literanger-package: 'literanger': Random Forests for Multiple Imputation based on...

In literanger: Random Forests for Multiple Imputation Based on 'ranger'

literanger: Random Forests for Multiple Imputation based on 'ranger'.

Description

'literanger' is an adaption of the 'ranger' R package for training and predicting from random forest models within multiple imputation algorithms. ranger is a fast implementation of random forests (Breiman, 2001) or recursive partitioning, particularly suited for high dimensional data (Wright et al, 2017a). literanger enables random forests to be embedded in the fully conditional specification framework for multiple imputation known as "Multiple Imputation via Chained Equations" (Van Buuren 2007).

Details

literanger trains classification and regression forests. The trained forest retains information about the in-bag responses in each terminal node, thus facilitating computationally efficient prediction within multiple imputation with random forests proposed by Doove et al (2014). This multiple imputation algorithm has better predictive distribution properties than competing approaches which use predictive mean matching. Alternatively, the usual bagged prediction may be used as in the imputation algorithm called 'missForest' (Stekhoven et al, 2014).

Classification and regression forests are implemented as in the original Random Forest (Breiman, 2001) or using extremely randomized trees (Geurts et al, 2006). 'data.frame', 'matrix', and sparse matrices ('dgCmatrix') are supported.

Split selection may be based on improvement in metrics such as the variance, Gini impurity, beta log-likelihood (Weinhold et al, 2019), Hellinger distance (Cieslak et al, 2012) or maximally selected rank statistics (Wright et al, 2017b).

See https://github.com/imbs-hl/ranger for the development version of ranger or https://gitlab.com/stephematician/literanger for development version of this package.

For alternative approaches to multiple imputation that employ random forests, see 'missRanger' https://cran.r-project.org/package=missRanger and 'miceRanger' https://cran.r-project.org/package=miceRanger, which use predictive mean matching combined with the original 'ranger' algorithm.

This package was adapted from the 'ranger' package for R Statistical Software. The C++ core is provided under the same license terms as the original C++ core in the 'ranger' package, namely the MIT license https://www.r-project.org/Licenses/MIT. The wrappers in this package around the core are licensed under the same terms of the corresponding components in the 'ranger' R package, namely the GPL3 license https://www.r-project.org/Licenses/GPL-3, https://www.gnu.org/licenses/.

License

'literanger' was adapted from the 'ranger' package for R statistical software. ranger was authored by Marvin N. Wright with the GNU General Public License version 3 for the R package (interface), while the C++ core of ranger has the MIT license. The adaptation was performed by stephematician in 2023. literanger carries the same license, terms, and permissions as ranger, including the GNU General Public License 3 for the R package interface, and the MIT license for the C++ core.

License statement for C++ core of ranger:

MIT License

Copyright (c) [2014-2018] [Marvin N. Wright]

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the “Software”), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED “AS IS”, WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
 AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

License statement for the ranger R package interface:

Ranger is free software: you can redistribute it and/or modify it under
the terms of the GNU General Public License as published by the Free
Software Foundation, either version 3 of the License, or (at your option)
any later version.

Ranger is distributed in the hope that it will be useful, but WITHOUT ANY
WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS
FOR A PARTICULAR PURPOSE. See the GNU General Public License for more
details.

You should have received a copy of the GNU General Public License along
with Ranger. If not, see <https://www.gnu.org/licenses/>.

Written by:
Marvin N. Wright
Institut für Medizinische Biometrie und Statistik
Universität zu Lübeck
Ratzeburger Allee 160
23562 Lübeck
https://www.imbs.uni-luebeck.de/institut

Author(s)

stephematician [e-mail], Marvin N Wright (original 'ranger' package)

References

• Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1023/A:1010933404324")}.

• Cieslak, D. A., Hoens, T. R., Chawla, N. V., & Kegelmeyer, W. P. (2012). Hellinger distance decision trees are robust and skew-insensitive. Data Mining and Knowledge Discovery, 24, 136-158. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10618-011-0222-1")}.

• Doove, L. L., Van Buuren, S., & Dusseldorp, E. (2014). Recursive partitioning for missing data imputation in the presence of interaction effects. Computational Statistics & Data Analysis, 72, 92-104. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.csda.2013.10.025")}.

• Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3-42. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1007/s10994-006-6226-1")}.

• Stekhoven, D.J. and Buehlmann, P. (2012). MissForest–non-parametric missing value imputation for mixed-type data. Bioinformatics, 28(1), 112-118. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/bioinformatics/btr597")}.

• Van Buuren, S. (2007). Multiple imputation of discrete and continuous data by fully conditional specification. Statistical Methods in Medical Research, 16(3), 219-242. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/0962280206074463")}.

• Weinhold, L., Schmid, M., Wright, M. N., & Berger, M. (2019). A random forest approach for modeling bounded outcomes. arXiv preprint, arXiv:1901.06211. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1901.06211")}.

• Wright, M. N., & Ziegler, A. (2017a). ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R. Journal of Statistical Software, 77, 1-17. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v077.i01")}.

• Wright, M. N., Dankowski, T., & Ziegler, A. (2017b). Unbiased split variable selection for random survival forests using maximally selected rank statistics. Statistics in medicine, 36(8), 1272-1284. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1002/sim.7212")}.

See Also

Useful links:

• https://gitlab.com/stephematician/literanger

• Report bugs at https://gitlab.com/stephematician/literanger/-/issues

literanger documentation built on Sept. 30, 2024, 9:15 a.m.