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Rmixmod (version 2.1.10)

mixmodGaussianModel: Create an instance of the [GaussianModel] class

Description

Define a list of Gaussian model to test in MIXMOD.

Usage

mixmodGaussianModel(
  family = "all",
  listModels = NULL,
  free.proportions = TRUE,
  equal.proportions = TRUE
)

Value

an object of [GaussianModel] which contains some of the 28 Gaussian Models:

ModelFamilyProp.VolumeShapeOrient.
Gaussian_p_L_CGeneralEqualEqualEqualEqual
Gaussian_p_Lk_CFreeEqualEqual
Gaussian_p_L_D_Ak_DEqualFreeEqual
Gaussian_p_Lk_D_Ak_DFreeFreeEqual
Gaussian_p_L_Dk_A_DkEqualEqualFree
Gaussian_p_Lk_Dk_A_DkFreeEqualFree
Gaussian_p_L_CkEqualFreeFree
Gaussian_p_Lk_CkFreeFreeFree
Gaussian_p_L_BDiagonalEqualEqualEqualAxes
Gaussian_p_Lk_BFreeEqualAxes
Gaussian_p_L_BkEqualFreeAxes
Gaussian_p_Lk_BkFreeFreeAxes
Gaussian_p_L_ISphericalEqualEqualEqualNA
Gaussian_p_Lk_IFreeEqualNA
Gaussian_pk_L_CGeneralFreeEqualEqualEqual
Gaussian_pk_Lk_CFreeEqualEqual
Gaussian_pk_L_D_Ak_DEqualFreeEqual
Gaussian_pk_Lk_D_Ak_DFreeFreeEqual
Gaussian_pk_L_Dk_A_DkEqualEqualFree
Gaussian_pk_Lk_Dk_A_DkFreeEqualFree
Gaussian_pk_L_CkEqualFreeFree
Gaussian_pk_Lk_CkFreeFreeFree
Gaussian_pk_L_BDiagonalFreeEqualEqualAxes
Gaussian_pk_Lk_BFreeEqualAxes
Gaussian_pk_L_BkEqualFreeAxes
Gaussian_pk_Lk_BkFreeFreeAxes
Gaussian_pk_L_ISphericalFreeEqualEqualNA
Gaussian_pk_Lk_IFreeEqualNA

Arguments

family

character defining a family of models. "general" for the general family, "diagonal" for the diagonal family, "spherical" for the spherical family and "all" for all families. Default is "general".

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 TRUE.

Author

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

Details

In the Gaussian mixture model, following Banfield and Raftery (1993) and Celeux and Govaert (1995), we consider a parameterization of the variance matrices of the mixture components consisting of expressing the variance matrix \(\Sigma_{k}\) in terms of its eigenvalue decomposition $$ \Sigma_{k}= \lambda_{k} D_{k} A_{k}D'_{k}$$ where \(\lambda_{k}=|\Sigma_{k}|^{1/d}, D_{k}\) is the matrix of eigenvectors of \(\Sigma_{k}\) and \(A_{k}\) is a diagonal matrix, such that \(| A_{k} |=1\), with the normalized eigenvalues of \(\Sigma_{k}\) on the diagonal in a decreasing order. The parameter \(\lambda_{k}\) determines the volume of the \(k\)th cluster, \(D_{k}\) its orientation and \(A_{k}\) its shape. By allowing some but not all of these quantities to vary between clusters, we obtain parsimonious and easily interpreted models which are appropriate to describe various clustering situations.

In general family, we can allow the volumes, the shapes and the orientations of clusters to vary or to be equal between clusters. Variations on assumptions on the parameters \(\lambda_{k}, D_{k}\) and \(A_{k}\) \((1 \leq k \leq K)\) lead to 8 general models of interest. For instance, we can assume different volumes and keep the shapes and orientations equal by requiring that \(A_{k}=A\) (\(A\) unknown) and \(D_{k}=D\) (\(D\) unknown) for \(k=1,\ldots,K\). We denote this model \([\lambda_{k}DAD']\). With this convention, writing \([\lambda D_{k}AD'_{k}]\) means that we consider the mixture model with equal volumes, equal shapes and different orientations. In diagonal family, we assume that the variance matrices \(\Sigma_{k}\) are diagonal. In the parameterization, it means that the orientation matrices \(D_{k}\) are permutation matrices. We write \(\Sigma_{k}=\lambda_{k}B_{k}\) where \(B_{k}\) is a diagonal matrix with \(| B_{k}|=1\). This particular parameterization gives rise to 4 models: \([\lambda B]\), \([\lambda_{k}B]\), \([\lambda B_{k}]\) and \([\lambda_{k}B_{k}]\).

In spherical family, we assume spherical shapes, namely \(A_{k}=I\), \(I\) denoting the identity matrix. In such a case, two parsimonious models are in competition: \([\lambda I]\) and \([\lambda_{k}I]\).

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

Run this code
mixmodGaussianModel()
# all Gaussian models with equal proportions
mixmodGaussianModel(family = "all", free.proportions = FALSE)
# Diagonal and Spherical Gaussian models
mixmodGaussianModel(family = c("diagonal", "spherical"))
# Gaussian models with a pre-defined list
mixmodGaussianModel(listModels = c("Gaussian_p_L_C", "Gaussian_p_L_Ck", "Gaussian_pk_L_I"))

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