NPCirc-package {NPCirc} | R Documentation |
Nonparametric smoothing methods for density and regression estimation involving circular data, including methods described in Oliveira et al. (2014) and proposals in Alonso-Pena et al. (2021).
Package: | NPCirc |
Type: | Package |
Version: | 3.0.1 |
Date: | 2021-07-21 |
License: | GPL-2 |
LazyLoad: | yes |
This package incorporates the function kern.den.circ
which computes the
circular kernel density estimator. For choosing the smoothing parameter different functions are available: bw.rt
, bw.CV
,
bw.pi
, and bw.boot
. For regression involving circular variables, the package includes the functions: kern.reg.circ.lin
for a circular
covariate and linear response; kern.reg.circ.circ
for a circular covariate and a circular response; kern.reg.lin.circ
for a linear covariate
and a circular response. The three functions compute Nadaraya-Watson and Local-Linear smoothers. The functions bw.reg.circ.lin
,
bw.reg.circ.circ
and bw.reg.circ.lin
implement cross–validation rules for selecting the smoothing parameter. Functions noeffect.circ.lin
, noeffect.circ.circ
and noeffect.lin.circ
compute the test of no effect to assess the significance of the predictor variable. Additionally, functions ancova.circ.lin
, ancova.circ.circ
and ancova.lin.circ
implement hypothesis testing tools to assess the equality and parallelism of regression curves across different groups of observations.
Functions circsizer.density
and circsizer.re-
gression
provide CircSiZer maps for kernel density estimation and regression estimation, respectively.
Functions dcircmix
and rcircmix
compute the density function and generate random samples of a circular distribution or a mixture of circular
distributions, allowing for different components such as the circular uniform, von Mises, cardioid, wrapped Cauchy, wrapped normal and wrapped skew-normal.
Finally, some data sets are provided. Missing data are allowed. Registries with missing data are simply removed.
For a complete list of functions, use library(help="NPCirc").
This work has been supported by Project MTM2008-03010 from the Spanish Ministry of Science; Project and MTM201676969-P from the AEI co-funded by the European Regional Development Fund (ERDF), the Competitive Reference Groups 2017-2020 (ED431C 2017/38) from the Xunta de Galicia through the ERDF; and Innovation 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 Mar?a Alonso-Pena was supported by grant ED481A-2019/139 from the Xunta de Galicia. Work of Jose Ameijeiras-Alonso was supported by the FWO research project G.0826.15N (Flemish Science Foundation); and GOA/12/014 project (Research Fund KU Leuven). The authors want to acknowledge Prof. Arthur Pewsey for facilitating data examples and for his comments.
Mar?a Oliveira, Mar?a Alonso-Pena, Jose Ameijeiras-Alonso, Rosa M. Crujeiras and Alberto Rodr?guez–Casal
Maintainer: Mar?a Alonso-Pena mariaalonso.pena@usc.es
Oliveira, M., Crujeiras, R.M. and Rodr?guez–Casal, A. (2012) A plug–in rule for bandwidth selection in circular density. Computational Statistics and Data Analysis, 56, 3898–3908.
Oliveira, M., Crujeiras R.M. and Rodr?guez–Casal, A. (2013) Nonparametric circular methods for exploring environmental data. Environmental and Ecological Statistics, 20, 1–17.
Oliveira, M., Crujeiras, R.M. and Rodr?guez–Casal (2014) CircSiZer: an exploratory tool for circular data. Environmental and Ecological Statistics, 21, 143–159.
Oliveira, M., Crujeiras R.M. and Rodr?guez–Casal, A. (2014) NPCirc: an R package for nonparametric circular methods. Journal of Statistical Software, 61(9), 1–26. https://www.jstatsoft.org/v61/i09/
Alonso-Pena, M., Ameijeiras-Alonso, J. and Crujeiras, R.M. (2021) Nonparametric tests for circular regression. Journal of Statistical Computation and Simulation, 91(3), 477–500.