comp_FDR: Compute a decision's false detection rate (FDR) from...

In hneth/riskyr: Rendering Risk Literacy more Transparent

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

Description

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.

See Also

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_acc(), comp_accu_freq(), comp_accu_prob(), comp_comp_pair(), comp_complement(), comp_complete_prob_set(), comp_err(), comp_fart(), comp_mirt(), comp_ppod(), comp_prob(), comp_prob_freq(), comp_sens(), comp_spec()

Examples

# (1) Ways to work:
comp_FDR(.50, .500, .500)  # => FDR = 0.5    = (1 - PPV)
comp_FDR(.50, .333, .666)  # => FDR = 0.5007 = (1 - PPV)

hneth/riskyr documentation built on Feb. 18, 2024, 6:01 p.m.