mixmodGaussianModel(Rmixmod)
mixmodGaussianModel()所属R语言包：Rmixmod
Create an instance of the [<a href="GaussianModel-class.html">GaussianModel</a>] class
创建一个实例[<a href="GaussianModel-class.html"> GaussianModel </ A>]类
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Define a list of Gaussian model to test in MIXMOD.
高斯模型，以测试在MIXMOD定义的列表。
用法----------Usage----------
mixmodGaussianModel(family = "all", listModels = NULL,
free.proportions = TRUE, equal.proportions = TRUE)
参数----------Arguments----------
参数：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".
字符定义一个家庭的模式。对于一般的家庭，“对角线”的的对角线家庭，“球”的球形家庭和“所有”，让所有家庭的“一般”。默认值是“一般”。
参数：listModels
a list of characters containing a list of models. It is optional.
含有列表模型的字符的列表。它是可选的。
参数：free.proportions
logical to include models with free proportions. Default is TRUE.
逻辑模型，包括与免费比例。默认值是TRUE。
参数：equal.proportions
logical to include models with equal proportions. Default is TRUE.
逻辑模型，包括与同等比例。默认值是TRUE。
Details
详细信息----------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 Σ_{k} in terms of its eigenvalue
高斯混合模型，Banfield和拉夫特里（1993）和Celeux Govaert（1995年）之后，我们考虑的参数化表达方差矩阵组成的混合物成分的方差矩阵Σ_{k}的条款，其特征值
where λ_{k}=|Σ_{k}|^{1/d}, D_{k} is the matrix of eigenvectors of Σ_{k} and A_{k} is a diagonal matrix, such that | A_{k} |=1, with the normalized eigenvalues of Σ_{k} on the diagonal in a decreasing order. The parameter λ_{k} determines the volume of the kth 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.
λ_{k}=|Σ_{k}|^{1/d}, D_{k}是Σ_{k}和矩阵的特征向量的A_{k}是一个对角线矩阵，例如，| A_{k} |=1，用规范化的特征值的Σ_{k}中的对角线上递减顺序。参数λ_{k}确定k个簇的体积，D_{k}其取向和A_{k}其形状。通过允许一些但不是所有的这些量的簇之间变化，我们得到吝啬的和很容易解释的适当的模型来描述不同的聚类的情况。
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 λ_{k}, D_{k} and A_{k} (1 ≤q k ≤q 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,…,K. We denote this model [λ_{k}DAD']. With this convention, writing [λ 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 Σ_{k} are diagonal. In the parameterization, it means that the orientation matrices D_{k} are permutation matrices. We write Σ_{k}=λ_{k}B_{k} where B_{k} is a diagonal matrix with | B_{k}|=1. This particular parameterization gives rise to 4 models: [λ B], [λ_{k}B], [λ B_{k}] and [λ_{k}B_{k}].
在一般家庭中，我们可以允许的体积，形状和聚类更改或等于簇之间的取向。假设的参数变化λ_{k}, D_{k}和A_{k}(1 ≤q k ≤q K)导致8个基本模式。例如，我们可以假设不同的卷的形状和方位保持平等的要求A_{k}=A（A未知）和D_{k}=D（D未知）k=1,…,K 。我们表示这模型[λ_{k}DAD']。本公约，写[λ D_{k}AD'_{k}]是指相同体积，相同的形状和不同的方向，我们认为混合模型。以对角线家庭，我们假定方差矩阵Σ_{k}是对角线。在参数化的，这意味着的取向矩阵D_{k}是置换矩阵。我们写Σ_{k}=λ_{k}B_{k}B_{k}是一个对角矩阵，| B_{k}|=1。这种特殊的参数化产生了4种型号：[λ B]，[λ_{k}B]，[λ B_{k}]和[λ_{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: [λ I] and [λ_{k}I].
在球形的家庭中，我们假设球体的形状，即A_{k}=I，I表示的恒等矩阵。在这种情况下，两个吝啬的车型都在竞争：[λ I]和[λ_{k}I]。
值----------Value----------
an object of [GaussianModel] which contains some of the 28 Gaussian Models:
[GaussianModel]其中包含了一些对象的28高斯模型：
（作者）----------Author(s)----------
Remi Lebret and Serge Iovleff and Florent Langrognet,
with contributions from C. Biernacki and G. Celeux and G.
Govaert <a href="mailto:contact@mixmod.org">contact@mixmod.org</a>
参考文献----------References----------
"Model-Based Cluster and Discriminant Analysis with the MIXMOD Software". Computational Statistics and Data Analysis, vol. 51/2, pp. 587-600. (2006)
实例----------Examples----------
mixmodGaussianModel()
mixmodGaussianModel(family="all",free.proportions=FALSE)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-27 00:04:55
