View source: R/simulation_pipelines.R
ref_and_mix_pipeline | R Documentation |
Takes a mixture and reference dataframe of two-column genetic data, and a desired method of estimation for the population mixture proportions (MCMC, PB, or BH MCMC) Returns the output of the chosen estimation method
ref_and_mix_pipeline(
reference,
mixture,
gen_start_col,
method = "MCMC",
reps = 2000,
burn_in = 100,
sample_int_Pi = 0,
sample_int_PofZ = 0,
sample_int_omega = 0,
sample_int_rho = 0,
sample_int_PofR = 0
)
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
|
gen_start_col |
the first column of genetic data in both data frames |
method |
this must be "MCMC". "PB" and "BH" are no longer supported in this function. |
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. |
sample_int_Pi |
the number of reps between samples being taken for pi traces. If 0 no traces are taken. Only used in methods "MCMC" and "PB". |
sample_int_PofZ |
the number of reps between samples being taken for the posterior traces of each individual's collection of origin. If 0 no trace samples are taken. Used in all methods |
sample_int_omega |
the number of reps between samples being taken for collection proportion traces. If 0 no traces are taken. Only used in method "BH" |
sample_int_rho |
the number of reps between samples being taken for reporting unit proportion traces. If 0 no traces are taken. Only used in method "BH" |
sample_int_PofR |
the number of reps between samples being taken for the posterior traces of each individual's reporting unit of origin. If 0 no trace samples are taken. Only used in method "BH". |
"MCMC" estimates mixing proportions and individual posterior probabilities of assignment through Markov-chain Monte Carlo, while "PB" does the same with a parametric bootstrapping correction, and "BH" uses the misassignment-scaled, hierarchical MCMC. All methods use a uniform 1/(# collections or RUs) prior for pi/omega and rho.
mix_proportion_pipeline
returns the standard output of the chosen
mixing proportion estimation method (always a list). For method "PB",
returns the standard MCMC results, as well as the bootstrap-corrected
collection proportions under $mean$bootstrap
reference <- small_chinook_ref
mixture <- small_chinook_mix
gen_start_col <- 5
# this function expects things as factors. This function is old and needs
# to be replaced and deprecated.
reference$repunit <- factor(reference$repunit, levels = unique(reference$repunit))
reference$collection <- factor(reference$collection, levels = unique(reference$collection))
mixture$repunit <- factor(mixture$repunit, levels = unique(mixture$repunit))
mixture$collection <- factor(mixture$collection, levels = unique(mixture$collection))
mcmc <- ref_and_mix_pipeline(reference, mixture, gen_start_col, method = "MCMC")
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