The pls package: Principal component and partial least squares regression in R
The pls package: Principal component and partial least squares regression in R
复制标题
DOI：
10.18637/jss.v018.i02
复制
发表时间：
2007-01-01
影响因子：
5.8
通讯作者：
Wehrens, Ron
中科院分区：
文献类型：
--
作者：
Mevik, Bjorn-Helge;Wehrens, Ron
pls包在R中实现了主成分回归（PCR）和偏最小二乘回归（PLSR）（R Development Core Team 2006 b），可以从Comprehensive R Archive Network（CRAN）免费获得，并在GNU通用公共许可证（GPL）下授权。用户界面仿照传统的公式界面，例如1m。这样做是为了让习惯R的人不必再学习另一个接口，也是因为我们相信公式接口是与模型交互工作的好方法。因此，它具有用于预测，更新和系数等通用功能的方法。它还具有更专业的功能，如评分，加载和RMSEP，以及灵活的交叉验证系统。目视检查和评估在化学计量学中很重要，pls软件包具有许多绘图功能，用于绘制分数，加载，预测，系数和RMSEP估计值。该软件包实现了PCR和PLSR的几种算法。该设计是模块化的，因此应该很容易在其他功能中使用底层算法。我们希望该软件包能够很好地用于交互式数据分析，并作为使用PLSR或PCR的其他功能或软件包的构建块。我们将在这里描述该软件包以及如何将其用于数据分析，以及如何将其用作其他软件包的一部分。还包括一个关于公式和数据框架的部分，供不习惯R建模习惯用法的人使用。
The pls package implements principal component regression ( PCR) and partial least squares regression ( PLSR) in R ( R Development Core Team 2006b), and is freely available from the Comprehensive R Archive Network ( CRAN), licensed under the GNU General Public License ( GPL). The user interface is modelled after the traditional formula interface, as exemplified by 1m. This was done so that people used to R would not have to learn yet another interface, and also because we believe the formula interface is a good way of working interactively with models. It thus has methods for generic functions like predict, update and coef. It also has more specialised functions like scores, loadings and RMSEP, and a flexible cross-validation system. Visual inspection and assessment is important in chemometrics, and the pls package has a number of plot functions for plotting scores, loadings, predictions, coefficients and RMSEP estimates. The package implements PCR and several algorithms for PLSR. The design is modular, so that it should be easy to use the underlying algorithms in other functions. It is our hope that the package will serve well both for interactive data analysis and as a building block for other functions or packages using PLSR or PCR. We will here describe the package and how it is used for data analysis, as well as how it can be used as a part of other packages. Also included is a section about formulas and data frames, for people not used to the R modelling idioms.