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_unikn
,
pal_vir
,
pal
,
txt_TF
,
txt_org
,
txt
prob <- comp_prob() # initialize prob to default parameters
prob # show current values
#> $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
#>
length(prob) # 13 key probabilities (and their values)
#> [1] 13