plsRglm：通过R的交叉验证和Bootstrap技术处理不完整数据集的偏最小二乘线性和广义线性回归
plsRglm: Partial least squares linear and generalized linear regression for processing incomplete datasets by cross-validation and bootstrap techniques with R
摘要：
plsRglm包的目的是通过几种新技术来处理完整和不完整的数据集，或者至少是一些尚未在R中实现的技术。事实上，它不仅使PLS回归的推广到广义线性回归模型，而且还bootstrap技术，留一法和重复的$k$倍交叉验证。此外，图形显示有助于用户评估使用自举技术时预测因子的重要性。双标图（图4）可用于深入研究个体与变量之间的关系。
英文摘要：
The aim of the plsRglm package is to deal with complete and incomplete datasets through several new techniques or, at least, some which were not yet implemented in R. Indeed, not only does it make available the extension of the PLS regression to the generalized linear regression models, but also bootstrap techniques, leave-one-out and repeated $k$-fold cross-validation. In addition, graphical displays help the user to assess the significance of the predictors when using bootstrap techniques. Biplots (Fig. 4) can be used to delve into the relationship between individuals and variables.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
