False Discovery Rate Q Calculator
Benjamini–Hochbergadjusted family pvaluesmonotone reversecummin(p*m/rank), original order retained. Not Storeyqestimatedπ0; independence/PRDS conditions or other corrections external.
Description
Benjamini-Hochberg adjusted p-values summarize a supplied complete test family in its original order.
Benjamini-Hochberg adjusted p-values summarize a supplied complete test family in its original order.1
This tool applies the ranked reverse-minimum calculation; its result is not a Storey estimate of the null proportion.
When to use False Discovery Rate Q Calculator
This tool applies the ranked reverse-minimum calculation; its result is not a Storey estimate of the null proportion.
How to use False Discovery Rate Q Calculator and how it works
- Prepare family pvalues, using the input definitions and matching units below.
- Enter the required fields and run False Discovery Rate Q Calculator.
- Read result.bhAdjustedPValues using the units, signs and ordering explained below.
- Correct any rejected inputs and rerun; interpret the result within the stated model and its assumptions.
| Input field | Meaning | Value type | Requirement |
|---|---|---|---|
| actualFamilyPValues | Actual Family P Values. Use the units and matching context stated in the calculation below. | array of number | Required; other constraints below |
Require a nonempty complete family of p-values, each from 0 to 1. Sort indices by increasing p-value. Traverse ranks backward, taking the minimum of the current value and p*m/rank, starting at one. Restore adjusted values to original input order. Return the BH-adjusted p-value array; no null-proportion estimation or dependence assessment occurs.
adjustedSorted_p_i=min(1,min_over_j>=i(m*p_j/j)); restoreOriginalOrder Apply the input ordering, units and branch conditions stated above.
| Output field | Meaning | Value type |
|---|---|---|
| result | Object containing the calculated or selected fields below. | object |
| result.bhAdjustedPValues | Benjamini-Hochberg adjusted p-values restored to original family order. | array of number |
{
"input": {
"actualFamilyPValues": [
0.01,
0.04,
0.2
]
},
"output": {
"result": {
"bhAdjustedPValues": [
0.03,
0.06,
0.20000000000000004
]
}
}
}Limitations and assumptions
The calculation uses the supplied observations and model inputs. It does not collect measurements, select a reference population or coefficient, or establish that the model applies to the actual case. The stated branch conditions and output units remain part of the result.
All fields require explicit values. Unexpected fields, wrong types, nonfinite numbers and sparse arrays are rejected. Alignment, bounds and supported-token rules are explained above; extremely large inputs or nonfinite results can also fail. Input errors differ from successful false comparisons, unavailable-domain flags and empty match lists.
Alternative or Complementary methods
Use Signal Detection Criterion Calculator for a complementary operation: In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.
Use Signal Detection Beta Calculator for a complementary operation: The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.
References
-
False Discovery Rate Q Calculator — tool.io
Similar or alternative tools
- Signal Detection Criterion Calculator
In equal-variance Gaussian signal detection, criterion c describes response tendency using hit and false-alarm fractions.
- Signal Detection Beta Calculator
The Gaussian signal-detection likelihood criterion beta is distinct from sensitivity and from criterion c.