infer_mixture: Estimate mixing proportions and origin probabilities from one...

In rubias: Bayesian Inference from the Conditional Genetic Stock Identification Model

Description

Takes a mixture and reference dataframe of two-column genetic data, and a desired method of estimation for the population mixture proportions (MCMC, PB, BR). Returns the output of the chosen estimation method

Usage

infer_mixture(
  reference,
  mixture,
  gen_start_col,
  method = "MCMC",
  alle_freq_prior = list(const_scaled = 1),
  pi_prior = NA,
  pi_init = NULL,
  reps = 2000,
  burn_in = 100,
  pb_iter = 100,
  prelim_reps = NULL,
  prelim_burn_in = NULL,
  sample_int_Pi = 1,
  sample_theta = TRUE,
  pi_prior_sum = 1
)

Arguments

reference	a dataframe of two-column genetic format data, proceeded by "repunit", "collection", and "indiv" columns. Does not need "sample_type" column, and will be overwritten if provided
mixture	a dataframe of two-column genetic format data. Must have the same structure as reference dataframe, but "collection" and "repunit" columns are ignored. Does not need "sample_type" column, and will be overwritten if provided
gen_start_col	the first column of genetic data in both data frames
method	a choice between "MCMC", "PB", "BR" methods for estimating mixture proportions
alle_freq_prior	a one-element named list specifying the prior to be used when generating Dirichlet parameters for genotype likelihood calculations. Valid methods include "const", "scaled_const", and "empirical". See ?list_diploid_params for method details.
pi_prior	The prior to be added to the collection allocations, in order to generate pseudo-count Dirichlet parameters for the simulation of new pi vectors in MCMC. Default value of NA leads to the calculation of a symmetrical prior based on pi_prior_sum. To provide other values to certain collections, you can pass in a data frame with two columns, "collection" listing the relevant collection, and "pi_param" listing the desired prior for that collection. Specific priors may be listed for as few as one collection. The special collection name "DEFAULT_PI" is used to set the prior for all collections not explicitly listed; if no "DEFAULT_PI" is given, it is taken to be 1/(# collections).
pi_init	The initial value to use for the mixing proportion of collections. This lets the user start the chain from a specific value of the mixing proportion vector. If pi_init is NULL (the default) then the mixing proportions are all initialized to be equal. Otherwise, you pass in a data frame with one column named "collection" and the other named "pi_init". Every value in the pi_init column must be strictly positive (> 0), and a value must be given for every collection. If they sum to more than one the values will be normalized to sum to one.
reps	the number of iterations to be performed in MCMC
burn_in	how many reps to discard in the beginning of MCMC when doing the mean calculation. They will still be returned in the traces if desired.
pb_iter	how many bootstrapped data sets to do for bootstrap correction using method PB. Default is 100.
prelim_reps	for method "BR", the number of reps of conditional MCMC (as in method "MCMC") to perform prior to MCMC with baseline resampling. The posterior mean of mixing proportions from this conditional MCMC is then used as pi_init in the baseline resampling MCMC.
prelim_burn_in	for method "BR", this sets the number of sweeps out of prelim_reps that should be discarded as burn in when preparing the posterior means of the mixing proportions to be set as pi_init in the baseline resampling MCMC.
sample_int_Pi	how many iterations between storing the mixing proportions trace. Default is 1. Can't be 0. Can't be so large that fewer than 10 samples are taken from the burn in and the sweeps.
sample_theta	for method "BR", whether or not the function should store the posterior mean of the updated allele frequences. Default is TRUE
pi_prior_sum	For pi_prior = NA, the prior on the mixing proportions is set as a Dirichlet vector of length C, with each element being W/C, where W is the pi_prior_sum and C is the number of collections. By default this is 1. If it is made much smaller than 1, things could start to mix more poorly.

Details

"MCMC" estimates mixing proportions and individual posterior probabilities of assignment through Markov-chain Monte Carlo conditional on the reference allele frequencies, while "PB" does the same with a parametric bootstrapping correction, and "BR" runs MCMC sweeps while simulating reference allele frequencies using the genotypes of mixture individuals and allocations from the previous sweep. All methods default to a uniform 1/(# collections or RUs) prior for the mixing proportions.

Value

Tidy data frames in a list with the following components: mixing_proportions: the estimated mixing proportions of the different collections. indiv_posteriors: the posterior probs of fish being from each of the collections. mix_prop_traces: the traces of the mixing proportions. Useful for computing credible intervals. bootstrapped_proportions: If using method "PB" this returns the bootstrap corrected reporting unit proportions.

Examples

mcmc <- infer_mixture(reference = small_chinook_ref,
                      mixture = small_chinook_mix,
                      gen_start_col = 5,
                      method = "MCMC",
                      reps  = 200)

rubias documentation built on Feb. 10, 2022, 1:06 a.m.