`prob`

is a list of named numeric variables
containing 3 essential (1 non-conditional `prev`

and
2 conditional `sens`

and `spec`

) probabilities
and 8 derived (`ppod`

and `acc`

,
as well as 6 conditional) probabilities:

`prob`

An object of class `list`

of length 13.

`prob`

currently contains the following probabilities:

the condition's prevalence

`prev`

(i.e., the probability of the condition being`TRUE`

):`prev = cond_true/N`

.the decision's sensitivity

`sens`

(i.e., the conditional probability of a positive decision provided that the condition is`TRUE`

).the decision's miss rate

`mirt`

(i.e., the conditional probability of a negative decision provided that the condition is`TRUE`

).the decision's specificity

`spec`

(i.e., the conditional probability of a negative decision provided that the condition is`FALSE`

).the decision's false alarm rate

`fart`

(i.e., the conditional probability of a positive decision provided that the condition is`FALSE`

).the proportion (baseline probability or rate) of the decision being positive

`ppod`

(but not necessarily true):`ppod = dec_pos/N`

.the decision's positive predictive value

`PPV`

(i.e., the conditional probability of the condition being`TRUE`

provided that the decision is positive).the decision's false detection (or false discovery) rate

`FDR`

(i.e., the conditional probability of the condition being`FALSE`

provided that the decision is positive).the decision's negative predictive value

`NPV`

(i.e., the conditional probability of the condition being`FALSE`

provided that the decision is negative).the decision's false omission rate

`FOR`

(i.e., the conditional probability of the condition being`TRUE`

provided that the decision is negative).the accuracy

`acc`

(i.e., probability of correct decisions`dec_cor`

or correspondence of decisions to conditions).the conditional probability

`p_acc_hi`

(i.e., the probability of`hi`

given that the decision is correct`dec_cor`

).the conditional probability

`p_err_fa`

(i.e., the probability of`fa`

given that the decision is erroneous`dec_err`

).

These probabilities are computed from basic probabilities
(contained in `num`

) and computed by using
`comp_prob`

.

The list `prob`

is the probability counterpart
to the list containing frequency information `freq`

.

Note that inputs of extreme probabilities (of 0 or 1)
may yield unexpected values (e.g., an `NPV`

value of NaN when `is_extreme_prob_set`

evaluates to `TRUE`

).

Key relationships between frequencies and probabilities
(see documentation of `comp_freq`

or `comp_prob`

for details):

Three perspectives on a population:

by condition / by decision / by accuracy.

Defining probabilities in terms of frequencies:

Probabilities can be computed as ratios between frequencies, but beware of rounding issues.

Functions translating between representational formats:
`comp_prob_prob`

, `comp_prob_freq`

,
`comp_freq_prob`

, `comp_freq_freq`

(see documentation of `comp_prob_prob`

for details).

Visualizations of current probability information
are provided by `plot_area`

,
`plot_prism`

, and `plot_curve`

.

`num`

contains basic numeric parameters;
`init_num`

initializes basic numeric parameters;
`txt`

contains current text information;
`init_txt`

initializes text information;
`pal`

contains current color information;
`init_pal`

initializes color information;
`freq`

contains current frequency information;
`comp_freq`

computes current frequency information;
`prob`

contains current probability information;
`comp_prob`

computes current probability information;
`accu`

contains current accuracy information.

Other lists containing current scenario information: `accu`

,
`freq`

, `num`

,
`pal_bwp`

, `pal_bw`

,
`pal_kn`

, `pal_mbw`

,
`pal_mod`

, `pal_org`

,
`pal_rgb`

, `pal_vir`

,
`pal`

, `txt_TF`

,
`txt_org`

, `txt`

#> $prev #> [1] 0.25 #> #> $sens #> [1] 0.85 #> #> $mirt #> [1] 0.15 #> #> $spec #> [1] 0.75 #> #> $fart #> [1] 0.25 #> #> $ppod #> [1] 0.4 #> #> $PPV #> [1] 0.53125 #> #> $FDR #> [1] 0.46875 #> #> $NPV #> [1] 0.9375 #> #> $FOR #> [1] 0.0625 #> #> $acc #> [1] 0.775 #> #> $p_acc_hi #> [1] 0.2741935 #> #> $p_err_fa #> [1] 0.8333333 #>#> [1] 13