R: Create an instance of the ['MixmodCluster'] class

mixmodCluster {Rmixmod} | R Documentation

## Create an instance of the [`MixmodCluster`] class

### Description

This function computes an optimal mixture model according to the criteria furnished, and the list of model defined in [`Model`], using the algorithm specified in [`Strategy`].

### Usage

    mixmodCluster(...)

### Arguments

`...` | all arguments are transfered to the MixmodCluster constructor. Valid arguments are: data: frame containing quantitative,qualitative or heterogeneous data. Rows correspond to observations and columns correspond to variables. nbCluster: numeric listing the number of clusters. dataType: character. Type of data is "quantitative", "qualitative" or "composite". Set as NULL by default, type will be guessed depending on variables type. models: a [`Model`] object defining the list of models to run. For quantitative data, the model "Gaussian_pk_Lk_C" is called (see mixmodGaussianModel() to specify other models). For qualitative data, the model "Binary_pk_Ekjh" is called (see mixmodMultinomialModel() to specify other models). strategy: a [`Strategy`] object containing the strategy to run. Call mixmodStrategy() method by default. criterion: list of character defining the criterion to select the best model. The best model is the one with the lowest criterion value. Possible values: "BIC", "ICL", "NEC", c("BIC", "ICL", "NEC"). Default is "BIC". weight: numeric vector with n (number of individuals) rows. Weight is optionnal. This option is to be used when weight is associated to the data. knownLabels: vector of size nbSample. it will be used for semi-supervised classification when labels are known. Each cell corresponds to a cluster affectation.

### Value

Returns an instance of the [`MixmodCluster`] class. Those two attributes will contain all outputs:

results

  a list of [`MixmodResults`] object containing all the results sorted in ascending order according to the given criterion.

bestResult

  a S4 [`MixmodResults`] object containing the best model results.

### Author(s)

Florent Langrognet and Remi Lebret and Christian Poli ans Serge Iovleff, with contributions from C. Biernacki and G. Celeux and G. Govaert contact@mixmod.org

### Examples

      ## A quantitative example with the famous geyser data set
      data(geyser)
      ## with default values
      mixmodCluster(geyser, nbCluster=2:6)

      ## A qualitative example with the birds data set
      data(birds)
      mixmodCluster(data=birds, nbCluster = 2:5, criterion= c("BIC","ICL","NEC"),
                    model = mixmodMultinomialModel())

      ## use graphics functions
      xem <- mixmodCluster(data=geyser, nbCluster=3)
      ## Not run:
      plot(xem)
      hist(xem)

    ## End(Not run)

      ## get summary
      summary(xem)

      ## A composite example with a heterogeneous data set
      data(heterodata)
      mixmodCluster(heterodata,2)

[Package Rmixmod version 2.1.2.2 Index]