R/comp_popu.R
comp_popu.Rdcomp_popu computes a table popu (as an R data frame)
from the current frequency information (contained in freq).
comp_popu(
hi = freq$hi,
mi = freq$mi,
fa = freq$fa,
cr = freq$cr,
cond_lbl = txt$cond_lbl,
cond_true_lbl = txt$cond_true_lbl,
cond_false_lbl = txt$cond_false_lbl,
dec_lbl = txt$dec_lbl,
dec_pos_lbl = txt$dec_pos_lbl,
dec_neg_lbl = txt$dec_neg_lbl,
sdt_lbl = txt$sdt_lbl,
hi_lbl = txt$hi_lbl,
mi_lbl = txt$mi_lbl,
fa_lbl = txt$fa_lbl,
cr_lbl = txt$cr_lbl
)An object of class data.frame
with N rows and 3 columns
(e.g., "X/truth/cd", "Y/test/dc", "SDT/cell/class").
The number of hits hi (or true positives).
The number of misses mi (or false negatives).
The number of false alarms fa (or false positives).
The number of correct rejections cr (or true negatives).
Text label for condition dimension ("by cd" perspective).
Text label for cond_true cases.
Text label for cond_false cases.
Text label for decision dimension ("by dc" perspective).
Text label for dec_pos cases.
Text label for dec_neg cases.
Text label for 4 cases/combinations (SDT classifications).
Text label for hi cases.
Text label for mi cases.
Text label for fa cases.
Text label for cr cases.
A data frame popu
containing N rows (individual cases)
and 3 columns (e.g., "X/truth/cd", "Y/test/dc", "SDT/cell/class").
encoded as ordered factors (with 2, 2, and 4 levels, respectively).
By default, comp_popu uses the text settings
contained in txt.
A visualization of the current population
popu is provided by plot_icons.
read_popu creates a scenario (description) from data (as df);
write_popu creates data (as df) from a riskyr scenario (description);
popu for data format;
num for basic numeric parameters;
freq for current frequency information;
txt for current text settings;
pal for current color settings.
Other functions converting data/descriptions:
read_popu(),
write_popu()
popu <- comp_popu() # => initializes popu (with current values of freq and txt)
dim(popu) # => N x 3
#> [1] 1000 3
head(popu)
#> True condition Outcome Cases
#> 1 present positive TP
#> 2 present positive TP
#> 3 present positive TP
#> 4 present positive TP
#> 5 present positive TP
#> 6 present positive TP
# (A) Diagnostic/screening scenario (using default labels):
comp_popu(hi = 4, mi = 1, fa = 2, cr = 3) # => computes a table of N = 10 cases.
#> True condition Outcome Cases
#> 1 present positive TP
#> 2 present positive TP
#> 3 present positive TP
#> 4 present positive TP
#> 5 present negative FN
#> 6 absent positive FP
#> 7 absent positive FP
#> 8 absent negative TN
#> 9 absent negative TN
#> 10 absent negative TN
# (B) Intervention/treatment scenario:
comp_popu(hi = 3, mi = 2, fa = 1, cr = 4,
cond_lbl = "Treatment", cond_true_lbl = "pill", cond_false_lbl = "placebo",
dec_lbl = "Health status", dec_pos_lbl = "healthy", dec_neg_lbl = "sick")
#> Treatment Health status Cases
#> 1 pill healthy TP
#> 2 pill healthy TP
#> 3 pill healthy TP
#> 4 pill sick FN
#> 5 pill sick FN
#> 6 placebo healthy FP
#> 7 placebo sick TN
#> 8 placebo sick TN
#> 9 placebo sick TN
#> 10 placebo sick TN
# (C) Prevention scenario (e.g., vaccination):
comp_popu(hi = 3, mi = 2, fa = 1, cr = 4,
cond_lbl = "Vaccination", cond_true_lbl = "yes", cond_false_lbl = "no",
dec_lbl = "Disease", dec_pos_lbl = "no flu", dec_neg_lbl = "flu")
#> Vaccination Disease Cases
#> 1 yes no flu TP
#> 2 yes no flu TP
#> 3 yes no flu TP
#> 4 yes flu FN
#> 5 yes flu FN
#> 6 no no flu FP
#> 7 no flu TN
#> 8 no flu TN
#> 9 no flu TN
#> 10 no flu TN