Rmixmod (version 2.1.10)

mixmodCluster: 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(...)

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.

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 optional. 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.

Author

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

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)
if (FALSE) {
plot(xem)
hist(xem)
}

## get summary
summary(xem)

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