| Title: | Multiplicity of Infection and Allele Frequency Recovery from Noisy Polyallelic Genetics Data |
| Version: | 3.7.0 |
| Description: | A Markov Chain Monte Carlo (MCMC) based approach to Bayesian estimation of individual level multiplicity of infection, within host relatedness, and population allele frequencies from polyallelic genetic data. Implements the model described in Murphy and Greenhouse (2024) <doi:10.1093/bioinformatics/btae619>. |
| License: | GPL (≥ 3) |
| Encoding: | UTF-8 |
| LazyData: | true |
| LazyDataCompression: | bzip2 |
| SystemRequirements: | C++17, GNU make |
| LinkingTo: | Rcpp, RcppProgress, RcppParallel, BH |
| Imports: | Rcpp, RcppProgress, RcppParallel, dplyr, tidyr, stats, parallel, purrr, rlang, ggplot2 |
| URL: | https://github.com/EPPIcenter/moire, https://eppicenter.github.io/moire/, https://eppicenter.ucsf.edu/resources |
| BugReports: | https://github.com/EPPIcenter/moire/issues |
| RoxygenNote: | 7.3.3 |
| Suggests: | knitr, rmarkdown, forcats, testthat (≥ 3.0.0), parallelly |
| VignetteBuilder: | knitr |
| Depends: | R (≥ 4.1.0) |
| Config/testthat/edition: | 3 |
| NeedsCompilation: | yes |
| Packaged: | 2026-09-15 17:53:11 UTC; mmurphy |
| Author: | Maxwell Murphy 
[aut, cre],
Bryan Greenhouse
[aut, ths] |
| Maintainer: | Maxwell Murphy <mm@maxmurphy.dev> |
| Repository: | CRAN |
| Date/Publication: | 2026-09-26 16:50:02 UTC |
Calculate the expected heterozygosity from allele frequencies
Description
Calculate the expected heterozygosity from allele frequencies
Usage
calculate_he(allele_freqs)
Arguments
allele_freqs |
Simplex of allele frequencies |
Value
Numeric scalar, the expected heterozygosity 1 - sum(p^2).
Examples
calculate_he(c(0.5, 0.3, 0.2))
Calculate the geometric median of the posterior distribution of allele frequencies
Description
Calculate the geometric median of the posterior distribution of allele frequencies
Usage
calculate_med_allele_freqs(mcmc_results, merge_chains = TRUE)
Arguments
mcmc_results |
Result of calling run_mcmc() |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Returns the geometric median of the posterior distribution, defined as the point minimizing the L2 distance from each sampled point.
Value
If merge_chains is TRUE, a named list with one numeric vector of
allele frequencies per locus. Otherwise a list with one such list per chain.
Examples
med_freqs <- calculate_med_allele_freqs(mcmc_results)
med_freqs[[1]]
Calculate naive allele frequencies
Description
Calculate naive allele frequencies
Usage
calculate_naive_allele_frequencies(data)
Arguments
data |
List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus |
Details
Estimate naive allele frequencies from the empirical distribution of alleles
Value
List with one numeric vector of allele frequencies per locus.
Examples
naive_freqs <- calculate_naive_allele_frequencies(simulated_data$data)
naive_freqs[[1]]
Calculate naive COI
Description
Calculate naive COI
Usage
calculate_naive_coi(data)
Arguments
data |
List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus. |
Details
Estimates the complexity of infection using a naive approach that chooses the highest number of observed alleles.
Value
Numeric vector with one naive COI estimate per sample.
Examples
calculate_naive_coi(simulated_data$data)
Calculate naive COI offset
Description
Calculate naive COI offset
Usage
calculate_naive_coi_offset(data, offset)
Arguments
data |
List of lists of numeric vectors, where each list element is a collection of observations across samples at a single genetic locus. |
offset |
Numeric offset – n'th highest number of observed alleles |
Details
Estimates the complexity of infection using a naive approach that chooses the n'th highest number of observed alleles.
Value
Numeric vector with one naive COI estimate per sample.
Examples
calculate_naive_coi_offset(simulated_data$data, offset = 2)
Load delimited data
Description
Load delimited data
Usage
load_delimited_data(data, sep = ";", warn_uninformative = TRUE)
Arguments
data |
data.frame containing the described data |
sep |
string used to separate alleles |
warn_uninformative |
boolean whether or not to print message when removing uninformative loci |
Details
Load data.frame with a sample_id column and the remaining
columns are loci. Each cell contains a separator delimited string
representing the observed alleles at that locus for that sample.
Returned data contains vectors sample_ids and loci that are ordered
as the results will be ordered from running the MCMC algorithm.
Value
List structured for run_mcmc(); see load_long_form_data().
Examples
df <- data.frame(
sample_id = c("S1", "S2", "S3"),
L1 = c("A;B", "A", "B"),
L2 = c("A", "A;C", "C")
)
dat <- load_delimited_data(df, sep = ";")
dat$sample_ids
dat$data[["L2"]]
Load long form data
Description
Load long form data
Usage
load_long_form_data(df, warn_uninformative = TRUE)
Arguments
df |
data frame with 3 columns: |
warn_uninformative |
boolean whether or not to print message when removing uninformative loci |
Details
Long form data is a data frame with
3 columns: sample_id, locus, allele. Returned data contains
vectors sample_ids and loci that are ordered as the results
will be ordered from running the MCMC algorithm.
Value
List structured for run_mcmc() with elements sample_ids,
data (list of per-locus lists of binary allele vectors), loci,
is_missing (loci by samples logical matrix), and uninformative_loci
(loci removed for having a single allele).
Examples
df <- data.frame(
sample_id = c("S1", "S1", "S1", "S2", "S2"),
locus = c("L1", "L1", "L2", "L1", "L2"),
allele = c("A", "B", "A", "A", "B")
)
dat <- load_long_form_data(df)
dat$loci
dat$data[["L1"]]
# A subset of the bundled Namibia data
ids <- unique(namibia_data$sample_id)[1:20]
dat <- load_long_form_data(namibia_data[namibia_data$sample_id %in% ids, ])
str(dat, max.level = 1)
MCMC results from using the packaged simulated data and calling run_mcmc()
Description
MCMC results from using the packaged simulated data and calling run_mcmc()
Usage
mcmc_results
Format
A list as returned by run_mcmc() on simulated_data: a single
chain with 1000 burnin and 1000 sampling iterations, using 80 parallel
tempering replicas with adaptive temperatures. The mcmc_demo vignette
shows the call.
Genetic and epidemiological data from Namibia
Description
A dataset containing the genetic and epidemiological data from Namibia
Usage
namibia_data
Format
A data frame with 7 columns and 97214 rows:
- sample_id
Sample ID
- HealthFacility
Health facility
- HealthDistrict
Health district
- Region
Region
- Country
Country
- locus
Genetic locus
- allele
Allele observed
Source
Plot chain swap acceptance rates
Description
Plot chain swap acceptance rates
Usage
plot_chain_swaps(mcmc_results)
Arguments
mcmc_results |
list of results from |
Details
Plot the swap acceptance rates for each chain. The x-axis is the temperature, and the y-axis is the swap acceptance rate. The dashed lines indicate the temperatures used for parallel tempering.
Value
list of ggplot objects, one per chain
Examples
plots <- plot_chain_swaps(mcmc_results)
plots[[1]]
Dirichlet distribution
Description
Dirichlet distribution
Usage
rdirichlet(n, alpha)
Arguments
n |
total number of draws |
alpha |
vector controlling the concentration of simplex |
Details
Implementation of random sampling from a Dirichlet distribution
Value
Numeric matrix with n rows and length(alpha) columns; each row
is a draw from the simplex.
Examples
rdirichlet(3, alpha = c(1, 1, 1))
Allele frequencies for different regions
Description
A list of allele frequencies for different regions, estimated from the pf7k dataset.
Usage
regional_allele_frequencies
Format
A list of lists, where each list element is a list of allele frequencies for a specific region.
Sample from the target distribution using MCMC
Description
Sample from the target distribution using MCMC
Usage
run_mcmc(
data,
is_missing = FALSE,
allow_relatedness = TRUE,
thin = 1,
burnin = 10000,
samples_per_chain = 1000,
verbose = TRUE,
use_message = FALSE,
eps_pos_alpha = 1,
eps_pos_beta = 1,
eps_neg_alpha = 1,
eps_neg_beta = 1,
r_alpha = 1,
r_beta = 1,
mean_coi_shape = 0.1,
mean_coi_scale = 10,
max_eps_pos = 2,
max_eps_neg = 2,
max_coi = 40,
record_latent_genotypes = FALSE,
num_chains = 1,
num_cores = 1,
pt_chains = 1,
pt_grad = 1,
pt_num_threads = 1,
adapt_temp = TRUE,
pre_adapt_steps = 25,
temp_adapt_steps = 25,
max_initialization_tries = 10000,
max_runtime = Inf,
seed = NULL
)
Arguments
data |
Data to be used in MCMC, as generated by the |
is_missing |
Boolean matrix indicating whether the observation should be treated as missing data and ignored. Number of rows equals the number of loci, number of columns equals the number samples. Alternatively, the user may pass in FALSE if no data should be considered missing. |
allow_relatedness |
Bool indicating whether or not to allow relatedness within host |
thin |
Positive Integer. How often to sample from mcmc, 1 means do not thin |
burnin |
Positive Integer. Number of MCMC samples to discard as burnin |
samples_per_chain |
Positive Integer. Number of samples to take after burnin |
verbose |
Logical indicating if progress is printed |
use_message |
Logical indicating if progress is printed using message or print |
eps_pos_alpha |
Positive Numeric. Alpha parameter in Beta distribution for eps_pos prior |
eps_pos_beta |
Positive Numeric. Beta parameter in Beta distribution for eps_pos prior |
eps_neg_alpha |
Positive Numeric. Alpha parameter in Beta distribution for eps_neg prior |
eps_neg_beta |
Positive Numeric. Beta parameter in Beta distribution for eps_neg prior |
r_alpha |
Positive Numeric. Alpha parameter in Beta distribution for relatedness prior |
r_beta |
Positive Numeric. Beta parameter in Beta distribution for relatedness prior |
mean_coi_shape |
shape parameter for gamma hyperprior on mean COI |
mean_coi_scale |
scale parameter for gamma hyperprior on mean COI |
max_eps_pos |
Numeric. Maximum allowed value for eps_pos |
max_eps_neg |
Numeric. Maximum allowed value for eps_neg |
max_coi |
Positive Numeric. Maximum allowed complexity of infection |
record_latent_genotypes |
Logical indicating whether or not to record the latent genotypes at each step of the MCMC. WARNING: This will increase the size of the output object significantly. |
num_chains |
Total number of chains to run, possibly simultaneously |
num_cores |
How many independent chains to run at once, passed to
|
pt_chains |
Total number of chains to run with parallel tempering or a vector containing the temperatures that should be used for parallel tempering. |
pt_grad |
Power to raise parallel tempering chains to. A value of 1 results in evenly distributed temperatures between [0,1], below 1 will bias towards 1 and above 1 will bias towards 0. Only used if pt_chains is a single value (i.e. not a vector). |
pt_num_threads |
Total number of OMP parallel threads to be used to process parallel tempered chains num_cores * pt_num_threads should not exceed the number of cores available on your system. |
adapt_temp |
Logical indicating whether or not to adapt the parallel
tempering temperatures. If TRUE, the temperatures will be adapted during the
|
pre_adapt_steps |
Number of steps to take before starting to adapt the
parallel tempering temperatures. Only used if |
temp_adapt_steps |
Number of steps to take between temperature
adaptation steps. Only used if |
max_initialization_tries |
Number of times to try to initialize the chain before giving up |
max_runtime |
Maximum runtime in minutes. If the MCMC is running for more than this amount of time, the function will stop and return the current state of the MCMC. |
seed |
Integer seed. |
Value
List with elements:
- chains
List with one entry per chain. Each holds the sampled
coi,allele_freqs,eps_pos,eps_neg, andrelatedness, along with log-likelihood traces, acceptance rates, and parallel tempering diagnostics. Pass the whole result to thesummarize_*()functions.- args
The arguments the sampler was called with, including
data.- seed
Integer seed used for the run.
- chain_seeds
Integer seed used for each chain.
- runtime
Wall-clock time of the run as a
difftime.
Initialization failure
If every Initialization retry yields a non-finite genotyping log-likelihood,
run_mcmc stops with a classed error
(moire_initialization_failure) whose message includes rule-based guidance
and whose $diagnostics field has Failure-locus counts and classification
(Consistent failure cause vs Hard starting set). Inspect with
tryCatch(run_mcmc(...), error = function(e) e$diagnostics).
Examples
sim <- simulate_data(
mean_coi = 2,
num_samples = 10,
epsilon_pos = 0.01,
epsilon_neg = 0.1,
locus_freq_alphas = list(rep(1, 4), rep(1, 4), rep(1, 4))
)
# A short run for illustration. Real analyses should use far more burnin
# and samples (see the defaults) and parallel tempering (pt_chains) to
# improve mixing.
res <- run_mcmc(
sim,
is_missing = sim$is_missing,
burnin = 100,
samples_per_chain = 100,
verbose = FALSE,
seed = 1
)
res$seed
summarize_coi(res)
Simulate allele frequencies
Description
Simulate allele frequencies
Usage
simulate_allele_frequencies(alpha, num_loci)
Arguments
alpha |
vector parameter controlling the Dirichlet distribution |
num_loci |
total number of loci to draw |
Details
Simulate allele frequency vectors as a draw from a Dirichlet distribution
Value
Numeric matrix with length(alpha) rows and num_loci columns;
each column is an allele frequency vector for one locus.
Examples
simulate_allele_frequencies(alpha = c(1, 1, 1, 1), num_loci = 3)
Simulate data generated according to the assumed model
Description
Simulate data generated according to the assumed model
Usage
simulate_data(
mean_coi = NULL,
num_samples,
epsilon_pos,
epsilon_neg,
sample_cois = NULL,
locus_freq_alphas = NULL,
allele_freqs = NULL,
internal_relatedness_alpha = 0,
internal_relatedness_beta = 1,
internal_relatedness = NULL,
missingness = 0
)
Arguments
mean_coi |
Mean multiplicity of infection drawn from a Poisson |
num_samples |
Total number of biological samples to simulate |
epsilon_pos |
False positive rate, expected number of false positives |
epsilon_neg |
False negative rate, expected number of false negatives |
sample_cois |
List of sample COIs to be used instead of simulating |
locus_freq_alphas |
List of alpha vectors to be used to simulate from a Dirichlet distribution to generate allele frequencies. |
allele_freqs |
List of allele frequencies to be used instead of simulating allele frequencies |
internal_relatedness_alpha |
alpha parameter of beta distribution controlling the random relatedness draws for each sample |
internal_relatedness_beta |
beta parameter of beta distribution controlling the random relatedness draws for each sample |
internal_relatedness |
List of internal relatedness values to be used instead of simulating |
missingness |
probability of data being missing |
Value
List structured for run_mcmc(), with elements data,
sample_ids, loci, and is_missing, along with the simulated truth
(allele_freqs, sample_cois, sample_relatedness, true_genotypes)
and the input arguments.
Examples
sim <- simulate_data(
mean_coi = 2,
num_samples = 10,
epsilon_pos = 0.01,
epsilon_neg = 0.1,
locus_freq_alphas = list(rep(1, 4), rep(1, 4), rep(1, 4))
)
sim$sample_cois
str(sim$data, max.level = 1)
Simulates the observation process
Description
Simulates the observation process
Usage
simulate_observed_allele(alleles, epsilon_pos, epsilon_neg, missingness)
Arguments
alleles |
A numeric vector representing the number of strains contributing each allele |
epsilon_pos |
expected number of false positives |
epsilon_neg |
expected number of false negatives |
missingness |
probability that the data is missing |
Details
Takes a numeric value representing the number of strains contributing an allele and returns a binary vector indicating the presence or absence of the allele.
Value
Binary numeric vector the same length as alleles indicating
which alleles were observed. All zeros if the observation is missing.
Examples
simulate_observed_allele(
alleles = c(2, 0, 1),
epsilon_pos = 0.01, epsilon_neg = 0.1, missingness = 0
)
Simulate observed genotypes
Description
Simulate observed genotypes
Usage
simulate_observed_genotype(
true_genotypes,
epsilon_pos,
epsilon_neg,
missingness
)
Arguments
true_genotypes |
a list of numeric vectors that are input to sim_observed_allele |
epsilon_pos |
expected number of false positives |
epsilon_neg |
expected number of false negatives |
missingness |
probability of data being missing |
Details
Simulate the observation process across a list of observation vectors
Value
List the same length as true_genotypes, each element a binary
vector of observed alleles as returned by simulate_observed_allele().
Examples
true_genotypes <- list(c(1, 0, 1), c(0, 2, 0))
simulate_observed_genotype(
true_genotypes,
epsilon_pos = 0.01, epsilon_neg = 0.1, missingness = 0
)
Simulate sample COI
Description
Simulate sample COI
Usage
simulate_sample_coi(num_samples, mean_coi)
Arguments
num_samples |
the total number of biological samples to simulate |
mean_coi |
mean multiplicity of infection |
Details
Simulate sample COIs from a zero-truncated Poisson distribution
Value
Integer vector of length num_samples with a COI of at least 1
for each sample.
Examples
simulate_sample_coi(num_samples = 10, mean_coi = 2)
Simulate sample genotype
Description
Simulate sample genotype
Usage
simulate_sample_genotype(sample_cois, locus_allele_dist, internal_relatedness)
Arguments
sample_cois |
Numeric vector indicating the multiplicity of infection for each biological sample |
locus_allele_dist |
Allele frequencies – simplex parameter of a multinomial distribution |
internal_relatedness |
numeric 0-1 indicating the probability for a strain's allele to come from an existing lineage within host |
Details
Simulates sampling the genetics at a single locus given an allele frequency distribution and a vector of sample COIs
Value
List with one element per sample, each a 1-row integer matrix counting the number of distinct strains carrying each allele.
Examples
simulate_sample_genotype(
sample_cois = c(1, 2, 3),
locus_allele_dist = c(0.5, 0.3, 0.2),
internal_relatedness = c(0, 0, 0.5)
)
Simulated genotyping data
Description
A simulated dataset created using simulate_data()
Usage
simulated_data
Format
A list as returned by simulate_data(), with 100 samples at 30
loci (15 with 5 alleles, 15 with 10) and a mean COI of 5: observed data,
sample_ids, loci, is_missing, and the simulated truth
(allele_freqs, sample_cois, sample_relatedness, true_genotypes).
Summarize Function of Allele Frequencies
Description
Summarize Function of Allele Frequencies
Usage
summarize_allele_freq_fn(
mcmc_results,
fn,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
fn |
Function that takes as input a simplex to apply to each allele frequency vector |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
General function to summarize the posterior distribution of functions of the sampled allele frequencies
Value
Data frame with one row per locus (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of fn applied to the sampled
allele frequencies.
Examples
# Posterior summary of the major allele frequency at each locus
major_allele <- summarize_allele_freq_fn(mcmc_results, fn = max)
head(major_allele)
Summarize allele frequencies
Description
Summarize allele frequencies
Usage
summarize_allele_freqs(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize individual allele frequencies from the posterior distribution of sampled allele frequencies
Value
Data frame with one row per allele (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of its frequency, along with
locus and allele identifiers.
Examples
allele_freq_summary <- summarize_allele_freqs(mcmc_results)
head(allele_freq_summary)
Summarize COI
Description
Summarize COI
Usage
summarize_coi(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
naive_offset = 2,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
naive_offset |
Offset used in calculate_naive_coi_offset |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize complexity of infection results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of COI for each biological sample, as well as naive estimates of COI.
Value
Data frame with one row per sample (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of COI, naive estimates, and
the posterior probability that the sample is polyclonal.
Examples
coi_summary <- summarize_coi(mcmc_results)
head(coi_summary)
Summarize effective COI
Description
Summarize effective COI
Usage
summarize_effective_coi(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize effective COI from MCMC. Returns a dataframe that contains summaries of the posterior distribution of effective COI for each biological sample.
Value
Data frame with one row per sample (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of effective COI,
(1 - relatedness) * (COI - 1) + 1.
Examples
effective_coi_summary <- summarize_effective_coi(mcmc_results)
head(effective_coi_summary)
Summarize epsilon_neg
Description
Summarize epsilon_neg
Usage
summarize_epsilon_neg(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize epsilon negative results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of epsilon negative for each biological sample.
Value
Data frame with one row per sample (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of the false negative rate.
Examples
eps_neg_summary <- summarize_epsilon_neg(mcmc_results)
head(eps_neg_summary)
Summarize epsilon_pos
Description
Summarize epsilon_pos
Usage
summarize_epsilon_pos(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize epsilon positive results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of epsilon positive for each biological sample.
Value
Data frame with one row per sample (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of the false positive rate.
Examples
eps_pos_summary <- summarize_epsilon_pos(mcmc_results)
head(eps_pos_summary)
Summarize locus heterozygosity
Description
Summarize locus heterozygosity
Usage
summarize_he(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
Merge the results of multiple chains into a single summary |
Details
Summarize locus heterozygosity from the posterior distribution of sampled allele frequencies.
Value
Data frame with one row per locus (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of expected heterozygosity.
Examples
he_summary <- summarize_he(mcmc_results)
head(he_summary)
Summarize relatedness
Description
Summarize relatedness
Usage
summarize_relatedness(
mcmc_results,
lower_quantile = 0.025,
upper_quantile = 0.975,
merge_chains = TRUE
)
Arguments
mcmc_results |
Result of calling run_mcmc() |
lower_quantile |
The lower quantile of the posterior distribution to return |
upper_quantile |
The upper quantile of the posterior distribution to return |
merge_chains |
boolean indicating that all chain results should be merged |
Details
Summarize relatedness results from MCMC. Returns a dataframe that contains summaries of the posterior distribution of relatedness for each biological sample.
Value
Data frame with one row per sample (per chain if merge_chains is
FALSE) giving posterior quantiles and mean of within-host relatedness,
computed over draws where COI is greater than 1.
Examples
relatedness_summary <- summarize_relatedness(mcmc_results)
head(relatedness_summary)