Global Envelopes
Global Envelopes
Adjusted global envelope tests
Graphical n sample test of correspondence of distribution functions
Test of independence of two general distributions
The test of local correlations
Convert an envelope or fdata object to a curve_set object
Central region / Global envelope
Combined global scaled maximum absolute difference (MAD) envelope test...
Create a curve set of images
Crop the curves
Create a curve_set object
Deviation test
Functional boxplot
Functional clustering
The FDR envelope
Functional ordering
Rank envelope F-test
F rank functional GLM
Central region plot
Central region plot
Testing global and local dependence of point patterns on covariates
Variogram and residual variogram with global envelopes
Global envelope test
Global quantile regression
One-way graphical functional ANOVA
Graphical functional GLM
Check class.
Functional ordering in parts
Plot method for the class 'combined_fboxplot'
Residual form of the functions
Plot method for the class 'combined_global_envelope'
Plotting function for combined 2d global envelopes
Plot method for the class 'curve_set'
Plot method for the class 'curve_set2d'
Plot method for the class 'fboxplot'
Plot method for the class 'fclust'
Subsetting curve sets
Plot method for the class 'global_envelope'
Plotting function for 2d global envelopes
Print method for the class 'combined_fboxplot'
Print method for the class 'combined_global_envelope'
Print method for the class 'curve_set'
Print method for the class 'deviation_test'
Print method for the class 'fboxplot'
Print method for the class 'fclust'
Print method for the class 'fdr_envelope'
Print method for the class 'GET_contingency'
Print method for the class 'global_envelope'
Global scaled maximum absolute difference (MAD) envelope tests
The rank envelope test
Implementation of global envelopes for a set of general d-dimensional vectors T in various applications. A 100(1-alpha)% global envelope is a band bounded by two vectors such that the probability that T falls outside this envelope in any of the d points is equal to alpha. Global means that the probability is controlled simultaneously for all the d elements of the vectors. The global envelopes can be used 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 analysis of variance, functional general linear model, n-sample test of correspondence of distribution functions), for central regions of functional or multivariate data (e.g. outlier detection, functional boxplot) and for global confidence and prediction bands (e.g. confidence band in polynomial regression, Bayesian posterior prediction). See Myllymäki and Mrkvička (2023) <doi:10.48550/arXiv.1911.06583>, Myllymäki et al. (2017) <doi:10.1111/rssb.12172>, Mrkvička and Myllymäki (2023) <doi:10.1007/s11222-023-10275-7>, Mrkvička et al. (2016) <doi:10.1016/j.spasta.2016.04.005>, Mrkvička et al. (2017) <doi:10.1007/s11222-016-9683-9>, Mrkvička et al. (2020) <doi:10.14736/kyb-2020-3-0432>, Mrkvička et al. (2021) <doi:10.1007/s11009-019-09756-y>, Myllymäki et al. (2021) <doi:10.1016/j.spasta.2020.100436>, Mrkvička et al. (2022) <doi:10.1002/sim.9236>, Dai et al. (2022) <doi:10.5772/intechopen.100124>, Dvořák and Mrkvička (2022) <doi:10.1007/s00180-021-01134-y>, Mrkvička et al. (2023) <doi:10.48550/arXiv.2309.04746>, and Konstantinou et al. (2024) <doi: 10.48550/arXiv.2403.01838>.