riskyr (version 0.2.0)

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, ...)

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).

Value

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

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

# NOT RUN {
# 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")

# }