logitFD：一个用于函数型主成分logit回归的R包
logitFD: an R package for functional principal component logit regression
AI总结：
本文介绍R包logitFD，用于函数型logit回归模型的拟合，通过函数型主成分分析解决多重共线性，并支持多个函数型和非函数型预测变量，附有实例演示。
摘要：
函数型logit回归模型由Escabias等人（2004）提出，其目标是根据函数型预测变量对标量二元响应变量进行建模。该情形下提出的模型估计在L2(T)的有限维平方可积函数子空间中进行，该子空间由一组有限的基函数生成。对于该估计，假设函数型预测变量的曲线和模型的函数型参数属于同一有限子空间。如此获得的估计受到高度多重共线性问题的影响，针对这些问题的解决方案基于不同的函数型主成分分析。本文介绍的logitFD包提供了一个工具箱，通过实现不同的已提出解决方案并将2004年提出的模型推广到多个函数型和非函数型预测变量的情形，来拟合这些模型。函数的性能通过使用R-CRAN中fda.usc包所包含的函数型数据数据集加以说明。
英文摘要：
The functional logit regression model was proposed by Escabias et al. (2004) with the objective of modeling a scalar binary response variable from a functional predictor. The model estimation proposed in that case was performed in a subspace of L2(T) of squared integrable functions of finite dimension, generated by a finite set of basis functions. For that estimation it was assumed that the curves of the functional predictor and the functional parameter of the model belong to the same finite subspace. The estimation so obtained was affected by high multicollinearity problems and the solution given to these problems was based on different functional principal component analysis. The logitFD package introduced here provides a toolbox for the fit of these models by implementing the different proposed solutions and by generalizing the model proposed in 2004 to the case of several functional and non-functional predictors. The performance of the functions is illustrated by using data sets of functional data included in the fda.usc package from R-CRAN.
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。
