Package 'ManyTests'

Multiple Testing Procedures of Cox (2011) and Wong and Cox (2007)

Description

Performs the multiple testing procedures of Cox (2011) and Wong and Cox (2007).

Details

Package:	ManyTests
Type:	Package
Version:	1.1
Date:	2016-10-30
License:	GPL-2

Author(s)

Christiana Kartsonaki

Maintainer: Christiana Kartsonaki <[email protected]>

References

Cox, D. R. (2011). Discovery: a statistical perspective. Phystat Conference CERN. <doi:10.5170/CERN-2011-006>

Cox, D. R. and Wong, M. Y. (2004). A simple procedure for the selection of significant effects. Journal of the Royal Statistical Society B 66 (2), 395–400. <doi:10.1111/j.1369-7412.2004.05695.x>

Wong, M. Y. and Cox, D. R. (2007). On the screening of large numbers of significance tests. Journal of Applied Statistics 34 (7), 779–783. <doi:10.1080/02664760701240014>

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False Discovery Rate corresponding to t_0

Description

Calculates the FDR which corresponds to a given cut-off t_0 according to the procedure of Wong and Cox (2007).

Usage

FDR(test_statistics, t_0)

Arguments

test_statistics	A vector of values of test statistics.
t_0	A cut-off value.

Value

The FDR which corresponds to a given cut-off t_0.

Author(s)

Christiana Kartsonaki

References

Cox, D. R. and Wong, M. Y. (2004). A simple procedure for the selection of significant effects. Journal of the Royal Statistical Society B 66 (2), 395–400. <doi:10.1111/j.1369-7412.2004.05695.x>

Wong, M. Y. and Cox, D. R. (2007). On the screening of large numbers of significance tests. Journal of Applied Statistics 34 (7), 779–783. <doi:10.1080/02664760701240014>

See Also

t_0

Examples

x <- c(rnorm(100, 2, 2), rnorm(50, 0, 2))
FDR(x, t_0(x))

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Calculate and test the local slope of the plot at large values

Description

Calculates the effective slope of the plot at large values and tests the deviation of the largest value from that line (Cox, 2011).

Usage

local_slope(p, k)

Arguments

p	Vector of p-values.
k	Number of 'top' k values.

Value

local_slope	The estimated local slope of the plot at large values.
test_statistic	The value of the test statistic.
Fvalue	The upper 5% value of the F distribution with 2 and 2k degress of freedom, which is the distribution of the test statistic under the null hypothesis.
pvalue	The p-value of the test.

Author(s)

Christiana Kartsonaki

References

Cox, D. R. (2011). Discovery: a statistical perspective. Phystat Conference CERN. <doi:10.5170/CERN-2011-006>

See Also

plot_pvalues

Examples

# generate a vector of p values
p <- runif(100, 0, 1)

local_slope(p, 10)

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Calculate the expected values of the Renyi decomposition

Description

Calculates the expected values of the Renyi decomposition.

Usage

ordered_values(n)

Arguments

n	Number of values.

Value

A vector of length n.

Author(s)

Christiana Kartsonaki

References

Cox, D. R. (2011). Discovery: a statistical perspective. Phystat Conference CERN. <doi:10.5170/CERN-2011-006>

Examples

ordered_values(10)

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Plot transformed p-values against the expected values of the Renyi decomposition

Description

Plots -log(p) against the expected values of the Renyi decomposition (Cox, 2011).

Usage

plot_pvalues(p)

Arguments

p	A vector of p-values.

Author(s)

Christiana Kartsonaki

References

Cox, D. R. (2011). Discovery: a statistical perspective. Phystat Conference CERN. <doi:10.5170/CERN-2011-006>

See Also

local_slope

Examples

# generate a vector of p-values
p <- runif(100, 0, 1)

plot_pvalues(p)

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Cut-off level corresponding to unit Bayes factor

Description

Calculates the cut-off level corresponding to unit Bayes factor according to the procedure of Wong and Cox (2007).

Usage

t_0(test_statistics)

Arguments

test_statistics

A vector of values of test statistics.

Value

Cut-off level corresponding to unit Bayes factor.

Author(s)

Christiana Kartsonaki

References

Cox, D. R. and Wong, M. Y. (2004). A simple procedure for the selection of significant effects. Journal of the Royal Statistical Society B 66 (2), 395–400. <doi:10.1111/j.1369-7412.2004.05695.x>

Wong, M. Y. and Cox, D. R. (2007). On the screening of large numbers of significance tests. Journal of Applied Statistics 34 (7), 779–783. <doi:10.1080/02664760701240014>

See Also

FDR

Examples

x <- c(rnorm(100, 2, 2), rnorm(50, 0, 2))
t_0(x)

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