The risk plot may be one of the most commonly used figures in tumor genetic data analysis. We can conclude the following two points: Comparing the prediction results of the model with the real survival situation to see whether the survival rate of the high-risk group is lower than that of the low-level group, and whether the survival time of the high-risk group is shorter than that of the low-risk group. The other is to compare the heat map and scatter plot to see the correlation between the predictors and the outcome.
| Version: | 1.0 |
| Depends: | R (≥ 2.10) |
| Imports: | ggplot2, survival, egg, do, set, cutoff, fastStat, grid, rms, nomogramFormula |
| Published: | 2020-02-09 |
| Author: | Jing Zhang [aut, cre], Zhi Jin [aut] |
| Maintainer: | Jing Zhang <zj391120 at 163.com> |
| BugReports: | https://github.com/yikeshu0611/ggrisk/issues |
| License: | GPL-2 |
| URL: | https://github.com/yikeshu0611/ggrisk |
| NeedsCompilation: | no |
| CRAN checks: | ggrisk results |
| Reference manual: | ggrisk.pdf |
| Package source: | ggrisk_1.0.tar.gz |
| Windows binaries: | r-devel: ggrisk_1.0.zip, r-release: ggrisk_1.0.zip, r-oldrel: ggrisk_1.0.zip |
| macOS binaries: | r-release: ggrisk_1.0.tgz, r-oldrel: ggrisk_1.0.tgz |
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