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Defines a gamma model for peak height variability

gamma_model.Rd

Defines a gamma model for peak height variability

Usage

gamma_model(
  mixture_proportions,
  mu,
  cv,
  degradation_beta = rep(1, length(mixture_proportions)),
  LSAE = stats::setNames(rep(1, length(model_settings$locus_names)),
    model_settings$locus_names),
  model_settings
)

Arguments

mixture_proportions

Numeric vector with the mixture proportion for each contributor.

mu

Numeric. Expectation of a full heterozygote contributing allele peak height.

cv

Numeric. Coefficient of variation of a full heterozygote contributing allele peak height

degradation_beta

Numeric Vector of same length as mixture_proportions. Degradation slope parameters for each contributor. Defaults to 1 for each contributor (i.e. not degraded)

LSAE

Numeric vector (named) with Locus Specific Amplification Efficiencies. See sample_LSAE. Defaults to 1 for each locus.

model_settings

List. Possible parameters:

  • locus_names. Character vector.

  • detection_threshold. Numeric vector (named) with Detection Thresholds.

  • size_regression. Function, see read_size_regression.

  • stutter_model. Optionally a stutter_model object that gives expected stutter heights. See global_stutter_model.

Value

Object of class pg_model.

Details

Define a gamma model for peak height variability with the parametrisation as described by Bleka et al. The model may then be used to sample DNA profiles using the sample_mixture_from_genotypes function. Alternatively, to sample many models and profiles in one go with parameters according to a specified distribution, the sample_mixtures function can be used.

References

Bleka, Ø., Storvik, G., & Gill, P. (2016). EuroForMix: An open source software based on a continuous model to evaluate STR DNA profiles from a mixture of contributors with artefacts. Forensic Science International: Genetics, 21, 35-44. doi:10.1016/j.fsigen.2015.11.008

See also

log_normal_model.

Examples

# read allele frequencies
freqs <- read_allele_freqs(system.file("extdata","FBI_extended_Cauc_022024.csv",
                                       package = "simDNAmixtures"))

gf <- gf_configuration()

# define the gamma model for peak heights
model <- gamma_model(mixture_proportions = 1, mu = 1000.,
                    cv = 0.1, model_settings = gf$gamma_settings_no_stutter)

# sample a single source profile (1-person 'mixture')
u1 <- sample_contributor_genotypes("U1", freqs, loci = gf$autosomal_markers)
sample <- sample_mixture_from_genotypes(u1, model)

# peaks follow a gamma distribution with an expected height of
# 1,000 for heterozygous alleles; 2,000 for homozygotes
hist(sample$Height)


# the gamma distribution is more obvious if many samples are taken
many_samples <- replicate(n = 1e2,
                          sample_mixture_from_genotypes(u1, model),
                          simplify = FALSE)

hist(sapply(many_samples, function(x) x$Height))