cutoff: Seek the Significant Cutoff Value

Seek the significant cutoff value for a continuous variable, which will be transformed into a classification, for linear regression, logistic regression, logrank analysis and cox regression. First of all, all combinations will be gotten by combn() function. Then n.per argument, abbreviated of total number percentage, will be used to remove the combination of smaller data group. In logistic, Cox regression and logrank analysis, we will also use p.per argument, patient percentage, to filter the lower proportion of patients in each group. Finally, p value in regression results will be used to get the significant combinations and output relevant parameters. In this package, there is no limit to the number of cutoff points, which can be 1, 2, 3 or more. Still, we provide 2 methods, typical Bonferroni and Duglas G (1994) <doi:10.1093/jnci/86.11.829>, to adjust the p value, Missing values will be deleted by na.omit() function before analysis.

Version:	1.3
Imports:	survival, set, do, ROCit
Published:	2019-12-20
Author:	Jing Zhang [aut, cre], Zhi Jin [aut]
Maintainer:	Jing Zhang <zj391120 at 163.com>
BugReports:	https://github.com/yikeshu0611/cutoff/issues
License:	GPL-3
URL:	https://github.com/yikeshu0611/cutoff
NeedsCompilation:	no
CRAN checks:	cutoff results

Documentation:

Reference manual:

cutoff.pdf

Downloads:

Package source:	cutoff_1.3.tar.gz
Windows binaries:	r-devel: cutoff_1.3.zip, r-release: cutoff_1.3.zip, r-oldrel: cutoff_1.3.zip
macOS binaries:	r-release (arm64): cutoff_1.3.tgz, r-oldrel (arm64): cutoff_1.3.tgz, r-release (x86_64): cutoff_1.3.tgz, r-oldrel (x86_64): cutoff_1.3.tgz

Reverse dependencies:

Reverse imports:

ggrisk

Linking:

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