# cond_false: Number of individuals for which the condition is false. In riskyr: Rendering Risk Literacy more Transparent

## Description

`cond_false` is a frequency that describes the number of individuals in the current population `N` for which the condition is `FALSE` (i.e., actually false cases).

## Usage

 `1` ```cond_false ```

## Format

An object of class `numeric` of length 1.

## Details

Key relationships:

1. to probabilities: The frequency of `cond_false` individuals depends on the population size `N` and the complement of the condition's prevalence `1 - prev` and is split further into two subsets of `fa` by the false alarm rate `fart` and `cr` by the specificity `spec`.

Perspectives:

1. by condition:

The frequency `cond_false` is determined by the population size `N` times the complement of the prevalence `(1 - prev)`:

`cond_false= N x (1 - prev)`

2. by decision:

a. The frequency `fa` is determined by `cond_false` times the false alarm rate `fart = (1 - spec)` (aka. `FPR`):

`fa = cond_false x fart = cond_false x (1 - spec) `

b. The frequency `cr` is determined by `cond_false` times the specificity `spec = (1 - fart)`:

`cr = cond_false x spec = cond_false x (1 - fart) `

2. to other frequencies: In a population of size `N` the following relationships hold:

• `N = cond_true + cond_false` (by condition)

• `N = dec_pos + dec_neg` (by decision)

• `N = dec_cor + dec_err` (by correspondence of decision to condition)

• `N = hi + mi + fa + cr` (by condition x decision)

Current frequency information is computed by `comp_freq` and contained in a list `freq`.

## References

Consult Wikipedia: Confusion matrix for additional information.

`is_freq` verifies frequencies; `num` contains basic numeric parameters; `init_num` initializes basic numeric parameters; `freq` contains current frequency information; `comp_freq` computes current frequency information; `prob` contains current probability information; `comp_prob` computes current probability information.
Other frequencies: `N`, `cond_true`, `cr`, `dec_cor`, `dec_err`, `dec_neg`, `dec_pos`, `fa`, `hi`, `mi`
 ```1 2 3``` ```cond_false <- 1000 * .90 # => sets cond_false to 90% of 1000 = 900 cases. is_freq(cond_false) # => TRUE is_prob(cond_false) # => FALSE, as cond_false is no probability [but (1 - prev) and spec are] ```