---
title: "Overview of MixtComp Object"
author: "Quentin Grimonprez"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
toc: true
vignette: >
%\VignetteIndexEntry{Overview of MixtComp Object}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
`mixtCompLearn` returns an object of class *MixtCompLearn* and *MixtComp* whereas `mixtCompPredict`returns an object of class *MixtComp*.
## MixtComp Object
Overview of output object with variables named *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:
```text
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 element **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 bounds 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** entropy used to compute the discriminative power (see computeDiscrimPowerVar function)
- **IDClassBar** entropy used to compute the discriminative power (see computeDiscrimPowerVar function)
- **delta** entropy used to compute the similarities between variables (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 seconds of different 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
Except for functional models and LatentClass, data contains, for each variable, two elements: *completed* and *stat*. *completed* contains the completed data and *stat* contains statistics about completed data.
The format is detailed below according to the model.
- **LatentClass**
Two elements: *completed* and *stat*. *completed* contains the completed data. *stat* is a matrix with the same number of columns as the number of class.
For each sample, it contains the $t_{ik}$ (probability of $x_i$ to belong to class *k*) estimated with the imputed values during the Gibbs at the end of each iteration after the burn-in phase of the algorithm.
- **Gaussian/Poisson/NegativeBinomial/Weibull**
*stat* is a matrix where each row corresponds to a missing data and contains 4 elements: index of the missing data, median, 2.5% quantile, 97.5% quantile (if the confidenceLevel parameter is set to 0.95) of imputed values during the Gibbs at the end of each iteration after the burn-in phase of the algorithm.
- **Multinomial**
*stat* is a named list where each element corresponds to a missing data. The name of the element corresponds to the index of the missing data. It contains a matrix containing the imputed values, during the Gibbs at the end of each iteration after the burn-in phase of the algorithm, and their frequency.
- **Rank_ISR**
*stat* is a named list where each element corresponds to a missing data. The name of the element corresponds to the index of the missing data. It contains a matrix containing the imputed values, during the Gibbs at the end of each iteration after the burn-in phase of the algorithm, and their frequency.
- **Func_CS** and **Func_SharedAlpha_CS**
Two elements: *data* and *time*. *time* (resp. *data*) is a list containing the time (resp. value) vector of the functional for each sample.
- **Other Models**
One element: *completed*, a matrix/vector containing the completed version of the dataset.
#### param
For one variable, it contains a list with estimated parameters (*param*), log recorded during the SEM (*log*) and hyperparameters if any (*paramStr*).
The output format depends of the model but in most of the case, *stat* is a matrix with 3 columns containing the median values of estimated parameters and quantile ate the desired confidence level,
*log* is matrix containing the estimated proportion during the M step of each iteration of the algorithm after the burn-in phase and *paramStr* is a string.
For the meaning of the parameters, user can refer to the documentation [data format](dataFormat.html).
- **LatentClass**
A list of 3 elements: *stat*, *log*, *paramStr*.
*log* is matrix containing the estimated proportion during the M step of each iteration of the algorithm after the burn-in phase. *stat* is a matrix containing the median (and quantiles corresponding to the confidenceLevel parameter) of the estimated proportion. The median proportions are the returned proportions. *paramStr* contains `""`.
- **Gaussian**
The *stat* matrix has 2\*nClass rows. For a class $k$, parameters are mean ($\mu_k$) and sd ($\sigma_k$).
- **Poisson**
The *stat* matrix has nClass rows. For a class $k$, the parameter is lambda ($\lambda_k$).
- **NegativeBinomial**
The *stat* matrix has 2\*nClass rows. For a class $k$, parameters are n ($n_k$) and p ($p_k$).
- **Weibull**
The *stat* matrix has 2\*nClass rows. For a class $j$, parameters are k (shape) ($k_j$) and lambda (scale) ($\lambda_j$).
- **Multinomial**
*paramStr* contains `"nModality: J"` where $J$ is the number of modalities.
The *stat* matrix has J\*nClass rows. For a class $k$, parameters are probabilities to belong to modality $J$.
- **Rank_ISR**
*paramStr* contains `"nModality: J"` where $J$ is the length of the rank (number of sorted objects).
Two lists (named *mu* and *pi*) of 2 elements: *stat*, *log*.
For *pi*, *stat* is a matrix with nClass rows. For a class $k$, parameter is pi ($pi_k$).
For *mu*, *stat* is a list with nClass elements. For a class $k$, a list is returned with the mode of the parameter ($\mu_k$), and the frequency of the mode during the SEM algorithm after the burn-in phase.
- **Func_CS** and **Func_SharedAlpha_CS**
*paramStr* contains `"nSub: S, nCoeff: C"` where $S$ is the number of subregressions and $C$ the number of coefficients of each regression.
Three lists (named *alpha*, *beta* and *sd*) of 2 elements: *stat*, *log*.
For *alpha*, *stat* is a matrix with 2\*S\*nClass rows. For a class $k$ and a subregression $s$, parameters are the estimated coefficients of a logistic regression controlling the transition between subregressions.
For *beta*, *stat* is a matrix with S\*C\*nClass rows. For a class $k$ and a subregression $s$, parameters are the estimated coefficient of the regression.
For *sd*, *stat* is a matrix with S\*nClass rows. For a class $k$ and a subregression $s$, the parameter is the standard deviation of the residuals of the regression.
## MixtCompLearn Object
A *MixtCompLearn* object is the output of `mixtCompLearn` function. It contains one or several $MixtComp$ object.
- **nClass** A vector containing the number of classes tested
- **crit** ICL and BIC values for each value of *nClass*
- **criterion** "BIC" or "ICL", the criterion used to choose the number of classes
- **algo**, **mixture**, **variable**, **warnLog** MixtComp object associated with the best number of classes
- **res** A list containing one *MixtComp* object per number of class. The first element (res[[1]]) corresponds to the *MixtComp* object for a number of classes of *nClass[1]*
- **nRun** Number of runs for each number of classes
- **totalTime** Total running time