GET - course: Tomas Mrkvicka, Mari Myllymaki
Content
Introduction to the methodology of GET
R-session: Example of a goodness-of-fit test - A variogram example - A point pattern example, simple and composite hypotheses
False discovery rate (FDR) envelope
R-session: Example of FDR envelope - Local spatial correlation
Functional general linear model (GLM), permutation strategies for testing
R-session: Example of GLM (FDR envelopes) - Population growth example
Vignettes
Main GET functions
- create_curve_set(): Create a curve_set out of data given in the right form
- crop_curves(): Crop curves
- forder(): Different measures for ordering the multivariate statistics from the most extreme to least extreme one
- central_region(): Central regions or global envelopes or conĄdence bands with
IGI
- global_envelope_test(): Global envelope tests - FWER / FDR
- GET.composite(): Adjusted global envelope tests for composite null hypotheses
- fBoxplot(): Functional boxplot based on a central region with IGI
- graph.fanova(): One-way ANOVA tests for functional data with graphical interpretation
- frank.fanova(): One-way functional ANOVA tests based on the global envelopes applied to F values
- graph.flm(): Non-parametric graphical tests of signiĄcance in functional
general linear model (GLM)
- frank.flm(): Global envelope tests applied to F values in permutation
inference for the GLM
- fclustering(): Functional clustering based on a speciĄed measure
- GET.necdf(): Graphical n sample test of correspondence of distribution
functions
- global_envelope_test(): Global envelope tests
- GET.composite(): Adjusted global envelope tests for composite null hypotheses
- GET.distrindep(): Permutation-based tests of independence to samples from
any bivariate distribution
- GET.spatialF(): Testing global and local covariate effects in point process
models