R/riskyr_sims.R
read_popu.Rd
read_popu
reads a data frame df
(containing observations of some population
that are cross-classified on two binary variables)
and returns a scenario of class "riskyr"
(i.e., a description of the data).
read_popu(
df = popu,
ix_by_top = 1,
ix_by_bot = 2,
ix_sdt = 3,
hi_lbl = txt$hi_lbl,
mi_lbl = txt$mi_lbl,
fa_lbl = txt$fa_lbl,
cr_lbl = txt$cr_lbl,
...
)
A data frame providing a population popu
of individuals, which are identified on at least
2 binary variables and cross-classified into 4 cases in a 3rd variable.
Default: df = popu
(as data frame).
Index of variable (column) providing the 1st (X/top) perspective (in df).
Default: ix_by_top = 1
(1st column).
Index of variable (column) providing the 2nd (Y/bot) perspective (in df).
Default: ix_by_bot = 2
(2nd column).
Index of variable (column) providing
a cross-classification into 4 cases (in df).
Default: ix_by_bot = 3
(3rd column).
Label of cases classified as hi (TP).
Label of cases classified as mi (FN).
Label of cases classified as fa (FP).
Label of cases classified as cr (TN).
Additional parameters (passed to riskyr
).
An object of class "riskyr" describing a risk-related scenario.
Note that df
needs to be structured (cross-classified)
according to the data frame popu
,
created by comp_popu
.
comp_popu
creates data (as df) from description (frequencies);
write_popu
creates data (as df) from a riskyr scenario (description);
popu
for data format;
riskyr
initializes a riskyr
scenario.
Other functions converting data/descriptions:
comp_popu()
,
write_popu()
# Generating and interpreting different scenario types:
# (A) Diagnostic/screening scenario (using default labels): ------
popu_diag <- comp_popu(hi = 4, mi = 1, fa = 2, cr = 3)
# popu_diag
scen_diag <- read_popu(popu_diag, scen_lbl = "Diagnostics", popu_lbl = "Population tested")
plot(scen_diag, type = "prism", area = "no", f_lbl = "namnum")
# (B) Intervention/treatment scenario: ------
popu_treat <- comp_popu(hi = 80, mi = 20, fa = 45, cr = 55,
cond_lbl = "Treatment", cond_true_lbl = "pill", cond_false_lbl = "placebo",
dec_lbl = "Health status", dec_pos_lbl = "healthy", dec_neg_lbl = "sick")
# popu_treat
s_treat <- read_popu(popu_treat, scen_lbl = "Treatment", popu_lbl = "Population treated")
plot(s_treat, type = "prism", area = "sq", f_lbl = "namnum", p_lbl = "num")
plot(s_treat, type = "icon", lbl_txt = txt_org, col_pal = pal_org)
# (C) Prevention scenario (e.g., vaccination): ------
popu_vacc <- comp_popu(hi = 960, mi = 40, fa = 880, cr = 120,
cond_lbl = "Vaccination", cond_true_lbl = "yes", cond_false_lbl = "no",
dec_lbl = "Disease", dec_pos_lbl = "no flu", dec_neg_lbl = "flu")
# popu_vacc
s_vacc <- read_popu(popu_vacc, scen_lbl = "Vaccination effects", popu_lbl = "RCT population")
plot(s_vacc, type = "prism", area = "sq", f_lbl = "namnum", col_pal = pal_rgb, p_lbl = "num")