mixmodLearn.default {Rmixmod}

Create an instance of the [MixmodLearn] class

Description

This function computes the first step of a discriminant analysis. It will find the best classification rule by running an M step from the training observations.

Usage

mixmodLearn.default(
  data,
  knownLabels,
  dataType = NULL,
  models = NULL,
  criterion = "CV",
  nbCVBlocks = 10,
  weight = NULL
)

Arguments

data

frame containing quantitative,qualitative or heterogeneous data. Rows correspond to observations and columns correspond to variables.

knownLabels

an integer vector or a factor of size number of observations. Each cell corresponds to a cluster affectation. So the maximum value is 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 (in case of homogeneous data). 'composite' type must be specified explicitly.

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).

criterion

list of character defining the criterion to select the best model. Possible values: "BIC", "CV" or c("CV","BIC"). Default is "CV".

nbCVBlocks

integer which defines the number of block to perform the Cross Validation. This value will be ignored if the CV criterion is not chosen. Default value is 10.

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.

Value

Returns an instance of the [MixmodLearn] 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 [e-mail]

Examples

## A quantitative example with the famous iris data set
learn.iris <- mixmodLearn(iris[1:4], iris$Species)
## get summary
summary(learn.iris)

## A qualitative example with the famous birds data set
data(birds)
birds.partition <- as.integer(c(rep(1, 34), rep(2, 35)))
learn.birds <- mixmodLearn(data = birds, knownLabels = birds.partition)
## get summary
summary(learn.birds)

## A composite example with a heterogeneous data set
data(heterodatatrain)
learn.hetero <- mixmodLearn(heterodatatrain[-1], knownLabels = heterodatatrain$V1)
## get summary
summary(learn.hetero)