sim_mp_prelim: Simulate Preliminary Investigation Data for Missing Persons

In mispitools: Missing Person Identification Tools

Simulate Preliminary Investigation Data for Missing Persons

Description

Generates a simulated database of preliminary investigation data for missing persons (MPs). This complements sim_poi_prelim which generates data for persons of interest. Supports two case types: missing children and missing migrants.

Usage

sim_mp_prelim(
  casetype = "children",
  dateinit = "1975/01/01",
  scenario = 1,
  femaleprop = 0.5,
  ext = 100,
  numsims = 10000,
  seed = 123,
  region = c("North America", "South America", "Africa", "Asia", "Europe", "Oceania"),
  regionprob = c(0.2, 0.2, 0.2, 0.1, 0.2, 0.1)
)

Arguments

casetype	Character. Type of missing person case: "children": Generates birth date, sex, birth month, and birth place "migrants": Generates age, sex, height, and region Default: "children".
dateinit	Character. Minimum birth date for simulated MPs in "YYYY/MM/DD" format. Only for casetype = "children". Default: "1975/01/01".
scenario	Integer (1 or 2). Birth date distribution scenario: 1: Non-uniform (gamma distribution) 2: Uniform distribution Only for casetype = "children". Default: 1.
femaleprop	Numeric (0-1). Proportion of females. Default: 0.5.
ext	Numeric. Extension parameter for date simulation. Default: 100.
numsims	Integer. Number of MPs to simulate. Default: 10000.
seed	Integer. Random seed for reproducibility. Default: 123.
region	Character vector. Names of regions/locations. Default: c("North America", "South America", "Africa", "Asia", "Europe", "Oceania").
regionprob	Numeric vector. Probabilities for each region. Default: c(0.2, 0.2, 0.2, 0.1, 0.2, 0.1).

Details

This function generates the "ground truth" characteristics of missing persons, while sim_poi_prelim generates the observed/recorded characteristics of persons of interest (which may include observation errors or falsified data).

Value

A data.frame with columns depending on casetype:

• children: POI-ID, DBD, Sex, Month, Birth place

• migrants: UHR-ID, Age, Sex, Height, Region

References

Marsico FL, et al. (2023). "Likelihood ratios for non-genetic evidence in missing person cases." Forensic Science International: Genetics, 66, 102891. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.fsigen.2023.102891")}

See Also

sim_poi_prelim for simulating POI data, sim_lr_prelim for using this data in LR calculations.

Examples

# Simulate missing children data
mp_children <- sim_mp_prelim(casetype = "children", numsims = 100, seed = 123)
head(mp_children)

# Simulate missing migrants data
mp_migrants <- sim_mp_prelim(casetype = "migrants", numsims = 100, seed = 456)
head(mp_migrants)

mispitools documentation built on Aug. 26, 2026, 1:08 a.m.