mixmodMultinomialModel: Create an instance of the ['MultinomialModel'] class

Description Usage Arguments Details Value Author(s) References Examples

View source: R/MultinomialModel.R

Description

Define a list of multinomial model to test in MIXMOD.

Usage

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mixmodMultinomialModel(listModels = NULL, free.proportions = TRUE,
  equal.proportions = TRUE, variable.independency = NULL,
  component.independency = NULL)

Arguments

listModels

a list of characters containing a list of models. It is optional.

free.proportions

logical to include models with free proportions. Default is TRUE.

equal.proportions

logical to include models with equal proportions. Default is FALSE.

variable.independency

logical to include models where [\varepsilon_k^j] is independent of the variable j. Optionnal.

component.independency

logical to include models where [\varepsilon_k^j] is independent of the component k. Optionnal.

Details

In the multinomial mixture model, the multinomial distribution is associated to the jth variable of the kth component is reparameterized by a center a_k^j and the dispersion \varepsilon_k^j around this center. Thus, it allows us to give an interpretation similar to the center and the variance matrix used for continuous data in the Gaussian mixture context. In the following, this model will be denoted by [\varepsilon_k^j]. In this context, three other models can be easily deduced. We note [\varepsilon_k] the model where \varepsilon_k^j is independent of the variable j, [\varepsilon^j] the model where \varepsilon_k^j is independent of the component k and, finally, [\varepsilon] the model where \varepsilon_k^j is independent of both the variable $j$ and the component k. In order to maintain some unity in the notation, we will denote also [\varepsilon_k^{jh}] the most general model introduced at the previous section.

Value

an object of [MultinomialModel] containing some of the 10 Binary Models:

Model Prop. Var. Comp.
Binary_p_E Equal TRUE TRUE
Binary_p_Ej FALSE TRUE
Binary_p_Ek TRUE FALSE
Binary_p_Ekj FALSE FALSE
Binary_p_Ekjh FALSE FALSE
Binary_pk_E Free TRUE TRUE
Binary_pk_Ej FALSE TRUE
Binary_pk_Ek TRUE FALSE
Binary_pk_Ekj FALSE FALSE
Binary_pk_Ekjh FALSE FALSE

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]

References

C. Biernacki, G. Celeux, G. Govaert, F. Langrognet. "Model-Based Cluster and Discriminant Analysis with the MIXMOD Software". Computational Statistics and Data Analysis, vol. 51/2, pp. 587-600. (2006)

Examples

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  mixmodMultinomialModel()
  # multinomial models with equal proportions
  mixmodMultinomialModel(equal.proportions=TRUE,free.proportions=FALSE)
  # multinomial models with a pre-defined list
  mixmodMultinomialModel( listModels=c("Binary_pk_E","Binary_p_E") )
  # multinomial models with equal proportions and independent of the variable
  mixmodMultinomialModel(free.proportions=FALSE, variable.independency=TRUE)

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