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

Description Usage Arguments Value Author(s) Examples

View source: R/MixmodCluster.R

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

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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 [email protected]

Examples

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  ## 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)

Rmixmod documentation built on June 26, 2018, 5:02 p.m.