riskyr (version 0.3.0)

read_popu: Read a population (given as data frame) into a riskyr scenario.

Description

read_popu interprets a data frame df (that contains individual observations of some population) and returns a scenario of class "riskyr".

Usage

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,
  ...
)

Value

An object of class "riskyr" describing a risk-related scenario.

Arguments

df

A data frame providing a population popu of individuals, which are identified on at least 2 binary variables and classified into 4 cases in a 3rd variable. Default: df = popu (as data frame).

ix_by_top

Index of variable (column) providing the 1st (top) perspective (in df). Default: ix_by_top = 1 (1st column).

ix_by_bot

Index of variable (column) providing the 2nd (bot) perspective (in df). Default: ix_by_bot = 2 (2nd column).

ix_sdt

Index of variable (column) providing a classification into 4 cases (in df). Default: ix_by_bot = 3 (3rd column).

hi_lbl

Variable label of cases classified as hi (TP).

mi_lbl

Variable label of cases classified as mi (FN).

fa_lbl

Variable label of cases classified as fa (FP).

cr_lbl

Variable label of cases classified as cr (TN).

...

Additional parameters (to be passed to riskyr function).

Details

Note that df needs to be structured according to the popu created by comp_popu.

See Also

the corresponding data frame popu; the corresponding generating function comp_popu; riskyr initializes a riskyr scenario.

Other riskyr scenario functions: plot.riskyr(), riskyr(), summary.riskyr()

Examples

# 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
scen_treat <- read_popu(popu_treat, scen_lbl = "Treatment", popu_lbl = "Population treated")
plot(scen_treat, type = "prism", area = "sq", f_lbl = "namnum", p_lbl = "num")
plot(scen_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
scen_vacc <- read_popu(popu_vacc, scen_lbl = "Prevention", popu_lbl = "Population vaccinated")
plot(scen_vacc, type = "prism", area = "sq", f_lbl = "namnum", col_pal = pal_bw, p_lbl = "num")