Kernel semi-parametric models and their equivalence with linear mixed models provide analysts with the flexibility of machine learning methods and a foundation for inference and tests of hypothesis. These models are not impacted by the number of predictor variables, since the kernel trick transforms them to a kernel matrix whose size only depends on the number of subjects. Hence, methods based on this model are appealing and numerous, however only a few R programs are available and none includes a complete set of features. Here, we present the KSPM package to fit the kernel semi-parametric model and its extensions in a unified framework. KSPM allows multiple kernels and unlimited interactions in the same model. It also includes predictions, statistical tests, variable selection procedure and graphical tools for diagnostics and interpretation of variable effects. Currently KSPM is implemented for continuous dependent variables but could be extended to binary or survival outcomes.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2021-012.zip
KSPM, coxme, SKAT, KRLS, e1071, SPA3G, np, mgcv, lme4, nlme, DEoptim, adegenet, CompQuadForm
Econometrics, Environmetrics, SocialSciences, Psychometrics, Bayesian, Distributions, OfficialStatistics, SpatioTemporal, ChemPhys, Cluster, Finance, Genetics, MachineLearning, Multivariate, Optimization, Spatial, Survival
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For attribution, please cite this work as
Schramm, et al., "The R Journal: KSPM: A Package For Kernel Semi-Parametric Models", The R Journal, 2021
BibTeX citation
@article{RJ-2021-012,
author = {Schramm, Catherine and Jacquemont, Sébastien and Oualkacha, Karim and Labbe, Aurélie and Greenwood, Celia M.T.},
title = {The R Journal: KSPM: A Package For Kernel Semi-Parametric Models},
journal = {The R Journal},
year = {2021},
note = {https://doi.org/10.32614/RJ-2021-012},
doi = {10.32614/RJ-2021-012},
volume = {12},
issue = {2},
issn = {2073-4859},
pages = {189-208}
}