Graphical Multiple Comparison Procedures.
graphicalMCP
Introduction
Graphical approaches for multiple comparison procedures (MCPs) are a general framework to control the family-wise error rate strongly at a pre-specified significance level $0<\alpha<1$. This approach includes many commonly used MCPs as special cases and is transparent in visualizing MCPs for better communications. graphicalMCP
is designed to design and analyze graphical MCPs in a flexible, informative and efficient way.
Installation
graphicalMCP
is currently not on CRAN but can be installed from GitHub using the following code:
# install.packages("pak")
pak::pak("Gilead-BioStats/graphicalMCP")
Documentation
- For basic usage instructions, see
vignette("graphicalMCP")
- To become familiar with graphical MCP terminologies, see
vignette("glossary")
- To learn examples of how to use
graphicalMCP
,- see
vignette("shortcut-testing")
for sequentially rejective graphical multiple comparison procedures based on Bonferroni tests - see
vignette("closed-testing")
for graphical multiple comparison procedures based on the closure principle - see
vignette("graph-examples")
for common multiple comparison procedures illustrated usinggraphicalMCP
- see
vignette("generate-closure")
for rationales to generate the closure and the weighting strategy of a graph - see
vignette("comparisons")
for comparisons to other R packages
- see
- To view vignettes in R after properly installing
graphicalMCP
from GitHub, we can build vignettes bydevtools::install(build_vignettes = TRUE)
, and then usebrowseVignettes("graphicalMCP")
to view the full list of vignettes
Related work
- Graphical MCPs - gMCP
- Lighter version of
gMCP
which removes the rJava dependency - gMCPLite - Graphical MCPs with Simes tests - lrstat
Built upon these packages, we hope to implement graphical MCPs in a more general framework, with fewer dependencies and simpler S3 classes, and without losing computational efficiency.
Acknowledgments
Along with the authors and contributors, thanks to the following people for their suggestions and inspirations on the package:
Frank Bretz, Willi Maurer, Ekkehard Glimm, Nan Chen, Jeremy Wildfire, Spencer Childress, Colleen McLaughlin, Matt Roumaya, Chelsea Dickens, and Ron Yu
We owe a debt of gratitude to the authors of gMCP for their pioneering work, without which this package would not be nearly as extensive as it is.