Plotting Decision Curve Analysis with Coloured Bars
Overview
Decision curve analysis is a method for evaluating and comparing prediction models that incorporates clinical consequences, requires only the data set on which the models are tested, and can be applied to models that have either continuous or dichotomous results. The 'ggscidca' package adds coloured bars of discriminant relevance to the traditional decision curve. Improved practicality and aesthetics. This method was described by Balachandran VP (2015) doi:10.1016/S1470-2045(14)71116-7.
Install
Health
NOTERd contentsr-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc
Rd files without \usage: ‘cmprskstdca.Rd’ ‘dca.Rd’ ‘stdca.Rd’ \arguments should not be documented without \usage.
- NOTE11 OK, 2 NOTENow
- NOTE12 OK, 1 NOTE2026-07-11
- OK14 OK2026-03-10
Documentation
- Help pages
- 28
- Examples that run
- 100%5 run in a plain check
- Exported functions with examples
- 14%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 0%
Downloads
Dependencies
Installing ggscidca adds 31 packages (20 with compiled code, 37 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Package metadata
- First published
- 2024-03-04
- Total releases
- 8 / 2 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 2.10
- Bundled data
- 63 KB / 4 files
- Download size
- 90 KB
Cite
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Run in R for the authors' preferred citation:
citation("ggscidca")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
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