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Type: Package
Title: Ryan-Holm Step-Down Bonferroni or Sidak Procedure
Version: 0.2.0
Description: The Ryan-Holm step-down Bonferroni or Sidak procedure is to control the family-wise (experiment-wise) type I error rate in the multiple comparisons. This procedure provides the adjusting p-values and adjusting CIs. The methods used in this package are referenced from John Ludbrook (2000) <doi:10.1046/j.1440-1681.2000.03223.x>.
License: GPL-3
Encoding: UTF-8
NeedsCompilation: no
Packaged: 2022-10-25 01:25:48 UTC; dgdzc
Author: Zhicheng Du Developer [aut, cre, cph], Hailin Feng Developer [aut]
Maintainer: Zhicheng Du Developer <dgdzc@hotmail.com>
Repository: CRAN
Date/Publication: 2022-10-25 02:37:48 UTC

Ryan-Holm Step-Down Bonferroni Procedure

Description

This procedure provides the adjusting p-values and adjusting CIs.

Usage

rh.sd.bonferroni(p,effect,effect.se,df,type,sig,side,digits)

Arguments

p

the raw p values

effect

the effect size from the multiple comparisons, e.g. the mean difference from t test or paried t test

effect.se

the standard error of effect size from the multiple comparisons, e.g. the standard error of mean difference from t test or paried t test)

df

the degree of freedom of hypothesis test, e.g. n1+n2-2 for t test, n-1 for paried t test

type

the type of the effect size, default is "mean"

sig

the significance level, default is 0.05

side

"one" or "two" sided hypothesis test

digits

the number of decimal digits

Value

p.adj

the adjusted p value

ci.adj.l

the lower limit of adjusted confidence interval

ci.adj.u

the upper limit of adjusted confidence interval

Note

Please feel free to contact us, if you have any advice and find any bug!

Reference:

1. John Ludbrook (2000). MULTIPLE INFERENCES USING CONFIDENCE INTERVALS. Clinical and Experimental Pharmacology and Physiology. 27: 212-215.

Update:

Version 0.1.0: The first version.

Version 0.2.0: Fix the bug for maintaining monotonicity of the ranking p-values.

See Also

rh.sd.sidak

Examples

p=c(0.217,0.00028,0,0.001,0.024,0.719,0.00033)
effect=c(16,74,-85,-38,29,5,91)
effect.se=c(12,16,14,9,12,16,20)
df=16
rh.sd.bonferroni(p,effect,effect.se,df)

Ryan-Holm Step-Down Sidak Procedure

Description

This procedure provides the adjusting p-values and adjusting CIs.

Usage

rh.sd.sidak(p,effect,effect.se,df,type,sig,side,digits)

Arguments

p

the raw p values

effect

the effect size from the multiple comparisons, e.g. the mean difference from t test or paried t test

effect.se

the standard error of effect size from the multiple comparisons, e.g. the standard error of mean difference from t test or paried t test)

df

the degree of freedom of hypothesis test, e.g. n1+n2-2 for t test, n-1 for paried t test

type

the type of the effect size, default is "mean"

sig

the significance level, default is 0.05

side

"one" or "two" sided hypothesis test

digits

the number of decimal digits

Value

p.adj

the adjusted p value

ci.adj.l

the lower limit of adjusted confidence interval

ci.adj.u

the upper limit of adjusted confidence interval

Note

Please feel free to contact us, if you have any advice and find any bug!

Reference:

1. John Ludbrook (2000). MULTIPLE INFERENCES USING CONFIDENCE INTERVALS. Clinical and Experimental Pharmacology and Physiology. 27: 212-215.

Update:

Version 0.1.0: The first version.

Version 0.2.0: Fix the bug for maintaining monotonicity of the ranking p-values.

See Also

rh.sd.bonferroni

Examples

p=c(0.217,0.00028,0,0.001,0.024,0.719,0.00033)
effect=c(16,74,-85,-38,29,5,91)
effect.se=c(12,16,14,9,12,16,20)
df=16
rh.sd.sidak(p,effect,effect.se,df)

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They may not be fully stable and should be used with caution. We make no claims about them.
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