URI: http://www.qualityml.org/1.0/metrics/FalseDiscoveryRate
Name: False Discovery Rate
Alternative names: FDR
Definition:

False discovery rate (FDR) control is a statistical method used in multiple hypothesis testing to correct for multiple comparisons. In a list of findings (i.e. studies where the null-hypotheses are rejected), FDR procedures are designed to control the expected proportion of incorrectly rejected null hypotheses ("false discoveries"). FDR controlling procedures exert a less stringent control over false discovery compared to familywise error rate (FWER) procedures (such as the Bonferroni correction), which seek to reduce the probability of even one false discovery, as opposed to the expected proportion of false discoveries. Thus FDR procedures have greater power at the cost of increased rates of type I errors, i.e., rejecting the null hypothesis of no effect when it should be accepted.

FDR = FP/(FP+TP)

where FP = false positive, TP = true positive

Parameters: actual categories
Source: GeoViQua
Categories: Thematic accuracy
Further information: http://en.wikipedia.org/wiki/Sensitivity_and_specificity
XML schema:
<schema xmlns:qml="http://www.qualityml.org/1.0" 
xmlns:un="http://www.uncertml.org/2.0" 
xmlns="http://www.w3.org/2001/XMLSchema" targetNamespace="http://www.qualityml.org/1.0" 
elementFormDefault="qualified" attributeFormDefault="unqualified">
<import namespace="http://www.uncertml.org/2.0" schemaLocation="../../uncertml/2.0/uncertml.xsd"/>


	<element name="FalseDiscoveryRate" type="qml:ContinuousValuesSummaryStatisticType" substitutionGroup="un:AbstractSummaryStatistic"/>
XML example:
JSON example:
N/A
API example:
N/A
Value constraints: categories: any string
counts: any positive natural number