# logitFD: an R package for functional principal component logit regression

The functional logit regression model was proposed by 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 $$L^2(T)$$ 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.

Manuel Escabias (Department of Statistics and Operation Research, University of Granada) , Ana M. Aguilera (Department of Statistics and Operation Research, University of Granada) , Christian Acal (Department of Statistics and Operation Research, University of Granada)
2022-12-20

## Supplementary materials

M. Escabias, A. M. Aguilera and M. J. Valderrama. Principal component estimation of functional logistic regression: Discussion of two different approaches. Journal of Nonparametric Statistics, 16(3-4): 365–384, 2004. URL https://doi.org/10.1080/10485250310001624738.

### Reuse

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### Citation

Escabias, et al., "The R Journal: logitFD: an R package for functional principal component logit regression", The R Journal, 2022

BibTeX citation

@article{RJ-2022-053,
author = {Escabias, Manuel and Aguilera, Ana M. and Acal, Christian},
title = {The R Journal: logitFD: an R package for functional principal component logit regression},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-053},
doi = {10.32614/RJ-2022-053},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {231-248}
}