Sparse Method to Identify Joint Effects of Functional Predictors.
SpiceFP
Sparse and Structured Procedure to Identify Combined 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 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.
Installation
To install the SpiceFP package, the easiest is to install it directly from GitHub. Open an R session and run the following commands:
library(remotes)
install_github("giraultg/SpiceFP", build_vignettes=TRUE)
Usage
Once the package is installed on your computer, it can be loaded into a R session:
library(SpiceFP)
help(package="SpiceFP")
Citation
As a lot of time and effort were spent in creating the SpiceFP method, please cite it when using it for data analysis:
METHODO PAPER CITATION IS COMING SOON.
You should also cite the SpiceFP package:
citation("SpiceFP")
See also citation() for citing R itself.
References
- Taylor B. Arnold and Ryan J. Tibshirani (2020). genlasso: Path Algorithm for Generalized Lasso Problems. R package version 1.5. https://CRAN.R-project.org/package=genlasso.