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

A function which computes the asymptotic variance of the false discovery fraction, T_N/J_N.

1 | ```
var.rtm.ToJ(x, groups, effect.size, n.sample, r.1, FDR, N.tests, control)
``` |

`x` |
Calls to this function can be made either specifying the single
argument, x, which is an object of class "pwr" returned from the
function, |

`groups` |
The number of experimental groups to compare. Default value is 2. |

`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 |

`FDR` |
The false discovery rate. |

`N.tests` |
Number of simultaneous tests. |

`control` |
Optionally, a list with components with the following components: 'groups', used when distop=3 (F-dist), specifying number of groups. 'version', used only in the 'JL' method, choice 0 gives the 'JL' version as published, whereas choice 1 replaces the FDR with r.0*FDR resulting in the infinite simultaneous tests limiting average power, which is the 'Iz' version, but this is redundant because you can specify the 'Iz' method to use this option. '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 1000 by default 'distop', specifying the distribution family of the central and non-centrally located sub-populations. =1 gives normal (2 groups) =2 gives t- (2 groups) and =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 list CS = list(rho=0.80, n.WC=50) for example |

The false discovery fraction (FDF), T_m/J_m, i.e., is the proportion of all centrally located statistical tests that are declared significant by the Benjamini-Hochberg procedure. It is shown, in the cited publication, to be root-m consistent and asymptotically normal i.e.

m^1/2 ( T_m/J_m - (1-r.1) f ) –D–> N(0, sigma^2)

which is to say that the distribution of T_m/J_m is spiked about its mean, the false discovery rate, and the width dies off as 1/m^1/2. This noraml approximation is of interest in its own right, allowing tighter control over the false discovery fraction that afforded by controlling its mean, the false discovery rate. One can determine the FDR required to bound the FDF by a given value with a probability no less than a given value.

Returns a value of class `vvv`

, containing components

`var.J.o.rtm` |
The computed asymptotic variance |

`power` |
The average power |

`gamma` |
The expected proportion of significant calls |

`c.g` |
The 'q-value', which is the value of the criterion on the scale of the statistic (t of given number of 2 n - 2 degrees of freedom) which can be used as a per test criterion resulting in the equivalent Benjamini-Hochberg procedure |

`call` |
The call which produced the result |

Grant Izmirlian <izmirlig at mail dot nih dot gov>

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

1 2 3 4 5 6 | ```
## call using result of pwrFDR
rslt.Iz <- pwrFDR(effect.size=0.79, n.sample=46, r.1=2000/54675, FDR=0.15)
vJ <- var.J.o.rtm(rslt.Iz)
## call via argument list specification
vJ <- var.J.o.rtm(effect.size=0.79, n.sample=46, r.1=2000/54675, FDR=0.15)
``` |

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