TSBKY_pi0_est: Two-step estimation method of Benjamini, Krieger and...
In mutoss: Unified Multiple Testing Procedures
TSBKY_pi0_est R Documentation
Two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0
Description
The two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0 is applied to pValues.
It consists of the following two steps:
Step 1. Use the linear step-up procedure at level alpha' =alpha/(1+alpha). Let r1 be the number of
rejected hypotheses. If r1=0 do not reject any hypothesis and stop; if r1=m reject all m
hypotheses and stop; otherwise continue.
Step 2. Let \hat{m0} =(m - r1) and \hat{pi0} = \hat{m0} / m.
Usage
TSBKY_pi0_est(pValues, alpha)
Arguments
pValues
The raw p-values for the marginal test problems
alpha
The parameter (to be interpreted as significance level) for the procedure
Value
pi0.TSBKY
The estimated proportion of true null hypotheses.
Author(s)
WerftWiebke
References
Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate
Biometrika 93, 3, page 495.
Examples
my.pvals <- c(runif(50), runif(50, 0, 0.01))
result <- TSBKY_pi0_est(my.pvals, 0.1)
mutoss documentation built on March 31, 2023, 8:46 p.m.
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| TSBKY_pi0_est | R Documentation |
Two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0
Description
The two-step estimation method of Benjamini, Krieger and Yekutieli for estimating pi0 is applied to pValues.
It consists of the following two steps:
Step 1. Use the linear step-up procedure at level alpha' =alpha/(1+alpha). Let r1 be the number of
rejected hypotheses. If r1=0 do not reject any hypothesis and stop; if r1=m reject all m
hypotheses and stop; otherwise continue.
Step 2. Let \hat{m0} =(m - r1) and \hat{pi0} = \hat{m0} / m.
Usage
TSBKY_pi0_est(pValues, alpha)Arguments
pValues |
The raw p-values for the marginal test problems |
alpha |
The parameter (to be interpreted as significance level) for the procedure |
Value
pi0.TSBKY |
The estimated proportion of true null hypotheses. |
Author(s)
WerftWiebke
References
Benjamini, Y., Krieger, A. and Yekutieli, D. (2006). Adaptive linear step-up procedures that control the false discovery rate Biometrika 93, 3, page 495.
Examples
my.pvals <- c(runif(50), runif(50, 0, 0.01))
result <- TSBKY_pi0_est(my.pvals, 0.1)R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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