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cutoff (version 1.3)

Seek the Significant Cutoff Value

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.

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Install

install.packages('cutoff')

Monthly Downloads

555

Version

1.3

License

GPL-3

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Maintainer

Jing Zhang

Last Published

December 20th, 2019

Functions in cutoff (1.3)

logit

Significant Cutoff Value for Logistic Regression
judge_123

Whether the Data Is Arranged from Small to Large
linear

Significant Cutoff Value for Linear Regression
cutit

Cut Continuous Vector to Classification
x_ab

Return x Between a and b
cox

Significant Cutoff Value for Cox Regression
roc

To Get the Best Cutoff Value for ROC Curve
logrank

Significant Cutoff Value for Logrank Analysis
judge_321

Whether the Data Is Arranged from Large to Small