SpiceFP: Sparse Method to Identify Joint Effects of Functional Predictors
A set of functions allowing to implement the 'SpiceFP' approach
which is iterative. It involves transformation of functional predictors into
several candidate explanatory matrices (based on contingency tables), to which
relative edge matrices with contiguity constraints are associated. Generalized
Fused Lasso regression are performed in order to identify the best candidate
matrix, the best class intervals and related coefficients at each iteration.
The approach is stopped when the maximal number of iterations is reached or
when retained coefficients are zeros. Supplementary functions allow to get
coefficients of any candidate matrix or mean of coefficients of many candidates.
The methods in this package are describing in Girault Gnanguenon Guesse,
Patrice Loisel, Bénedicte Fontez, Thierry Simonneau, Nadine Hilgert (2021)
"An exploratory penalized regression to identify combined effects of functional
variables -Application to agri-environmental issues"
<https://hal.archives-ouvertes.fr/hal-03298977>.
| Version: |
0.1.2 |
| Depends: |
R (≥ 3.6.0) |
| Imports: |
doParallel, foreach, stringr, tidyr, Matrix, genlasso, purrr |
| Suggests: |
fields |
| Published: |
2023-06-01 |
| DOI: |
10.32614/CRAN.package.SpiceFP |
| Author: |
Girault Gnanguenon Guesse [aut, cre],
Patrice Loisel [aut],
Benedicte Fontez [aut],
Nadine Hilgert [aut],
Thierry Simonneau [ctr],
Isabelle Sanchez [ctr] |
| Maintainer: |
Girault Gnanguenon Guesse <girault.gnanguenon at gmail.com> |
| License: |
GPL-3 |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
SpiceFP results |
Documentation:
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