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

## 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
cond_false <- 1000 * .90  # => sets cond_false to 90% of 1000 = 900 cases.
is_freq(cond_false)       # => TRUE#> [1] TRUEis_prob(cond_false)       # => FALSE, as cond_false is no probability [but (1 - prev) and spec are]#> [1] FALSE