Title: Missing Person Identification Tools
Version: 1.2.0
Description: An open source software package written in R statistical language. It consist in a set of decision making tools to conduct missing person searches. Particularly, it allows computing optimal LR threshold for declaring potential matches in DNA-based database search. More recently 'mispitools' incorporates preliminary investigation data based LRs. Statistical weight of different traces of evidence such as biological sex, age and hair color are presented. For citing mispitools please use the following references: Marsico and Caridi, 2023 <doi:10.1016/j.fsigen.2023.102891> and Marsico, Vigeland et al. 2021 <doi:10.1016/j.fsigen.2021.102519>.
License: GPL (≥ 3)
Encoding: UTF-8
LazyData: true
Imports: forrel, pedtools, dplyr, tidyr, tidyverse, DirichletReg, stats, purrr, patchwork, reshape2, graphics, ggplot2, shiny
RoxygenNote: 7.2.3
URL: https://github.com/MarsicoFL/mispitools
BugReports: https://github.com/MarsicoFL/mispitools/issues
Depends: R (≥ 2.10)
NeedsCompilation: no
Packaged: 2024-08-16 14:20:37 UTC; franco
Author: Franco Marsico ORCID iD [aut, cre]
Maintainer: Franco Marsico <franco.lmarsico@gmail.com>
Repository: CRAN
Date/Publication: 2024-08-17 02:40:02 UTC

mispitools: Missing Person Identification Tools

Description

An open source software package written in R statistical language. It consist in a set of decision making tools to conduct missing person searches. Particularly, it allows computing optimal LR threshold for declaring potential matches in DNA-based database search. More recently 'mispitools' incorporates preliminary investigation data based LRs. Statistical weight of different traces of evidence such as biological sex, age and hair color are presented. For citing mispitools please use the following references: Marsico and Caridi, 2023 doi: 10.1016/j.fsigen.2023.102891 and Marsico, Vigeland et al. 2021 doi: 10.1016/j.fsigen.2021.102519.

Author(s)

Maintainer: Franco Marsico franco.lmarsico@gmail.com (ORCID)

See Also

Useful links:

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

Argentina

Format

A data frame allele frequencies

STRs allelic frequencies from specified country.

Description

A dataset of allele frequencies.

Usage

Asia

Format

A data frame allele frequencies

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

Austria

Format

A data frame allele frequencies

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

BosniaHerz

Format

A data frame allele frequencies

Missing person based conditioned probability

Description

Missing person based conditioned probability

Usage

CPT_MP(MPs = "F", MPc = 1, eps = 0.05, epa = 0.05, epc = Cmodel())

Arguments

MPs

Missing person sex

MPc

Missing person hair color

eps

sex epsilon

epa

age epsilon - Age is not specified in this first version, because it asumes uniformity.

epc

color model

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, sex.

Examples

CPT_MP()

Population based conditioned probability

Description

Population based conditioned probability

Usage

CPT_POP(
  propS = c(0.5, 0.5),
  MPa = 40,
  MPr = 6,
  propC = c(0.3, 0.2, 0.25, 0.15, 0.1)
)

Arguments

propS

age epsilon - Age is not specified in this first version, because it asumes uniformity.

MPa

Missing person sex

MPr

Missing person hair color

propC

sex epsilon

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, sex.

Examples

CPT_POP()

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

China

Format

A data frame allele frequencies

Epsilon hair color matrix

Description

Epsilon hair color matrix

Usage

Cmodel(
  errorModel = c("custom", "uniform")[1],
  ep = 0.01,
  ep12 = 0.01,
  ep13 = 0.005,
  ep14 = 0.01,
  ep15 = 0.003,
  ep23 = 0.01,
  ep24 = 0.003,
  ep25 = 0.01,
  ep34 = 0.003,
  ep35 = 0.003,
  ep45 = 0.01
)

Arguments

errorModel

custom allows selecting a specfic epsilon for each MP-UHR pair, uniform use ep for all.

ep

epsilon

ep12

epsilon

ep13

epsilon

ep14

epsilon

ep15

epsilon

ep23

epsilon

ep24

epsilon

ep25

epsilon

ep34

epsilon

ep35

epsilon

ep45

epsilon

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, sex.

Examples

Cmodel()

General plot for condiionted probabilities and LR combining variables

Description

General plot for condiionted probabilities and LR combining variables

Usage

CondPlot(CPT_POP, CPT_MP)

Arguments

CPT_POP

Population conditioned probability table

CPT_MP

Missing person conditioned probability table

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, sex.

Examples

Cmodel()

Decision Threshold: a function for computing likelihood ratio decision threshold.

Description

Decision Threshold: a function for computing likelihood ratio decision threshold.

Usage

DeT(datasim, weight)

Arguments

datasim

Input dataframe containing expected LRs for related and unrelated POIs. It should be the output from makeLRsims function.

weight

The differential weight between false positives and false negatives. A value of 10 is suggested.

Value

A value of Likelihood ratio suggested as threshold based on false positive-false negative trade-off.

Examples

library(forrel)
x = linearPed(2)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)
DeT(datasim, 10)

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

Europe

Format

A data frame allele frequencies

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

Japan

Format

A data frame allele frequencies

Likelihood ratio for age variable

Description

Likelihood ratio for age variable

Usage

LRage(
  MPa = 40,
  MPr = 6,
  UHRr = 1,
  gam = 0.07,
  nsims = 1000,
  epa = 0.05,
  erRa = epa,
  H = 1,
  modelA = c("uniform", "custom")[1],
  LR = FALSE,
  seed = 1234
)

Arguments

MPa

Missing person age

MPr

Missing person age range.

UHRr

Unidentified person range

gam

Simulation parameter for UHR ages.

nsims

number of simulations.

epa

epsilon age

erRa

error rate in the database.

H

hipothesis tested, H1: UHR is MP, H2: UHR is not MP.

modelA

reference database probabilities, uniform assumes equally probable ages. Custom needs a vector with ages frequencies.

LR

compute LR values

seed

For reproducible simulations

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, Age.

Likelihood ratio for color variable

Description

Likelihood ratio for color variable

Usage

LRcol(
  MPc = 1,
  epc = Cmodel(),
  erRc = epc,
  nsims = 1000,
  Pc = c(0.3, 0.2, 0.25, 0.15, 0.1),
  H = 1,
  Qprop = MPc,
  LR = FALSE,
  seed = 1234
)

Arguments

MPc

MP hair color

epc

epsilon paramenter.

erRc

error rate in the database.

nsims

number of simulations performed.

Pc

hair color probabilities.

H

hypothesis tested, H1: UHR is MP, H2: UHR is no MP

Qprop

Query color tested.

LR

compute LR values

seed

For reproducible simulations

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, hair color.

Examples

LRcol()

Simulate LR values considering H1 and H2

Description

Simulate LR values considering H1 and H2

Usage

LRcolors(df, seed = 1234, nsim = 500)

Arguments

df

A data.frame containing the characteristics of individuals, numerator, f_h_s_y and LRs. Output from compute_LRs function.

seed

For replication purposes.

nsim

Number of LRs simulated.

Value

LR distribution considering H1 (Related) and H2 (Unrelated).

Likelihood ratio for birth date in missing person searches

Description

Likelihood ratio for birth date in missing person searches

Usage

LRdate(
  ABD = "1976-05-31",
  DBD = "1976-07-15",
  PrelimData,
  alpha = c(1, 4, 60, 11, 6, 4, 4),
  cuts = c(-120, -30, 30, 120, 240, 360),
  draw = 500,
  type = 1,
  seed = 123
)

Arguments

ABD

Actual birth date of the missing person.

DBD

Declared birth date of the person of interest.

PrelimData

Used when type = 2, is the dataframe with the DBD of the persons of interest in the database.

alpha

A vector containing the alpha values for the dirichlet. It should contain the number of categories of differences between DBD and ABD.

cuts

Value of differences between DBD and ABD used for category definition.

draw

Number of simulations for Dirichlet distribution computation.

type

Type of scenario, type 1 is an "open search", where it is unknown if the missing person is in the database. Type 2 refers to a scenario where the missing person is in the database.

seed

Seed for simulations.

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, birth date.

Examples

library(DirichletReg)
LRdate(ABD = "1976-05-31", DBD = "1976-07-15", 
PrelimData, alpha = c(1, 4, 60, 11, 6, 4, 4), 
cuts = c(-120, -30, 30, 120, 240, 360), 
type = 1, seed = 123)

Likelihood ratio distribution: a function for plotting expected log10(LR) distributions under relatedness and unrelatedness.

Description

Likelihood ratio distribution: a function for plotting expected log10(LR) distributions under relatedness and unrelatedness.

Usage

LRdist(datasim)

Arguments

datasim

Input dataframe containing expected LRs for related and unrelated POIs. It should be the output from makeLRsims function.

Value

A plot showing likelihood ratio distributions under relatedness and unrelatedness hypothesis.

Examples

library(forrel)
x = linearPed(2)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)
LRdist(datasim)

Likelihood ratio for sex variable

Description

Likelihood ratio for sex variable

Usage

LRsex(
  MPs = "F",
  eps = 0.05,
  erRs = eps,
  nsims = 1000,
  Ps = c(0.5, 0.5),
  H = 1,
  LR = FALSE,
  seed = 1234
)

Arguments

MPs

MP sex

eps

epsilon paramenter.

erRs

error rate in the database.

nsims

number of simulations performed.

Ps

Sex probabilities in the population.

H

hypothesis tested, H1: UHR is MP, H2: UHR is no MP

LR

compute LR values

seed

For reproducible simulations

Value

A value of Likelihood ratio based on preliminary investigation data. In this case, sex.

Examples

LRsex()

Threshold rates: a function for computing error rates and Matthews correlation coefficient of a specific LR threshold.

Description

Threshold rates: a function for computing error rates and Matthews correlation coefficient of a specific LR threshold.

Usage

Trates(datasim, threshold)

Arguments

datasim

Input dataframe containing expected LRs for related and unrelated POIs. It should be the output from makeLRsims function.

threshold

Likelihood ratio threshold selected for error rates calculation.

Value

Values of false positive and false negative rates and MCC for a specific LR threshold.

Examples

library(forrel)
x = linearPed(2)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)
Trates(datasim, 10)

STRs allelic frequencies from specified country.

Description

STRs allelic frequencies from specified country.

Usage

USA

Format

A data frame allele frequencies

Kullback-Leibler Divergence Calculation for Genetic Markers

Description

This function calculates the Kullback-Leibler divergence for shared genetic markers between two populations, considering allele frequencies. It normalizes data, adjusts zero frequencies, and calculates divergence in both directions.

Usage

bidirectionalKL(data1, data2, minFreq = 1e-10)

Arguments

data1

DataFrame with allele frequencies for the first population.

data2

DataFrame with allele frequencies for the second population.

minFreq

Minimum frequency to be considered for unobserved or poorly observed alleles.

Value

A list containing the Kullback-Leibler divergence from data1 to data2 and vice versa.

Examples

bidirectionalKL(Argentina, BosniaHerz)

Combine LRs: a function for combining LRs obtained from simulations.

Description

Combine LRs: a function for combining LRs obtained from simulations.

Usage

combLR(LRdatasim1, LRdatasim2)

Arguments

LRdatasim1

A data frame object with the results of simulations. Outputs from simLRgen or simLRprelim funcionts.

LRdatasim2

A second data frame object with the results of simulations. Outputs from simLRgen or simLRprelim funcionts.

Value

An object of class data.frame combining the LRs obtained from simulations (the function multiplies the LRs).

Examples

library(mispitools)
library(forrel) 
x = linearPed(2)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
LRdatasim1 = simLRgen(x, missing = 5, 10, 123)
LRdatasim2 = simLRprelim("sex")
combLR(LRdatasim1,LRdatasim2)

Compute Likelihood Ratios based con color characteristics

Description

This function calculates the Likelihood Ratios (LRs) for each combination of hair colour, skin colour, and eye colour between two datasets. It assumes one dataset (conditioned) contains numerators and the other (unconditioned) contains denominators.

Usage

compute_LRs_colors(conditioned, unconditioned)

Arguments

conditioned

A dataframe with at least the columns 'hair_colour', 'skin_colour', 'eye_colour', and 'numerators'.

unconditioned

A dataframe with at least the columns 'hair_colour', 'skin_colour', 'eye_colour', and 'f_h_s_y'.

Value

A dataframe with the merged data and computed LRs.

Examples

data <- simRef()
conditioned <- conditionedProp(data, 1, 1, 1, 0.01, 0.01, 0.01) 
unconditioned <- refProp(data)
compute_LRs_colors(conditioned, unconditioned)

Compute Conditioned Proportions for UPs

Description

This function calculates the conditioned proportions for pigmentation traits for UP, when UP is MP. It considers error rates for observations of hair color, skin color, and eye color.

Usage

conditionedProp(data, h, s, y, eh, es, ey)

Arguments

data

A data.frame containing the characteristics of UPs.

h

An integer representing the MP's hair color.

s

An integer representing the MP's skin color.

y

An integer representing the MP's eye color.

eh

A numeric value representing the error rate for observing hair color.

es

A numeric value representing the error rate for observing skin color.

ey

A numeric value representing the error rate for observing eye color.

Value

A numeric vector containing the conditioned proportion (numerator) for each individual in the dataset. These values are calculated based on the probability of observing the given combination of characteristics in the MP, compared to each UP.

Decision making plot: a function for plotting false positive and false negative rates for each LR threshold.

Description

Decision making plot: a function for plotting false positive and false negative rates for each LR threshold.

Usage

deplot(datasim, LRmax = 1000)

Arguments

datasim

Input dataframe containing expected LRs for related and unrelated POIs. It should be the output from makeLRsims function.

LRmax

Maximum LR value used as a threshold. 1000 setted by default.

Value

A plot showing false positive and false negative rates for each likelihood ratio threshold.

Examples

library(forrel)
x = linearPed(2)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)
deplot(datasim)

Function for getting STR allele frequencies from different world populations.

Description

Function for getting STR allele frequencies from different world populations.

Usage

getfreqs(region)

Arguments

region

select the place of the allele frequency database. Possible values are listed: "Argentina", "Asia", "Europe", "USA", "Austria", "BosniaHerz", "China" and "Japan".

Value

An allele frequency database adapted compatible with pedtools format.

Source

https://doi.org/10.1016/j.fsigss.2009.08.178; https://doi.org/10.1016/j.fsigen.2016.06.008; https://doi.org/10.1016/j.fsigen.2018.07.013.

Calculate Kullback-Leibler Divergence with Base 10 Logarithm

Description

This function computes the Kullback-Leibler (KL) divergence between two probability distributions represented by matrices, using a base 10 logarithm. The function calculates KL divergence in both directions (P || Q and Q || P) and handles zero probabilities by replacing them with a minimum value to avoid undefined logarithms.

Usage

klPIE(P, Q, min_value = 1e-12)

Arguments

P

A numeric matrix representing the first probability distribution. The entire matrix should sum to 1.

Q

A numeric matrix representing the second probability distribution. The entire matrix should sum to 1.

min_value

A numeric value representing the minimum value to replace any zero probabilities. Defaults to 1e-12.

Value

A named numeric vector with two elements:

"P || Q"

The KL divergence from P to Q (P || Q).

"Q || P"

The KL divergence from Q to P (Q || P).

Make preliminary investigation MP data simulations: a function for obtaining a database of preliminary investigation data for a missing person search.

Description

Make preliminary investigation MP data simulations: a function for obtaining a database of preliminary investigation data for a missing person search.

Usage

makeMPprelim(
  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

Type of missing person search case. Two options are available: "migrants" or "children".

dateinit

Minimun birth date of simulated missing person. Casetype: Children.

scenario

Birth date distribution scenarios: (1) non-uniform, (2) uniform. Casetype: Children.

femaleprop

Proportion of females. Casetype: All.

ext

Time extension for minimun birth date, range in scenario 1 and days in scenario 2. Casetype: Children.

numsims

Number of simulated MPs. Casetype: All.

seed

Select a seed for simulations. If it is defined, results will be reproducible. Casetype: All.

region

Birth region or place in missing children case or place of place of the last seen in missing migrant case. Casetype: All.

regionprob

Region proportions. Casetype: All.

Value

An object of class data.frame with preliminary investigation data.

Examples

makeMPprelim()

Make POIs gen: a function for obtaining a database with genetic information from simulated POIs or UHRs.

Description

Make POIs gen: a function for obtaining a database with genetic information from simulated POIs or UHRs.

Usage

makePOIgen(numsims = 100, reference, seed = 123)

Arguments

numsims

Number of simulations performed (numer of POIs or UHRs).

reference

Indicate the reference STRs/SNPs frequency database used for simulations.

seed

Select a seed for simulations. If it is defined, results will be reproducible. Suggested, seed = 123

Value

An object of class data.frame with genetic information from POIs (randomly sampled from the frequency database).

Examples

library(forrel) 
freqdata <- getfreqs(Argentina)
makePOIgen(numsims = 100, reference = freqdata, seed = 123)

Make preliminary investigation POI/UHR data simulations: a function for obtaining a database of preliminary investigation data for a missing person search.

Description

Make preliminary investigation POI/UHR data simulations: a function for obtaining a database of preliminary investigation data for a missing person search.

Usage

makePOIprelim(
  casetype = "children",
  dateinit = "1975/01/01",
  scenario = 1,
  femaleprop = 0.5,
  ext = 100,
  numsims = 10000,
  seed = 123,
  birthprob = c(0.09, 0.9, 0.01),
  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

Type of missing person search case. Two options are available: "migrants" or "children".

dateinit

Minimun birth date of simulated persons of interest. Casetype: Children.

scenario

Birth date distribution scenarios: (1) non-uniform, (2) uniform. Casetype: Children.

femaleprop

Proportion of females. Casetype: All.

ext

Time extension for minimun birth date, range in scenario 1 and days in scenario 2. Casetype: Children.

numsims

Number of simulated POIs/UHRs. Casetype: All.

seed

Select a seed for simulations. If it is defined, results will be reproducible. Casetype: All.

birthprob

Birth type probabilities: home birth, hospital birth and unknown-adoption. Casetype: Children.

region

Birth region or place in missing children case or place of discovery of the human remain in missing migrant case. Casetype: All.

regionprob

Region proportions. Casetype: All.

Value

An object of class data.frame with preliminary investigation data.

Examples

makePOIprelim(
  dateinit = "1975/01/01",
  scenario = 1,
  femaleprop = 0.5,
  ext = 100,
  numsims = 10000,
  seed = 123,
  birthprob = c(0.09, 0.9, 0.01),
  region = c("North America", "South America", "Africa", "Asia", "Europe", "Oceania"),
  regionprob = c(0.2, 0.2, 0.2, 0.1, 0.2, 0.1))

Missing person shiny app

Description

Missing person shiny app

Usage

mispiApp()

Value

An user interface for computing non-genetic LRs and conditioned probability tables.

Examples

CPT_MP()

Multi-dataset Kullback-Leibler Divergence Calculation

Description

This function calculates the Kullback-Leibler divergence for all pairs of provided datasets, considering allele frequencies. It normalizes data, adjusts zero frequencies, and computes KL divergence in both directions for each pair.

Usage

multi_kl_divergence(datasets, minFreq = 1e-10)

Arguments

datasets

List of dataframes, each containing allele frequencies for different populations.

minFreq

Minimum frequency to be considered for unobserved or poorly observed alleles.

Value

A matrix containing the Kullback-Leibler divergence for each dataset pair.

Examples

kl_matrix <- multi_kl_divergence(list(Argentina, BosniaHerz, Europe))

postSim: A function for simulating posterior odds

Description

postSim: A function for simulating posterior odds

Usage

postSim(
  datasim,
  Prior = 0.01,
  PriorModel = c("prelim", "uniform")[1],
  eps = 0.05,
  erRs = 0.01,
  epc = Cmodel(),
  erRc = Cmodel(),
  MPc = 1,
  epa = 0.05,
  erRa = 0.01,
  MPa = 10,
  MPr = 2
)

Arguments

datasim

Output from simLRgen function.

Prior

Prior probability for H1

PriorModel

Prior odds model: "prelim" is based on preliminary data, and "uniform" uses only the prior probability of H1

eps

epsilon parameter sex

erRs

error parameter sex

epc

epsilon parameter hair color

erRc

error parameter hair color

MPc

Missing person hair color

epa

epsilon parameter age

erRa

error parameter age

MPa

Missing person age

MPr

Missing person age error range

Value

A value of posterior odds.

Examples

library(forrel)
x = linearPed(2)
plot(x)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)
postSim(datasim)

Generate a dataframe with hair colour, skin colour, eye colour and their specific combination frequencies

Description

This function creates a dataframe that lists every unique combination of hair colour, skin colour, and eye colour in the provided dataset, along with the proportion of occurrences of each combination.

Usage

refProp(data)

Arguments

data

A data.frame containing the characteristics of individuals.

Value

A data.frame with columns for hair_colour, skin_colour, eye_colour, and f_h_s_y.

Examples

data <- simRef(1000)
refProp(data)

simLR2dataframe: A function for extracting LR distributions in a dataframe from simLRgen() output.

Description

simLR2dataframe: A function for extracting LR distributions in a dataframe from simLRgen() output.

Usage

simLR2dataframe(datasim)

Arguments

datasim

Input dataframe containing expected LRs for related and unrelated POIs. It should be the output from makeLRsims function.

Value

A dataframe with LR values obtained from simulations.

Simulate likelihoods ratio (LRs) based on genetic data: a function for obtaining expected LRs under relatedness and unrelatedness kinship hypothesis.

Description

Simulate likelihoods ratio (LRs) based on genetic data: a function for obtaining expected LRs under relatedness and unrelatedness kinship hypothesis.

Usage

simLRgen(reference, missing, numsims, seed, numCores = 1)

Arguments

reference

Reference pedigree. It could be an input from read_fam() function or a pedigree built with pedtools.

missing

Missing person ID/label indicated in the pedigree.

numsims

Number of simulations performed.

seed

Select a seed for simulations. If it is defined, results will be reproducible. Suggested, seed = 123

numCores

Enables parallelization

Value

An object of class data.frame with LRs obtained for both hypothesis, Unrelated where POI is not MP or Related where POI is MP.

Examples

library(forrel)
x = linearPed(2)
plot(x)
x = setMarkers(x, locusAttributes = NorwegianFrequencies[1:5])
x = profileSim(x, N = 1, ids = 2)
datasim = simLRgen(x, missing = 5, 10, 123)

Simulate likelihoods ratio (LRs) based on preliminary investigation data: a function for obtaining expected LRs under relatedness and unrelatedness kinship hypothesis.

Description

Simulate likelihoods ratio (LRs) based on preliminary investigation data: a function for obtaining expected LRs under relatedness and unrelatedness kinship hypothesis.

Usage

simLRprelim(
  vartype,
  numsims = 1000,
  seed = 123,
  int = 5,
  ErrorRate = 0.05,
  alphaBdate = c(1, 4, 60, 11, 6, 4, 4),
  numReg = 6,
  MP = NULL,
  database,
  cuts = c(-120, -30, 30, 120, 240, 360)
)

Arguments

vartype

Indicates type of preliminary investigation variable. Options are: sex, region, age, birthDate and height.

numsims

Number of simulations performed.

seed

Seed for simulations.

int

Interval parameter, used for height and age vartypes. It defines the estimation range, for example, if MP age is 55, and int is 10, the estimated age range will be between 45 and 65.

ErrorRate

Error rate for sex, region, age and Height LR calculations.

alphaBdate

Vector containing alpha parameters for Dirichlet distribution. Usually they are the frequencies of the solved cases in each category.

numReg

Number of regions present in the case.

MP

Introduce the preliminary data of the selected variable (vartype) of the MP. If it is null, open search is carried out. If it is not NULL, close search LR is computed. Variables values must be named as those presented in makePOIprelim function.

database

It is used when the close search (MP not NULL), is carried out. It could be the output from makePOIprelim or a database with the same structure.

cuts

Value of differences between DBD and ABD used for category definition. They must be the same as the ones selected for alphaBdate vector.

Value

An object of class data.frame with LRs obtained for both hypothesis, Unrelated where POI/UHR is not MP or Related where POI/UHR is MP.

Examples

library(mispitools) 
simLRprelim("sex")

Generate Reference Properties for a Hypothetical Population

Description

This function simulates a dataset representing physical characteristics (hair color, skin color, eye color) of a hypothetical population, based on conditional probability distributions. The size of the simulated population can be adjusted by the user.

Usage

simRef(n = 1000, seed = 1234)

Arguments

n

The number of individuals in the simulated population.

seed

Selected seed for simulations.

Value

A data.frame with three columns: hair_colour, skin_colour, and eye_colour, each representing the respective characteristics of each individual in the sample population. The hair color is simulated based on predefined probabilities, and skin and eye colors are generated conditionally based on the hair color.

Examples

simRef(1000) # Generates a data frame with 1000 entries based on the defined distributions.