Repeated double cross validation
Repeated double cross validation
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DOI：
10.1002/cem.1225
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发表时间：
2009-03-01
影响因子：
2.4
通讯作者：
Varmuza, Kurt
中科院分区：
文献类型：
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作者：
Filzmoser, Peter;Liebmann, Bettina;Varmuza, Kurt
重复双交叉验证(RDCV)是一种策略，用于(A)优化回归模型的复杂性，以及(B)当模型应用于新病例(在所用数据的总体内)时，对预测误差进行现实估计。这种策略适合于小数据集，是对Bootstrap方法的补充。RDCV是已知过程和方法的正式的、部分新的组合，并且已经在编程环境R的函数中实现，为模型评估提供了几种类型的曲线图。该软件的当前版本专门用于通过偏最小二乘法(PLS)获得的回归模型。描述了将数据重复分割成测试集和校准集以及估计最小二乘分量的最佳数目的实用方法。研究了一些参数(CV中的分段数、重复次数)之间的相关性。RDCV被应用于来自化学的两个数据集：(1)从生物乙醇生产的糖浆样品的近红外(NIR)数据确定葡萄糖浓度；(2)从分子描述符建模多环芳香化合物的气相色谱保留指数。使用所有原始变量的模型和使用由遗传算法(GA)选择的一小部分变量的模型通过RDCV进行比较。版权所有0 2009 John Wiley&Sons，Ltd.
Repeated double cross validation (rdCV) is a strategy for (a) optimizing the complexity of regression models and (b) for a realistic estimation of prediction errors when the model is applied to new cases (that are within the population of the data used). This strategy is suited for small data sets and is a complementary method to bootstrap methods. rdCV is a formal, partly new combination of known procedures and methods, and has been implemented in a function for the programming environment R, providing several types of plots for model evaluation. The current version of the software is dedicated to regression models obtained by partial least-squares (PLS). The applied methods for repeated splits of the data into test sets and calibration sets, as well as for estimation of the optimum number of PLS components, are described. The relevance of some parameters (number of segments in CV, number of repetitions) is investigated. rdCV is applied to two data sets from chemistry: (1) determination of glucose concentrations from near infrared (NIR) data in mash samples from bioethanol production; (2) modeling the gas chromatographic retention indices of polycyclic aromatic compounds from molecular descriptors. Models using all original variables and models using a small subset of the variables, selected by a genetic algorithm (GA), are compared by rdCV. Copyright 0 2009 John Wiley & Sons, Ltd.