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)