Global Envelopes.
GET: Global envelopes
https://cran.r-project.org/package=GET
The R package GET provides global envelopes which can be used for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot), for graphical Monte Carlo and permutation tests where the test statistic is a multivariate vector or function (e.g. goodness-of-fit testing for point patterns and random sets, functional ANOVA, functional GLM, n-sample test of correspondence of distribution functions), and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction).
The development version
The github repository holds a copy of the current development version of the contributed R package GET.
This development version is as or more recent than the official release of GET on the Comprehensive R Archive Network (CRAN) at https://cran.r-project.org/package=GET
Where is the official release?
For the most recent official release of GET, see https://cran.r-project.org/package=GET
Installation
Installing the official release
To install the official release of GET from CRAN, start R and type
install.packages('GET')
Installing the development version
The easiest way to install the GET library from github is through the remotes package. Start R and type:
require(remotes)
install_github('myllym/GET')
If you do not have the R library remotes installed, install it first by running
install.packages("remotes")
After installation, in order to start using GET, load it to R and see the main help page, which describes the functions of the library:
require(GET)
help('GET-package')
If you want to have also vignettes working, you should also install packages from the 'suggests' field, have MiKTeX on your computer, and install the library with
install_github('myllym/GET', build_vignettes = TRUE)
Vignettes
The package contains four vignettes. The GET vignette describes the package in general. It is available by starting R and typing
library("GET")
vignette("GET")
This vignette corresponds to Myllymäki and Mrkvička (2023).
The package provides also a vignette for global envelopes for point pattern analyses, which is available by starting R and typing
library("GET")
vignette("pointpatterns")
The third vignette describes and provides code for the examples of Mrkvička and Myllymäki (2023) using the false discovery rate (FDR) envelopes,
library("GET")
vignette("FDRenvelopes")
Finally, the fourth vignette, available by
library("GET")
vignette("HotSpots")
shows how the methodology proposed by Mrkvička et al. (2023b) for detecting hotspots on a linear network can be performed using GET.
All vignettes are also available at the package webpage https://cran.r-project.org/package=GET
Branches
Currently two branches are provided in the development version. The main branch of GET is called master.
The other branches are called FDR and quantileregression. The FDR branch includes also the experimental FDR envelopes tested in Mrkvička and Myllymäki (2023). The main branch includes the FDR envelopes which were found to have good performance in Mrkvička and Myllymäki (2023).
We note that the quantileregression branch, which included the implementation of the global quantile regression proposed in Mrkvička et al. (2023a), was recently merger to the master.
References
To cite GET in publications use
Myllymäki, M. and Mrkvička, T. (2023). GET: Global envelopes in R. arXiv:1911.06583 [stat.ME] https://doi.org/10.48550/arXiv.1911.06583
Myllymäki, M., Mrkvička, T., Grabarnik, P., Seijo, H. and Hahn, U. (2017). Global envelope tests for spatial processes. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 79: 381-404. doi: 10.1111/rssb.12172 http://dx.doi.org/10.1111/rssb.12172 (You can find the preprint version of the article here: http://arxiv.org/abs/1307.0239v4)
and a suitable selection of:
Myllymäki, M., Grabarnik, P., Seijo, H., and Stoyan, D. (2015). Deviation test construction and power comparison for marked spatial point patterns. Spatial Statistics 11: 19-34. https://doi.org/10.1016/j.spasta.2014.11.004 (You can find the preprint version of the article here: http://arxiv.org/abs/1306.1028)
Mrkvička, T., Soubeyrand, S., Myllymäki, M., Grabarnik, P., and Hahn, U. (2016). Monte Carlo testing in spatial statistics, with applications to spatial residuals. Spatial Statistics 18, Part A: 40--53. https://doi.org/10.1016/j.spasta.2016.04.005
Mrkvička, T., Myllymäki, M. and Hahn, U. (2017). Multiple Monte Carlo testing, with applications in spatial point processes. Statistics and Computing 27 (5): 1239-1255. https://doi.org/10.1007/s11222-016-9683-9
Mrkvička, T., Myllymäki, M., Jilek, M. and Hahn, U. (2020). A one-way ANOVA test for functional data with graphical interpretation. Kybernetika 56 (3), 432-458. http://doi.org/10.14736/kyb-2020-3-0432
Myllymäki, M., Kuronen, M. and Mrkvička, T. (2020). Testing global and local dependence of point patterns on covariates in parametric models. Spatial Statistics 42, 100436. https://doi.org/10.1016/j.spasta.2020.100436
Mrkvička, T., Roskovec, T. and Rost, M. (2021). A nonparametric graphical tests of significance in functional GLM. Methodology and Computing in Applied Probability 23, 593-612. https://doi.org/10.1007/s11009-019-09756-y
Dai, W., Athanasiadis, S. and Mrkvička, T. (2022). A new functional clustering method with combined dissimilarity sources and graphical interpretation. Intech open. https://doi.org/10.5772/intechopen.100124
Dvořák, J. and Mrkvička, T. (2022). Graphical tests of independence for general distributions. Computational Statistics 37, 671--699. https://doi.org/10.1007/s00180-021-01134-y
Mrkvička, T., Myllymäki, M., Kuronen, M. and Narisetty, N. N. (2022). New methods for multiple testing in permutation inference for the general linear model. Statistics in Medicine 41(2), 276-297. https://doi.org/10.1002/sim.9236
Mrkvička and Myllymäki (2023). False discovery rate envelopes. Statistics and Computing 33, 109. https://doi.org/10.1007/s11222-023-10275-7
Mrkvička, T., Konstantinou, K., Kuronen, M. and Myllymäki, M. (2023a). Global quantile regression. arXiv:2309.04746 [stat.ME] https://doi.org/10.48550/arXiv.2309.04746
Mrkvička T., Kraft S., Blažek V., Myllymäki M. (2023b). Hotspot detection on a linear network in the presence of covariates: a case study on road crash data. Available at SSRN: http://dx.doi.org/10.2139/ssrn.4627591