Compute a population table (data) from frequencies (description).

comp_popu computes a table popu (as an R data frame) from the current frequency information (contained in freq).

Usage

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
)

Format

An object of class data.frame with N rows and 3 columns (e.g., "X/truth/cd", "Y/test/dc", "SDT/cell/class").

Arguments

hi: The number of hits hi (or true positives).
mi: The number of misses mi (or false negatives).
fa: The number of false alarms fa (or false positives).
cr: The number of correct rejections cr (or true negatives).
cond_lbl: Text label for condition dimension ("by cd" perspective).
cond_true_lbl: Text label for cond_true cases.
cond_false_lbl: Text label for cond_false cases.
dec_lbl: Text label for decision dimension ("by dc" perspective).
dec_pos_lbl: Text label for dec_pos cases.
dec_neg_lbl: Text label for dec_neg cases.
sdt_lbl: Text label for 4 cases/combinations (SDT classifications).
hi_lbl: Text label for hi cases.
mi_lbl: Text label for mi cases.
fa_lbl: Text label for fa cases.
cr_lbl: Text label for cr cases.

Value

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).

Details

By default, comp_popu uses the text settings contained in txt.

A visualization of the current population popu is provided by plot_icons.

Examples

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