# acc: Accuracy (acc) is the probability of a correct decision. In riskyr: Rendering Risk Literacy more Transparent

## Description

`acc` defines overall accuracy as the probability of correspondence between a positive decision and true condition (i.e., the proportion of correct classification decisions or of `dec_cor` cases).

## Usage

 `1` ```acc ```

## Format

An object of class `numeric` of length 1.

## Details

Importantly, correct decisions `dec_cor` are not necessarily positive decisions `dec_pos`.

Understanding or obtaining the accuracy metric `acc`:

• Definition: `acc` is the (non-conditional) probability:

`acc = p(dec_cor) = dec_cor/N`

or the base rate (or baseline probability) of a decision being correct, but not necessarily positive.

`acc` values range from 0 (no correct decision/prediction) to 1 (perfect decision/prediction).

• Computation: `acc` can be computed in several ways:

(a) from `prob`: `acc = (prev x sens) + [(1 - prev) x spec]`

(b) from `freq`: `acc = dec_cor/N = (hi + cr)/(hi + mi + fa + cr)`

(c) as complement of the error rate `err`: `acc = 1 - err`

When frequencies in `freq` are not rounded, (b) coincides with (a) and (c).

• Perspective: `acc` classifies a population of `N` individuals by accuracy/correspondence (`acc = dec_cor/N`).

`acc` is the "by accuracy" or "by correspondence" counterpart to `prev` (which adopts a "by condition" perspective) and to `ppod` (which adopts a "by decision" perspective).

• Alternative names: base rate of correct decisions, non-erroneous cases

• In terms of frequencies, `acc` is the ratio of `dec_cor` (i.e., `hi + cr`) divided by `N` (i.e., `hi + mi` + `fa + cr`):

`acc = dec_cor/N = (hi + cr)/(hi + mi + fa + cr)`

• Dependencies: `acc` is a feature of both the environment (true condition) and of the decision process or diagnostic procedure. It reflects the correspondence of decisions to conditions.

See `accu` for other accuracy metrics and several possible interpretations of accuracy.

## References

`comp_acc` computes accuracy from probabilities; `accu` lists all accuracy metrics; `comp_accu_prob` computes exact accuracy metrics from probabilities; `comp_accu_freq` computes accuracy metrics from frequencies; `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 probabilities: `FDR`, `FOR`, `NPV`, `PPV`, `err`, `fart`, `mirt`, `ppod`, `prev`, `sens`, `spec`

Other metrics: `accu`, `comp_accu_freq`, `comp_accu_prob`, `comp_acc`, `comp_err`, `err`

## Examples

 ```1 2 3``` ```acc <- .50 # sets a rate of correct decisions of 50% acc <- 50/100 # (dec_cor) for 50 out of 100 individuals is_prob(acc) # TRUE ```

riskyr documentation built on Jan. 3, 2019, 1:06 a.m.