`comp_min_N`

computes a population size value `N`

(an integer
as a power of 10) so that the frequencies of the 4 combinations of conditions and decisions
(i.e., the cells of the confusion table, or center row of boxes in the frequency prism)
reach or exceed a minimum value `min_freq`

given the basic parameters
`prev`

, `sens`

, and `spec`

(`spec = 1 - fart`

).

comp_min_N(prev, sens, spec, min_freq = 1)

prev | The condition's prevalence value |
---|---|

sens | The decision's sensitivity value |

spec | The specificity value |

min_freq | The minimum frequency of each combination of
a condition and a decision (i.e., hits, misses, false alarms, and correct rejections).
Default: |

An integer value `N`

(as a power of 10).

Using this function helps avoiding excessively small decimal values in categories
-- especially `hi`

, `mi`

, `fa`

, `cr`

--
when expressing combinations of conditions and decisions as natural frequencies.
As values of zero (0) are tolerable, the function only increases `N`

(in powers of 10) while the current value of any frequency (cell in confusion table or
leaf of a frequency tree) is positive but below `min_freq`

.

By default, `comp_freq_prob`

and `comp_freq`

round frequencies to nearest integers to avoid decimal values in
`freq`

(i.e., `round = TRUE`

by default).
Using the option `round = FALSE`

turns off rounding.

population size `N`

;
`num`

contains basic numeric parameters;
`freq`

contains current frequency information;
`comp_freq`

computes frequencies from probabilities;
`prob`

contains current probability information;
`comp_prob`

computes probabilities from probabilities;
`comp_freq_freq`

computes current frequency information from (4 essential) frequencies;
`comp_freq_prob`

computes current frequency information from (3 essential) probabilities;
`comp_prob_freq`

computes current probability information from (4 essential) frequencies;
`comp_prob_prob`

computes current probability information from (3 essential) probabilities.

Other functions computing frequencies: `comp_freq_freq`

,
`comp_freq_prob`

, `comp_freq`

,
`comp_popu`

, `comp_prob_prob`

comp_min_N(0, 0, 0) # => 1#> Warning: Extreme case (prev = 0 & spec = 0): #> N fa (FP) cases; 0 cond_true or dec_true cases; PPV = NaN.#> [1] 1comp_min_N(1, 1, 1) # => 1#> Warning: Extreme case (prev = 1 & sens = 1): #> N hi (TP) cases; 0 cond_false or dec_false cases; NPV = NaN.#> [1] 1comp_min_N(1, 1, 1, min_freq = 10) # => 10#> Warning: Extreme case (prev = 1 & sens = 1): #> N hi (TP) cases; 0 cond_false or dec_false cases; NPV = NaN.#> [1] 10comp_min_N(1, 1, 1, min_freq = 99) # => 100#> Warning: Extreme case (prev = 1 & sens = 1): #> N hi (TP) cases; 0 cond_false or dec_false cases; NPV = NaN.#> [1] 100comp_min_N(.1, .1, .1) # => 100 = 10^2#> [1] 100comp_min_N(.001, .1, .1) # => 10 000 = 10^4#> [1] 10000comp_min_N(.001, .001, .1) # => 1 000 000 = 10^6#> [1] 1e+06comp_min_N(.001, .001, .001) # => 1 000 000 = 10^6#> [1] 1e+06