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.

翻译：函数Logit回归模型由Escabias等人（2004）提出，其目标是通过函数型预测变量对二元标量响应变量进行建模。该模型的估计在由有限基函数张成的有限维平方可积函数空间L2(T)的子空间中进行，且假设函数型预测变量的曲线与模型中的函数参数属于同一有限子空间。由此获得的估计受到高度多重共线性问题的影响，而针对这些问题提出的解决方案基于不同的函数主成分分析。本文介绍的logitFD包通过实现不同的解决方案并对2004年提出的模型进行推广（涵盖多个函数型与非函数型预测变量的情况），为这些模型的拟合提供了工具箱。通过使用R-CRAN中fda.usc包所包含的函数型数据数据集，展示了各函数的性能表现。