Ch02-pwrFDR-grid: Evaluate 'pwrFDR' on a grid.
In pwrFDR: FDR Power

Description Usage Arguments Details Value Author(s) References See Also Examples

Function for evaluating pwrFDR on a factorial design of possible parameters.

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  pwrFDR.grid(effect.size, n.sample, r.1, alpha, delta, groups, N.tests,
              average.power, tp.power, lambda, type, grpj.per.grp1,
              FDP.control.method, control)

`effect.size`	A vector of effect sizes to be looped over. 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`	A vector of sample sizes to be looped over. The sample size is the number of experimental replicates. Required for calculation of power
`r.1`	A vector of mixing proportions to be looped over. The mixing proportion is the proportion of simultaneous tests that are non-centrally located
`alpha`	The false discovery rate (in the BH case) or the upper bound on the probability that the FDP exceeds delta (BHCLT and Romano case)
`delta`	If the "FDP.control.method" is set to 'Romano' or 'BHCLT', then this optional argument can be set to the exceedance thresh-hold in defining the FDP-tp: P\{ FDP > δ \} < α. The default value is α.
`groups`	The number of experimental groups to compare. Must be integral and >=1. The default value is 2.
`N.tests`	The number of simultaneous hypothesis tests.
`average.power`	The desired average power. Calculation of sample size, effect size mixing proportion or alpha requires specification of either 'average.power' or 'tp.power'.
`tp.power`	The desired tp-power (see `pwrFDR` documentation). Calculation of sample size, effect size mixing proportion or alpha requires specification of either 'average.power' or 'tp.power'.
`lambda`	The tp-power threshold, required when calculating the tp-power (see `pwrFDR` documentation) or when calculating the sample size, effect size mixing proportion or alpha required for tp-power.
`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.
`FDP.control.method`	A character string specifying how the false discovery proportion (FDP) is to be controlled. You may specify the whole word or any shortened uniquely identifying truncation. "BHFDR": the usual BH-FDR "BHCLT": use asymptotic approximation to the distribution of the FDP to find a smaller FDR which guarantees probability less than alpha that the FDP exceeds alpha. "Romano": use Romano's method which guarantees probability less than alpha that the FDP exceeds alpha. "Auto": in 'FixedPoint' mode, the program will use its own wisdom to determine which choice above to make. The order of conservatism is Romano > BHCLT > BHFDR, but BHFDR offers only expected control while the other two guarantee bounds on the excedance probabilty. If the distribution of the FDP is nearly degenerate, then BHFDR is the best option. Otherwise, if it can be reliably used, BHCLT would be the best choice. The 'effective' denominator, gamma*N.tests, in the CLT determines when the approximation is good enough and the asymptotic standard error of the FDP determines when the distribution is dispersed enough to matter. Use "Auto" to run through these checks and determine the best. A return argument, 'Auto', displays the choice made. See output components and details. "both": in 'simulation' mode, compute statistics R and T under BHCLT and Romano (in addition to BHFDR). Corresponding statistics are denoted R.st, T.st corresponding to BHCLT control of the FDP, and R.R and T.R corresponding to Romano control of the FDP. If sim.level is set to 2, (default) the statistics R.st.ht and T.st.ht, which are the number rejected and number true positives under BHCLT where r_0 = 1-r_1, gamma, and alpha.star have been estimated from the P-value data and then alpha.star computed from these.
`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. 'distop', specifying the distribution family of the central and non-centrally located sub-populations. distopt=1 gives normal (2 groups), distop=2 gives t- (2 groups) and distopt=3 gives F- (2+ groups) 'CS', correlation structure, for use only with 'method="simulation"' which will simulate m simulatenous tests with correlations 'rho' in blocks of size 'n.WC'. Specify as a list CS = list(rho=0.80,n.WC=50) for example. 'sim.level' sim level 2 (default) stipulates, when FDP.control.method is set to "BHCLT", 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 BHCLT 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. 'ast.le.a' leaving this at the default value TRUE forces 'alpha.star', the solution under FDP.method.control="BHCLT", to be less than the specified 'alpha'.

Arguments may be specified as vectors of possible values or can be set to a single constant value.

A list having two components:

`conditions`	A data.frame with one column for each argument listing the distinct settings for all parameters.
`results`	A list with components objects of class `pwr`, the results of the calls to `pwrFDR`

Grant Izmirlian <izmirlian at nih dot gov>

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 λ-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.

pwrFDR.grid controlFDP

1 2	tst <- pwrFDR.grid(effect.size=c(0.6,0.9), n.sample=c(50,60,70), r.1=0.4+0.2(0:1), alpha=0.05+0.05(0:3), N.tests=1000, FDP.control.method="Auto")