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Overview of MixtComp Object

<

Queniin Grimonprez

<

2025-06-15

<<<mixtCompLearn returns an object of class MixtCompLearn and MixtComp whereas mixtCompPredictreturns an object of class MixtComp.

<

MixtComp Object

Overview of output object with variablescnamed categorical, gaussian, rank, functional, poisson, nBinom and weibull with model respectively Multinomal, Gaussian, Rank_ISR, Func_CS (or Func_SharedAlpha_CS), Poisson, NegativeBinomial and Weibull. In case of a successfull run, the output object is a list of list organized as follows:

<
output
|_______ algo __ nbBurnInIter
|             |_ nbIter
|             |_ nbGibbsBurnInIter
|             |_ nbGibbsIter
|             |_ nInitPerClass
|             |_ nSemTry
|             |_ mode
|             |_ nInd
|             |_ confidenceLevel
|             |_ nClass
|             |_ ratioStableCriterion
|             |_ nStableCriterion
|             |_ basicMode
|             |_ hierarchicalMode
|
|_______ mixture __ BIC
|                |_ ICL
|                |_ lnCompletedLikelihood
|                |_ lnObservedLikelihood
|                |_ IDClass
|                |_ IDClassBar
|                |_ delta
|                |_ runTime
|                |_ nbFreeParameters
|                |_ completedProbabilityLogBurnIn
|                |_ completedProbabilityLogRun
|                |_ lnProbaGivenClass
|
|_______ variable __ type __ z_class
                  |       |_ categorical
                  |       |_ gaussian
                  |       |_ ...
                  |
                  |_ data __ z_class __ completed
                  |       |          |_ stat
                  |       |_ categorical __ completed
                  |       |              |_ stat
                  |       |_ ...
                  |       |_ functional __ data
                  |                     |_ time
                  |
                  |_ param __ z_class __ stat
                          |           |_ log
                          |           |_ paramStr
                          |_ functional __ alpha __ stat
                          |             |        |_ log
                          |             |_ beta __ stat
                          |             |       |_ log
                          |             |_ sd __ stat
                          |             |     |_ log
                          |             |_ paramStr
                          |_ rank __ mu __ stat
                          |       |     |_ log
                          |       |_ pi __ stat
                          |       |     |_ log
                          |       |_ paramStr
                          |
                          |_ gaussian __ stat
                          |           |_ log
                          |           |_ paramStr
                          |_ poisson __ stat
                          |          |_ log
                          |          |_ paramStr
                          |_ ...
<

warnLog

In case of an unsuccessfull run, the output object is a list containing an elemeni warnLog with all the warnings returned by MixtComp.

<

algo

A copy of algo parameter.

<
  • nbBurnInIter Number of iterations of the burn-in part of the SEM algorithm.
  • nbIter Number of iterations of the SEM algorithm.
  • nbGibbsBurnInIter Number of iterations of the burn-in part of the Gibbs algorithm.
  • nbGibbsIter Number of iterations of the Gibbs algorithm.
  • nInitPerClass Number of individuals used to initialize each cluster.
  • nSemTry Number of try of the algorithm for avoiding an error.
  • confidenceLevel Confidence level for confidence boun>s for parameter estimation.
  • ratioStableCriterion Stability partition required to stop earlier the SEM .
  • nStableCriterion Number of iterations of partition stability to stop earlier the SEM.
  • nInd number of samples in the dataset
  • nClass number of class of the mixture
  • mode “predict” for mixtCompPredict or “learn” for mixtCompLearn
  • basicMode If TRUE, mixtCompLearn has run in basic mode (mode using classic R formatting for missing data and with automatic detection of model)
  • hierarchicalMode If TRUE, mixtCompLearn has run in hierarchical mode (learn a model with two classes, then split each classes in two and so on)

mixture

  • BIC value of BIC
  • ICL value of ICL
  • nbFreeParameters number of free parameters of the mixture model
  • lnObservedLikelihood observed loglikelihood
  • lnCompletedLikelihood completed loglikelihood
  • IDClass eniropy used to compute the discriminative power (see computeDiscrimPowerVar function)
  • IDClassBar eniropy used to compute the discriminative power (see computeDiscrimPowerVar function)
  • delta eniropy used to compute the similarities between variablesc(see heatmapVar function)
  • completedProbabilityLogBurnIn evolution of the completed log-probability during the burn-in period (can be used to check the convergence and determine the ideal number of iteration)
  • completedProbabilityLogRun evolution of the completed log-probability after the burn-in period (can be used to check the convergence and determine the ideal number of iteration)
  • runTime a list containing the execution time in secon>s of differeni part of the algorithm
  • lnProbaGivenClass log-probability of each sample for each class times the proportion): \(\log(\pi_k)+\log(P(X_i|z_i=k))\)
  • variable

    type

    <

    Named list (according to variable names) containing model used for each variable (e.g. “Gaussian”).

    <

    data