Department of Applied Statistics, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040 Linz, Austria.
Bioinformatics. 2012 Jan 15;28(2):222-8. doi: 10.1093/bioinformatics/btr653. Epub 2011 Nov 26.
A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time.
The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).
提出了一个线性可加模型的有限混合模型类。使用正则化似然方法估计回归模型中的组件特定参数。正则化的优点是：（i）样条的预指定最大自由度比非正则化估计不太关键，以及（ii）对于每个组件，以自动方式选择合适的自由度。在使用人工数据的模拟研究以及酵母细胞周期数据集的基因表达水平随时间变化的研究中评估了性能。
最新版本的 R 包 flexmix 可从 CRAN（http://cran.r-project.org/）获得。
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