FDR defines a decision's false detection (or false discovery) rate (FDR): The conditional probability of the condition being FALSE provided that the decision is positive.

FDR

## Format

An object of class numeric of length 1.

## Details

Understanding or obtaining the false detection fate or false discovery rate (FDR):

• Definition: FDR is the conditional probability for the condition being FALSE given a positive decision:

FDR = p(condition = FALSE | decision = positive)

• Perspective: FDR further classifies the subset of dec_pos individuals by condition (FDR = fa/dec_pos = fa/(hi + fa)).

• Alternative names: false discovery rate

• Relationships:

a. FDR is the complement of the positive predictive value PPV:

FDR = 1 - PPV

b. FDR is the opposite conditional probability -- but not the complement -- of the false alarm rate fart:

fart = p(decision = positive | condition = FALSE)

• In terms of frequencies, FDR is the ratio of fa divided by dec_pos (i.e., hi + fa):

FDR = fa/dec_pos = fa/(hi + fa)

• Dependencies: FDR is a feature of a decision process or diagnostic procedure and a measure of incorrect decisions (positive decisions that are actually FALSE).

However, due to being a conditional probability, the value of FDR is not intrinsic to the decision process, but also depends on the condition's prevalence value prev.

prob contains current probability information; comp_prob computes current probability information; num contains basic numeric parameters; init_num initializes basic numeric parameters; freq contains current frequency information; comp_freq computes current frequency information; is_prob verifies probabilities.
Other probabilities: FOR, NPV, PPV, acc, err, fart, mirt, ppod, prev, sens, spec
FDR <- .45     # sets a false detection rate (FDR) of 45%