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cutoff
View on CRAN: Click
here
Download and install cutoff package within the R console
Install from CRAN:
install.packages("cutoff")
Install from Github:
library("remotes")
install_github("cran/cutoff")
Install by package version:
library("remotes")
install_version("cutoff", "1.3")
Attach the package and use:
library("cutoff")
Maintained by
Jing Zhang
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-12-20
Latest Update: 2019-12-20
Description:
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) , to
adjust the p value, Missing values will be deleted by na.omit() function
before analysis.
How to cite:
Jing Zhang (2019). cutoff: Seek the Significant Cutoff Value. R package version 1.3, https://cran.r-project.org/web/packages/cutoff. Accessed 22 Dec. 2024.
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Complete documentation for cutoff
Functions, R codes and Examples using
the cutoff R package
Some associated functions: cox . cutit . judge_123 . judge_321 . linear . logit . logrank . roc . x_ab .
Some associated R codes: P_douglas1994.R . cox.R . linear.R . logit.R . logrank.R . roc.R . zp_cut_it.R . zp_delet_na_df.mtr.R . zp_get_cutoff.R . zp_help.R . zp_judge.R . zp_x_ab.R . Full cutoff package functions and examples
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