pwrFDR (version 3.2.4)

CDF.Pval.apsi.eq.u: Calculates the fixed point for the Romano procedure.

Description

Calculates the fixed point for the Romano procedure, e.g. finds u which solves u = G( psi(u, d) a) where G is the common p-value CDF, and psi(u, d) = u d/(1 - (1-a) u). Essentially an internal function and included at the user level for pedagogic purposes.

Usage

CDF.Pval.apsi.eq.u(effect.size, n.sample, r.1, alpha, delta, groups, type,
                     grpj.per.grp1, distopt, control)

Value

An object of class cdf which contains components

call

The call which produced the result

gamma

The fixed point for the Romano method.

Arguments

effect.size

The effect size (mean over standard deviation) for test statistics having non-zero means. Assumed to be a constant (in magnitude) over non-zero mean test statistics.

n.sample

The number of experimental replicates. Required for calculation of power

r.1

The proportion of simultaneous tests that are non-centrally located

alpha

The upper bound on the probability that the FDP exceeds delta.

delta

The exceedance thresh-hold for the FDP tail probability control method (BHFDX or Romano) \(P\{ FDP > \delta \} < \alpha\). The default value is \(\alpha\).

groups

The number of experimental groups to compare. Must be integral and >=1. The default value is 2.

type

A character string specifying, in the groups=2 case, whether the test is 'paired', 'balanced', or 'unbalanced' and in the case when groups >=3, whether the test is 'balanced' or 'unbalanced'. The default in all cases is 'balanced'. Left unspecified in the one sample (groups=1) case.

grpj.per.grp1

Required when type="unbalanced", specifies the group 0 to group 1 ratio in the two group case, and in the case of 3 or more groups, the group j to group 1 ratio, where group 1 is the group with the largest effect under the alternative hypothesis.

distopt

Test statistic distribution in among null and alternatively distributed sub-populations. distopt=0 gives normal (2 groups), distop=1 gives t- (2 groups) and distopt=2 gives F- (2+ groups)

control

Optionally, a list with components with the following components:
'tol' is a convergence criterion used in iterative methods which is set to 1e-8 by default. 'max.iter' is an iteration limit, set to 20 for the iterated function limit and 1000 for all others by default. 'sim.level' sim level 2 (default) stipulates, when FDP.control.method is set to "BHFDX", or "both", R.st.ht and T.st.ht are computed in addition to R.st and T.st (see above).
'low.power.stop' in simulation option, will result in an error message if the power computed via FixedPoint method is too low, which result in no solution for the BHFDX option. Default setting is TRUE. Set to FALSE to over-ride this behavior.
'FDP.meth.thresh' fine-tunes the 'Auto' voodoo (see above). Leave this alone.
'verb' vebosity level.

Author

Grant Izmirlian <izmirlian at nih dot gov>

References

Izmirlian G. (2020) Strong consistency and asymptotic normality for quantities related to the Benjamini-Hochberg false discovery rate procedure. Statistics and Probability Letters; 108713, <doi:10.1016/j.spl.2020.108713>.

Izmirlian G. (2017) Average Power and \(\lambda\)-power in Multiple Testing Scenarios when the Benjamini-Hochberg False Discovery Rate Procedure is Used. <arXiv:1801.03989>

Jung S-H. (2005) Sample size for FDR-control in microarray data analysis. Bioinformatics; 21:3097-3104.

Liu P. and Hwang J-T. G. (2007) Quick calculation for sample size while controlling false discovery rate with application to microarray analysis. Bioinformatics; 23:739-746.

Lehmann E. L., Romano J. P.. Generalizations of the familywise error rate. Ann. Stat.. 2005;33(3):1138-1154.

Romano Joseph P., Shaikh Azeem M.. Stepup procedures for control of generalizations of the familywise error rate. Ann. Stat.. 2006;34(4):1850-1873.

See Also

CDF.Pval.au.eq.u

Examples

  ## An example showing that the Romano method is more conservative than the BHFDX method
  ## which is in turn more conservative than the BH-FDR method based upon ordering of the
  ## significant call proportions, R_m/m

  ## First find alpha.star for the BH-CLT method at level alpha=0.15
  a.st.BHFDX <-controlFDP(effect.size=0.8,r.1=0.05,N.tests=1000,n.sample=70,alpha=0.15)$alpha.star

  ## now find the significant call fraction under the BH-FDR method at level alpha=0.15
  gamma.BHFDR <- CDF.Pval.au.eq.u(effect.size = 0.8, n.sample = 70, r.1 = 0.05, alpha=0.15)

  ## now find the significant call fraction under the Romano method at level alpha=0.15
  gamma.romano <- CDF.Pval.apsi.eq.u(effect.size = 0.8, n.sample = 70, r.1 = 0.05, alpha=0.15)

  ## now find the significant call fraction under the BH-CLT method at level alpha=0.15
  gamma.BHFDX <- CDF.Pval.au.eq.u(effect.size = 0.8, n.sample = 70, r.1 = 0.05, alpha=a.st.BHFDX)