sample_drop_model: Sample drop model(s) with parameters according to priors

In simDNAmixtures: Simulate Forensic DNA Mixtures

Sample drop model(s) with parameters according to priors

Description

Sample drop model(s) with parameters according to priors

Usage

sample_drop_model(
  number_of_contributors,
  sampling_parameters,
  drop_in_rate = 0,
  model_settings
)

Arguments

number_of_contributors	Integer
sampling_parameters	List. Needs to contain: min_dropout_probability. Numeric of length one. max_dropout_probability Numeric of length one.
drop_in_rate	Numeric vector of length one. Expected number of drop-ins per locus. Default is 0.
model_settings	List. See drop_model.

Details

In simulation studies involving many mixed DNA profiles, one often needs to generate various samples with different model parameters. This function samples a drop model with parameters according to prior distributions. The dropout probability for each contributor is sampled uniformly between min_dropout_probability. and max_dropout_probability.

Value

When length(number_of_contributors)==1, a single drop_model of class pg_model. Otherwise, a list of these.

See Also

sample_mixtures_fixed_parameters to directly supply parameters of choice for more control

Examples

gf <- gf_configuration()

sampling_parameters <- list(min_dropout_probability. = 0., max_dropout_probability. = 0.5)

model <- sample_drop_model(number_of_contributors = 1,
                           sampling_parameters = sampling_parameters,
                           model_settings = list(locus_names = gf$autosomal_markers,
                           size_regression = gf$size_regression))

simDNAmixtures documentation built on April 15, 2025, 1:11 a.m.