Add-on pacakge for the R package deepregression
to fit structured additive models when the number of factor levels is prohibitively large and needs an efficient implementation, when fitting varying coefficient terms with a factorization approach, factorized effects in general, (higher-order) factorization machines, or additive (higher-order) factorization machines.
In (one of) your formula(s) in deepregression
you can use any function (e.g., fm
) and pass the respective processor (e.g., hofm_processor
) via the additional_processors
argument in deepregression
. For example:
mod <- deepregression(
y = y,
list_of_formulas = list(~ 1 + age + fm(V1, V2, V3, V4, V5, V6)),
data = data,
family = "bernoulli",
additional_processors = list(fm = hofm_processor)
)
The following processors are available:
fac_processor
: efficient computation of categorical effects with many factor levelsinteraction_processor
: same as fac_processor
but for interactions of two categorical effectsvc_processor
: efficient computation of varying coefficients (interaction of smooth and one or two categorical effect)am_processor
: same as vc_processor
but using an linear array model-type formulation (only for one categorical effect)fz_processor
: computes a matrix factorization for two or three categorical effect interactionsvf_processor
: same as vc_processor
with two levels, but using a factorization approach as in the fz_processor
hofm_processor
: computes (higher-order) factorization machines for a given set of featuresafm_processor
: computes additive factorization machines for a given set of featuresahofm_processor
: computes additive higher-order factorization machines for a given set of featuresWhen using or referencing the contents of this package, cite
@InProceedings{FaStR,
title={Factorized Structured Regression for Large-Scale Varying Coefficient Models},
author={David R{\"u}gamer and Andreas Bender and Simon Wiegrebe and Daniel Racek and Bernd Bischl and Christian M{\"u}ller and Clemens Stachl},
year={2023},
publisher={Springer International Publishing},
booktitle="Machine Learning and Knowledge Discovery in Databases",
publisher="Springer Nature Switzerland",
address="Cham",
pages="20--35"
}
for the efficient factor effect implementation or (time-varying) factorization approaches and
@InProceedings{AFM,
title = {Scalable Higher-Order Tensor Product Spline Models},
author = {David R\"ugamer},
year={2024},
booktitle = {Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
series = {Proceedings of Machine Learning Research},
publisher = {PMLR}
}
for (higher-order) factorization machines or additive (higher-order) factorization machines.