`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).

`cond_false`

An object of class `numeric`

of length 1.

Key relationships:

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:

by condition:

The frequency

`cond_false`

is determined by the population size`N`

times the complement of the prevalence`(1 - prev)`

: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)`

:

to other frequencies: In a population of size

`N`

the following relationships hold:

Current frequency information is computed by
`comp_freq`

and contained in a list
`freq`

.

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`

cond_false <- 1000 * .90 # => sets cond_false to 90% of 1000 = 900 cases. is_freq(cond_false) # => TRUE#> [1] TRUE#> [1] FALSE