multimode-package: Mode testing and exploring.
In multimode: Mode Testing and Exploring
Description
Details
Acknowledgements
References
Description
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer map) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Details
Package: multimode
Type: Package
Version: 1.5
Date: 2021-03-17
License: GPL-3
NeedsCompilation: yes
LazyData: yes
This package incorporates the function modetest which tests if the number of modes of a sample is equal to a given number (against if it is greater).
Functions bw.crit and excessmass provide the critical bandwidth and the excess mass statistic, respectively.
Function nmodes computes the number of modes for a given bandwidth. Given a certain number of modes, function locmodes provides the estimation of the locations of modes and antimodes and their density value.
Functions modetree and modeforest provide the mode tree and forest, respectively; they represent the estimated mode locations for different bandwidths. Function sizer can be used for determining where the smoothed curve is significantly increasing or decreasing. Registries with missing data are removed.
For a complete list of functions, use library(help="multimode").
Acknowledgements
This work has been supported by Projects MTM2016–76969–P (Spanish State Research Agency, AEI) and MTM2013–41383–P (Spanish Ministry of Economy, Industry and Competitiveness), both co–funded by the European Regional Development Fund (ERDF), IAP network (Developing crucial Statistical methods for Understanding major complex Dynamic Systems in natural, biomedical and social sciences, StUDyS) from Belgian Science Policy. Work of J. Ameijeiras-Alonso has been supported by the PhD grant BES-2014-071006 from the Spanish Ministry of Economy and Competitiveness.
References
Ameijeiras-Alonso, J., Crujeiras, R.M. and Rodríguez-Casal, A. (2019). Mode testing, critical bandwidth and excess mass, Test, 28, 900–919.
Ameijeiras-Alonso, J., Crujeiras, R.M. and Rodríguez-Casal, A. (2021). multimode: An R Package for Mode Assessment, Journal of Statistical Software, 97, 1–32.
Chaudhuri, P. and Marron, J. S. (1999). SiZer for exploration of structures in curves, Journal of the American Statistical Association, 94, 807–823.
Minnotte, M. C., Marchette, D. J. and Wegman, E. J. (1998). The bumpy road to the mode forest, Journal of Computational and Graphical Statistics, 7, 239–251.
multimode documentation built on March 21, 2021, 1:06 a.m.
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Description Details Acknowledgements References
Description
Different examples and methods for testing (including different proposals described in Ameijeiras-Alonso et al., 2019 <DOI:10.1007/s11749-018-0611-5>) and exploring (including the mode tree, mode forest and SiZer map) the number of modes using nonparametric techniques <DOI:10.18637/jss.v097.i09>.
Details
| Package: | multimode |
| Type: | Package |
| Version: | 1.5 |
| Date: | 2021-03-17 |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| LazyData: | yes |
This package incorporates the function modetest which tests if the number of modes of a sample is equal to a given number (against if it is greater).
Functions bw.crit and excessmass provide the critical bandwidth and the excess mass statistic, respectively.
Function nmodes computes the number of modes for a given bandwidth. Given a certain number of modes, function locmodes provides the estimation of the locations of modes and antimodes and their density value.
Functions modetree and modeforest provide the mode tree and forest, respectively; they represent the estimated mode locations for different bandwidths. Function sizer can be used for determining where the smoothed curve is significantly increasing or decreasing. Registries with missing data are removed.
For a complete list of functions, use library(help="multimode").
Acknowledgements
This work has been supported by Projects MTM2016–76969–P (Spanish State Research Agency, AEI) and MTM2013–41383–P (Spanish Ministry of Economy, Industry and Competitiveness), both co–funded by the European Regional Development Fund (ERDF), IAP network (Developing crucial Statistical methods for Understanding major complex Dynamic Systems in natural, biomedical and social sciences, StUDyS) from Belgian Science Policy. Work of J. Ameijeiras-Alonso has been supported by the PhD grant BES-2014-071006 from the Spanish Ministry of Economy and Competitiveness.
References
Ameijeiras-Alonso, J., Crujeiras, R.M. and Rodríguez-Casal, A. (2019). Mode testing, critical bandwidth and excess mass, Test, 28, 900–919.
Ameijeiras-Alonso, J., Crujeiras, R.M. and Rodríguez-Casal, A. (2021). multimode: An R Package for Mode Assessment, Journal of Statistical Software, 97, 1–32.
Chaudhuri, P. and Marron, J. S. (1999). SiZer for exploration of structures in curves, Journal of the American Statistical Association, 94, 807–823.
Minnotte, M. C., Marchette, D. J. and Wegman, E. J. (1998). The bumpy road to the mode forest, Journal of Computational and Graphical Statistics, 7, 239–251.
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