# comp_FDR

0th

Percentile

##### Compute a decision's false detection rate (FDR) from probabilities.

comp_FDR computes the false detection rate FDR from 3 essential probabilities prev, sens, and spec.

##### Usage
comp_FDR(prev, sens, spec)
##### Arguments
prev

The condition's prevalence prev (i.e., the probability of condition being TRUE).

sens

The decision's sensitivity sens (i.e., the conditional probability of a positive decision provided that the condition is TRUE).

spec

The decision's specificity value spec (i.e., the conditional probability of a negative decision provided that the condition is FALSE).

##### Details

comp_FDR uses probabilities (not frequencies) and does not round results.

##### Value

The false detection rate FDR as a probability. A warning is provided for NaN values.

comp_sens and comp_PPV compute related probabilities; is_extreme_prob_set verifies extreme cases; comp_complement computes a probability's complement; is_complement verifies probability complements; comp_prob computes current probability information; prob contains current probability information; is_prob verifies probabilities.

Other functions computing probabilities: comp_FOR, comp_NPV, comp_PPV, comp_accu_freq, comp_accu_prob, comp_acc, comp_comp_pair, comp_complement, comp_complete_prob_set, comp_err, comp_fart, comp_mirt, comp_ppod, comp_prob_freq, comp_prob, comp_sens, comp_spec

• comp_FDR
##### Examples
# NOT RUN {
# (1) Ways to work:
comp_FDR(.50, .500, .500)  # => FDR = 0.5    = (1 - PPV)
comp_FDR(.50, .333, .666)  # => FDR = 0.5007 = (1 - PPV)

# }

Documentation reproduced from package riskyr, version 0.2.0, License: GPL-2 | GPL-3

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