted without having to supply noise.init.
• MoE_compare now correctly prints noise information for sub-optimal models.
• Slight edit to criterion used when stopping="relative": now conforms to mclust.
• Added check.margin=FALSE to calls to sweep().
• Added call.=FALSE to all stop() messages.
• Removed dependency on the grid library.
• Many documentation clarifications.

MoEClust v1.0.0 - (1^st release: 2017-11-28)