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.



An object of class numeric of length 1.


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.


Consult Wikipedia for additional information.

See also

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% FDR <- 45/100 # (condition = FALSE) for 45 out of 100 people with (decision = positive) is_prob(FDR) # TRUE
#> [1] TRUE