| Type: | Package |
| Title: | Bayesian Evaluation of Variant Involvement in Mendelian Disease |
| Version: | 6.0 |
| Encoding: | UTF-8 |
| Date: | 2025-04-28 |
| Description: | A fast integrative genetic association test for rare diseases based on a model for disease status given allele counts at rare variant sites. Probability of association, mode of inheritance and probability of pathogenicity for individual variants are all inferred in a Bayesian framework - 'A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases', Greene et al 2017 <doi:10.1016/j.ajhg.2017.05.015>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| Imports: | Rcpp (≥ 0.12.3), Matrix, methods |
| LinkingTo: | Rcpp |
| Depends: | R (≥ 3.0.0) |
| Suggests: | rmarkdown, knitr |
| RoxygenNote: | 7.3.2 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | yes |
| Packaged: | 2025-04-29 14:18:13 UTC; dg |
| Author: | Daniel Greene [aut, cre] |
| Maintainer: | Daniel Greene <dg333@cam.ac.uk> |
| Repository: | CRAN |
| Date/Publication: | 2025-04-29 17:10:05 UTC |
Bayesian Evaluation of Variant Involvement in Mendelian Disease
Description
A fast integrative genetic association test for rare diseases.
Details
BeviMed estimates a probability of association between a case/control label and allele counts at rare variant sites in a genomic locus and also, given that there is an association, the probabilities that each variant is involved in the disease. It does so by estimating the evidence for a model where the case/control label is independent of the allele configurations, and a model in which the probability of the case/control label depends on the corresponding allele configuration and a latent partition of variants into pathogenic and non-pathogenic groups.
Author(s)
Daniel Greene.
Maintainer: Daniel Greene <dg333@cam.ac.uk>
References
Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017), http://dx.doi.org/10.1016/j.ajhg.2017.05.015.
See Also
Estimate confidence interval for estimated marginal likelihood
Description
Central limit theorem not applicable so use simulation to estimate confidence interval for evidence.
Usage
CI_gamma1_evidence(
temperatures,
y_log_lik_t_equals_1_traces,
confidence = 0.95,
simulations = 1000
)
Arguments
temperatures |
Numeric vector of temperatures of power posteriors. One chain will be created for each element of the vector at the corresponding temperature. |
y_log_lik_t_equals_1_traces |
Numeric matrix of log probabilities of |
confidence |
Numeric value of statistical confidence with which returning interval should contain the true value. |
simulations |
Integer value of number of simulations to use in estimation of the confidence interval. |
Value
Confidence interval as numeric vector of length 2.
Bayesian Evaluation of Variant Involvement in Mendelian Disease
Description
Infer probabilities of association between disease label and locus and posterior parameter values under BeviMed model.
Usage
bevimed(
y,
G,
ploidy = rep(2L, length(y)),
prior_prob_association = 0.01,
prior_prob_dominant = 0.5,
dominant_args = NULL,
recessive_args = NULL,
...
)
Arguments
y |
Logical vector of case ( |
G |
Integer matrix of variant counts per individual, one row per individual and one column per variant. |
ploidy |
Integer vector giving ploidy of samples. |
prior_prob_association |
The prior probability of association. |
prior_prob_dominant |
The prior probability of dominant inheritance given that there is an association. |
dominant_args |
Arguments to pass to |
recessive_args |
Arguments to pass to |
... |
Arguments to be passed to |
Value
BeviMed object containing results of inference.
References
Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017), http://dx.doi.org/10.1016/j.ajhg.2017.05.015.
See Also
prob_association, bevimed_m, summary.BeviMed, bevimed_polytomous
Perform inference under model gamma = 1 conditional on mode of inheritance
Description
Sample from posterior distribution of parameters under model gamma = 1 and conditional on mode of inheritance, set via the min_ac argument.
Usage
bevimed_m(
y,
G,
min_ac = 1L,
tau_shape = c(1, 1),
pi_shape = c(6, 1),
omega_shape = if (max(min_ac) == 1L) c(2, 8) else c(2, 2),
samples_per_chain = 1000,
stop_early = FALSE,
blocks = 5,
burn = as.integer(samples_per_chain/10),
temperatures = (0:6/6)^2,
tune_temps = 0,
vec_sums = FALSE,
return_z_trace = TRUE,
return_x_trace = TRUE,
raw_only = FALSE,
swaps = as.integer(length(temperatures)/2),
optimise_z0 = FALSE,
tune_omega_and_phi_proposal_sd = FALSE,
tune_block_size = 100,
variant_weights = NULL,
standardise_weights = TRUE,
log_phi_mean = -0.15,
log_phi_sd = sqrt(0.3),
tandem_variant_updates = if (max(min_ac) == 1) 0 else min(sum(y), ncol(G)),
...
)
Arguments
y |
Logical vector of case ( |
G |
Integer matrix of variant counts per individual, one row per individual and one column per variant. |
min_ac |
Integer vector with a length equalling the number of individuals or length |
tau_shape |
Beta shape hyper-priors for prior on rate of affection (i.e. being a case) amongst individuals with non-pathogenic variant combinations (i.e. they have less than |
pi_shape |
Beta shape hyper-priors for prior on rate of affection (i.e. being a case) amongst individuals with pathogenic variant combinations (i.e. they have at least |
omega_shape |
Beta shape hyper-priors for prior on rate of pathogenicity amongst variants. |
samples_per_chain |
Number of samples to draw from each chain. |
stop_early |
Logical value determining whether to attempt to stop the sampling as soon as certain conditions are met (i.e. either the estimated marginal log likelihood lies within a certain confidence interval, or we are sufficiently confidence that the log Bayes factor against of model gamma = 1 over model gamma = 0 is sufficiently low). |
blocks |
Maximum number of blocks of |
burn |
Number of samples to drop from the start of the chain. |
temperatures |
Numeric vector of temperatures of power posteriors. One chain will be created for each element of the vector at the corresponding temperature. |
tune_temps |
Integer value - if greater than 0, the |
vec_sums |
Logical value determining whether to calculate vector summary statistics. |
return_z_trace |
Logical value determining whether to store the z-vectors for each chain, which uses alot of memory, particularly if |
return_x_trace |
Logical value determining whether to store the x variable determined by success samples of z. Potentially uses alot of memory, particularly if |
raw_only |
Logical value determining whether to return raw output of MCMC routine only. |
swaps |
Number of swaps between adjacent tempered chains to perform per update cycle. |
optimise_z0 |
Logical value determining whether to use a simulated annealing optimisation run to tune the initial values of |
tune_omega_and_phi_proposal_sd |
Logical value determining whether the proposal SDs of the Metropolis-Hastings estimated parameters should be tuned for a target acceptance range. |
tune_block_size |
Integer value giving number of samples to draw when estimatating the acceptance rate of the omega/phi proposals. |
variant_weights |
Vector of log-odds off-sets for rates of pathogenicity of individual variants relative to the global rate, omega. |
standardise_weights |
Boolean value determining whether weights should be standardised by subtracting their mean and dividing by their sample standard deviation. If |
log_phi_mean |
Mean for normal prior on scaling factor phi. |
log_phi_sd |
SD for normal prior on scaling factor phi. Setting to 0 causes the weights to be fixed and not estimated. |
tandem_variant_updates |
Number of tandem variant updates to make per update cycle. |
... |
Other arguments to be passed to |
Details
A BeviMed_m object is a list containing elements:
‘parameters’: a list containing arguments used in the function call, including the adjusted weights used in the inference in the ‘c_weights’ slot,
‘traces’: a list of traces of model parameters from all MCMC chains for each parameter. Parameters sampled are z, omega, phi and x (the indicator of having a pathogenic configuration of alleles). The list of traces is named by parameter name, and each is a matrix where the rows correspond to samples. $z has k columns for each temperature, with the samples from the true posterior (i.e. with temperature equal to 1) of z corresponding to the final k columns. Likewise, the true posterior is given by the final column for the traces of phi and omega. The trace of x is only given for temperature equal to 1 to reduce memory usage.
‘final’: a list named by model parameter giving the final sample of each,
‘swaps’: a list with an element named ‘accept’ which is a logical vector whose ith element indicates whether the ith swap between adjacent tempered chains was accepted or not, and an element named 'at_temperature', an integer vector whose ith element indicates which pair of consecutive temperatures was the ith to be proposed for swapping (giving the lowest one).
Value
An object of class BeviMed_m.
References
Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017), http://dx.doi.org/10.1016/j.ajhg.2017.05.015.
See Also
Model selection for multiple association models
Description
Apply bevimed to the no association model (gamma = 0) and multiple association models for different sets of variants, for instance, corresponding to different functional consequences.
Usage
bevimed_polytomous(
y,
G,
ploidy = rep(2L, length(y)),
variant_sets,
prior_prob_association = rep(0.01/length(variant_sets), length(variant_sets)),
tau0_shape = c(1, 1),
moi = rep("dominant", length(variant_sets)),
model_specific_args = vector(mode = "list", length = length(variant_sets)),
...
)
Arguments
y |
Logical vector of case ( |
G |
Integer matrix of variant counts per individual, one row per individual and one column per variant. |
ploidy |
Integer vector giving ploidy of samples. |
variant_sets |
List of integer vectors corresponding to sets of indices of |
prior_prob_association |
The prior probability of association. |
tau0_shape |
Beta shape hyper-priors for prior on rate of case labels. |
moi |
Character vector giving mode of inheritance for each model. |
model_specific_args |
List of named lists of parameters to use in |
... |
Other arguments to pass to |
References
Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017), http://dx.doi.org/10.1016/j.ajhg.2017.05.015.
See Also
R interface to BeviMed c++ MCMC procedure
Description
Allows other functions in the package to call the c++ function passing arguments more succinctly and by name.
Usage
call_cpp(
samples_per_chain,
y,
block_starts,
block_ends,
cases,
counts,
min_ac,
tau_shape,
pi_shape,
omega_shape,
temperatures,
z0_matrix,
estimate_omega,
logit_omegas,
logit_omega_proposal_sds,
variant_weights,
estimate_phi,
log_phis,
log_phi_mean,
log_phi_sd,
log_phi_proposal_sds,
chain_swaps_per_cycle,
annealing,
tandem_variant_updates,
comphet_variant_block_starts,
comphet_variant_block_ends,
comphet_variants,
return_z_trace,
return_x_trace,
vec_sums = FALSE,
burn = 0,
check = TRUE
)
Arguments
samples_per_chain |
Number of samples to draw from each chain. |
y |
Logical vector of subject affectedness status. |
block_starts |
Integer vector of k 0-indexed start positions (with respect to |
block_ends |
Integer vector of (exclusive) k 0-indexed end positions. |
cases |
0 based vector of case indices with respect to y. |
counts |
Vector of variant counts. |
min_ac |
Integer vector with a length equalling the number of individuals or length |
tau_shape |
Beta distribution parameterisation of benign variant configuration rate of affection, q. |
pi_shape |
Beta distribution parameterisation of pathogenic variant configuration rate of affection, p. |
omega_shape |
Beta distribution of global rate of pathogenicty of variants in gene given pathogenicity of gene, omega. |
temperatures |
Numeric vector of temperatures of power posteriors. One chain will be created for each element of the vector at the corresponding temperature. |
z0_matrix |
Matrix of logicals, where the rows are used as an initial zs for the chains. |
estimate_omega |
Logical value determining whether to estimate the parameter omega. |
logit_omegas |
Numeric vector of logit omega values, one value per chain. |
logit_omega_proposal_sds |
Numeric vector of proposal standard deviations for Metropolis-Hastings sampling of logit omega parameter, one value per chain. |
variant_weights |
Vector of log-odds off-sets for rates of pathogenicity of individual variants relative to the global rate, omega. |
estimate_phi |
Logical value determining whether to estimate a scaling factor of |
log_phis |
Numeric vector of log phi values, one value per chain. |
log_phi_mean |
Mean for normal prior on scaling factor phi. |
log_phi_sd |
SD for normal prior on scaling factor phi. |
log_phi_proposal_sds |
Numeric vector of proposal standard deviations for Metropolis-Hastings sampling of log phi parameter, one value per chain. |
chain_swaps_per_cycle |
Number of chain swaps to propose per update cycle. |
annealing |
Logical value determining whether to anneal the chains, e.g. for optimisation. |
tandem_variant_updates |
Number of tandem variant updates to make per update cycle. |
comphet_variant_block_starts |
0-indexed start positions for contiguous blocks of variants in |
comphet_variant_block_ends |
As |
comphet_variants |
Integer vector giving variant numbers (0-based, i.e. between 0 and k-1). Used to pick pairs of variants for tandem updates from. |
return_z_trace |
Logical value determining whether to store the z-vectors for each chain, which uses alot of memory, particularly if |
return_x_trace |
Logical value determining whether to store the x variable determined by success samples of z. Potentially uses alot of memory, particularly if |
vec_sums |
Logical value determining whether to calculate vector summary statistics. |
burn |
Number of samples to drop from the start of the chain. |
check |
Logical value indicating whether to perform validation on the arguments before calling the c++ function. |
Value
Object of class BeviMed_raw, containing the output of the MCMC sampling.
Calculate probability of pathogencity for variants conditional on mode of inheritance.
Description
Calls bevimed_m and extract_conditional_prob_pathogenic to obtain probabilities of pathogenicity.
Usage
conditional_prob_pathogenic(...)
Arguments
... |
Arguments to pass to |
Value
Probabilities of pathogenicity.
See Also
extract_conditional_prob_pathogenic, bevimed_m
Calculate expected number of explained cases
Description
Use bevimed_m to perform inference under model gamma = 1 and return only the expected number of cases explained by pathogenic allele configurations.
Usage
expected_explained(...)
Arguments
... |
Arguments to pass to |
Value
Numeric value.
See Also
bevimed_m, extract_expected_explained
Calculate expected number of pathogenic variants in cases
Description
Use bevimed_m to perform inference under model gamma = 1 and return only the expected number of pathogenic variants in cases.
Usage
explaining_variants(...)
Arguments
... |
Arguments to pass to |
Value
Numeric value.
See Also
extract_explaining_variants, bevimed_m
Extract probability of pathogenicity for variant conditional on a given association model
Description
Extract the probability of pathogenicity for individual variants from a BeviMed_m object.
Usage
extract_conditional_prob_pathogenic(x)
Arguments
x |
Object of class |
Value
Vector of probabilities of pathogenicity for individual variants.
See Also
conditional_prob_pathogenic, bevimed_m
Extract expected number of explained cases
Description
Extract expected number of cases explained by pathogenic configurations of alleles from BeviMed_m object.
Usage
extract_expected_explained(x)
Arguments
x |
Object of class |
Value
Numeric value.
See Also
Extract expected number of pathogenic variants in cases
Description
Extract expected number of variants involved in cases explained by pathogenic conigurations of alleles from BeviMed_m object.
Usage
extract_explaining_variants(x)
Arguments
x |
Object of class |
Value
Numeric value.
See Also
explaining_variants, bevimed_m
Extract evidence for model gamma = 1
Description
Extract evidence from BeviMed_m object.
Usage
extract_gamma1_evidence(x)
Arguments
x |
Object of class |
Value
Log marginal likelihood.
See Also
Extract the posterior probability of association
Description
Get posterior probability of association as numeric value, or optionally as numeric vector of length two with probabilities broken down by mode of inheritance (by passing by_model=TRUE), from a BeviMed object.
Usage
extract_prob_association(x, by_model = FALSE)
Arguments
x |
Object of class |
by_model |
Logical value determining whether to return probabilities broken down by mode of inheritance. |
Value
Probability values.
See Also
Extract variant marginal probabilities of pathogenicity
Description
Extract the marginal probability of pathogenicity for individual variants from BeviMed object, optionally broken down by mode of inheritance/model.
Usage
extract_prob_pathogenic(x, by_model = TRUE)
Arguments
x |
Object of class |
by_model |
Logical value determining whether to return probabilities broken down by mode of inheritance. |
Value
A vector of probabilities of pathogenicity for individual variants, or if by_model is TRUE, then a matrix of probabilities, with rows corresponding to modes of inheritance and columns to variants.
See Also
Calculate marginal probability of observed case-control status y under model gamma = 0
Description
Marginal probability calculated exactly by integration.
Usage
gamma0_evidence(y, tau0_shape = c(1, 1))
Arguments
y |
Logical vector of case ( |
tau0_shape |
Beta shape hyper-priors for prior on rate of case labels |
Value
Log marginal likelihood.
See Also
Calculate evidence under model gamma = 1
Description
Use bevimed_m to perform inference under model gamma = 1 and return only the log evidence/integrated likelihood.
Usage
gamma1_evidence(...)
Arguments
... |
Arguments to pass to |
Value
Log marginal likelihood.
See Also
bevimed_m, extract_gamma1_evidence
Calculate log Bayes factor between an association model with a given mode of inheritance and model gamma = 0
Description
Compute log Bayes factor of an association model and model gamma = 0.
Usage
log_BF(y, tau0_shape = c(1, 1), ...)
Arguments
y |
Logical vector of case ( |
tau0_shape |
Beta shape hyper-priors for prior on rate of case labels. |
... |
Arguments to pass to |
Value
Log Bayes factor.
See Also
Print readable summary of BeviMed object
Description
Print summary statistics of BeviMed inference, including probability of association, probability of dominant inheritance given association and probability of pathogenicity of each variant under dominant and recessive inheritance.
Usage
## S3 method for class 'BeviMed'
print(x, ...)
Arguments
x |
|
... |
Arguments passed to |
Value
Prints a summary.
See Also
Print BeviMed_m object
Description
Print summary statistics for BeviMed_m object.
Usage
## S3 method for class 'BeviMed_m'
print(x, ...)
Arguments
x |
Object of class |
... |
Unused arguments. |
Value
Prints a summary.
See Also
Print readable summary of BeviMed_summary object.
Description
Print summary statistics of BeviMed inference, including probability of association, probability of dominant inheritance given association and probability of pathogenicity of each variant under dominant and recessive inheritance.
Usage
## S3 method for class 'BeviMed_summary'
print(x, print_prob_pathogenic = TRUE, ...)
Arguments
x |
|
print_prob_pathogenic |
Logical value indicating whether to print list of marginal probabilities of |
... |
Unused arguments |
Value
Prints a summary
Calculate probability of association
Description
Calculate probability of an association between case/control label and allele configuration, optionally broken down by mode of inheritance/model.
Usage
prob_association(by_model = FALSE, ...)
Arguments
by_model |
Logical value determining whether to return probabilities broken down by mode of inheritance. |
... |
Arguments to pass to |
Value
Probability of association.
See Also
bevimed, extract_prob_association
Calculate probability of association for one mode of inheritance
Description
Equivalent to prob_association where the prior probability of one mode of inheritance is 1. This function is faster, as it only calls bevimed_m once.
Usage
prob_association_m(y, min_ac = 1L, prior_prob_association = 0.01, ...)
Arguments
y |
Logical vector of case ( |
min_ac |
Integer vector with a length equalling the number of individuals or length |
prior_prob_association |
The prior probability of association. |
... |
Other arguments to pass to |
Value
Probability value.
See Also
log_BF, prob_association, bevimed_m
Calculate variant marginal probabilities of pathogencity
Description
Calls bevimed and extract_prob_pathogenic to obtain marginal probabilities of pathogenicity.
Usage
prob_pathogenic(by_model = FALSE, ...)
Arguments
by_model |
Logical value determining whether to return probabilities broken down by mode of inheritance. |
... |
Arguments to pass to |
Value
If by_model is FALSE, a vector of probabilities of pathogenicity for each variant, otherwise a list of vectors of probabilities of pathogenicity conditional on each compared association model.
See Also
extract_prob_pathogenic, bevimed
Concatenate objects of class BeviMed_raw
Description
This function could be used to stitch together consecutive chains to create one larger sampled set of states from the MCMC procedure.
Usage
stack_BeviMeds(objects)
Arguments
objects |
|
Value
BeviMed object.
Apply the MCMC algorithm in blocks until conditions are met
Description
Sample blocks of a given size until either the estimated log marginal likelihood falls within a given confidence interval, there is sufficient confidence that the evidence model gamma = 1 is at most a certain quantity, or a certain number of blocks have been sampled.
Usage
stop_chain(
y,
blocks_remaining,
start_zs,
start_logit_omegas,
start_log_phis,
temperatures,
tolerance = 1,
confidence = 0.95,
simulations = 1000,
log_evidence_threshold = -Inf,
y_log_lik_t_equals_1_traces = matrix(ncol = length(temperatures), nrow = 0),
full_block_traces = list(),
verbose = FALSE,
...
)
Arguments
y |
Logical vector of case ( |
blocks_remaining |
Maximum number of blocks left before termination. |
start_zs |
Initial (logical) z-matrix. |
start_logit_omegas |
Initial values of logit_omega (numeric vector - one value per chain). |
start_log_phis |
Initial values of log_phi (numeric vector - one value per chain). |
temperatures |
Numeric vector of temperatures of power posteriors. One chain will be created for each element of the vector at the corresponding temperature. |
tolerance |
Maximum width for confidence_interval of log marginal likelihood to allow before stopping the chain. |
confidence |
Numeric value of statistical confidence with which returning interval should contain the true value. |
simulations |
Integer value of number of simulations to use in estimation of the confidence interval. |
log_evidence_threshold |
Numeric value used to determine whether to stop the sampling procedure after successive blocks. If we are confident (to the level of |
y_log_lik_t_equals_1_traces |
Numeric matrix of log probabilities of |
full_block_traces |
List of outputs of calls to MCMC routine. |
verbose |
To print execution progress or not. |
... |
Other arguments passed to |
Value
An object of class BeviMed.
Remove variants with no data for pathogenicity
Description
Subset an allele count matrix given a minimum allele count threshold for pathogenicity per individual so that only variants for which data relevant to pathogencity are retained. This is useful to apply before running bevimed as it reduces the size of the parameter space used in the inference.
Usage
subset_variants(G, min_ac = 1L, return_variants = FALSE)
Arguments
G |
Integer matrix of variant counts per individual, one row per individual and one column per variant. |
min_ac |
Integer vector with a length equalling the number of individuals or length |
return_variants |
Logical value determining whether to return an integer vector of indices of retained variants or the subsetted allele count matrix |
Calculate marginal likelihood from power posteriors output
Description
Calculate the Marginal Likelihood by summation over power posterior likelihood exptectances
Usage
sum_ML_over_PP(y_log_lik_t_equals_1_traces, temperatures)
Arguments
y_log_lik_t_equals_1_traces |
Numeric matrix of log probabilities of |
temperatures |
Numeric vector of temperatures used to produce |
Value
Numeric value of estimated log marginal likelihood.
Summarise a BeviMed object
Description
Create a summary of inference over model gamma = 0 and association models.
Usage
## S3 method for class 'BeviMed'
summary(object, ...)
Arguments
object |
Object of class |
... |
Arguments passed to |
Details
Returns a BeviMed_summary object, which is a list containing elements:
'prob_association': the probability of association under each association model,
'prior_prob_association': the prior probability of association for each association model,
‘gamma0_evidence’: the log evidence under model gamma = 0,
‘models’: a list of summaries of model conditional inferences, i.e. objects of class
BeviMed_m_summary. Seesummary.BeviMed_mfor more details.
Value
Object of class BeviMed_summary.
See Also
Summarise a BeviMed_m object
Description
Create a summary of inference conditional on mode of inheritance.
Usage
## S3 method for class 'BeviMed_m'
summary(object, confidence = 0.95, simulations = 1000, ...)
Arguments
object |
Object of class |
confidence |
Numeric value of statistical confidence with which returning interval should contain the true value. |
simulations |
Integer value of number of simulations to use in estimation of the confidence interval. |
... |
Unused arguments. |
Details
Returns a BeviMed_m_summary object, which is a list containing elements:
‘gamma1_evidence’: the log evidence under model gamma = 1,
‘gamma1_evidence_confidence_interval’: a confidence interval for the log evidence under model gamma = 1,
‘conditional_prob_pathogenic’: vector of marginal probabilities of pathogenicity for individual variants,
‘expected_explained’: the expected number of cases with a pathogenic configuration of alleles,
‘explaining_variants’: the expected number of variants present for which cases harbour a rare allele,
‘number_of_posterior_samples’: the number of samples from the posterior distribution of the model parameters which upon which the summary is based,
‘omega_estimated’: logical value indicating whether the parameter omega was estimated,
‘omega’: the posterior mean of omega,
‘omega_acceptance_rate’: if omega was estimated, the rate of acceptance of proposed omega values in the Metropolis-Hastings sampling routine,
‘phi_estimated’: logical value indicating whether the parameter phi was estimated,
‘tau’: the posterior mean of tau,
‘pi’: the posterior mean of pi,
‘phi’: the posterior mean of phi,
‘phi_acceptance_rate’: if phi was estimated, the rate of acceptance of proposed phi values in the Metropolis-Hastings sampling routine,
'N': number of samples in the analysis,
'k': number of variants in the analysis,
‘variant_counts’: list of counts of each variant for cases and controls,
‘temperatures’: numeric vector of temperatures used as temperatures for tempered MCMC chains
Value
Object of class BeviMed_m_summary.
See Also
Tune proposal standard deviation for MH sampled parameters
Description
Tune the proposal standard deviations for the Metropolis-Hastings updates of either phi or omega
Usage
tune_proposal_sds(
tune_for = c("logit_omega"),
initial_proposal_sds,
target_acceptance_range = c(0.3, 0.7),
other_param_proposal_sd = 0.7,
max_tuning_cycles = 10,
initial_rate = 1,
rate_decay = 1.2,
verbose = FALSE,
...
)
Arguments
tune_for |
Character vector of length one, naming which variable to tune the proposal SDs for: either |
initial_proposal_sds |
Numeric vector with the initial values of the proposal SDs. |
target_acceptance_range |
Numeric vector of length 2 where the first element is the lower bound for the acceptance interval and the second is the upper bound. |
other_param_proposal_sd |
The proposal SD to use for |
max_tuning_cycles |
Maximum number of tuning cycles to perform before returning the proposal SDs as they are. |
initial_rate |
Initial rate at which to mutate the proposal SDs. |
rate_decay |
Geometric rate of decay for size of proposal SD mutation with each successive tuning cycle. |
verbose |
To print execution progress or not. |
... |
Other arguments to be passed to |
Value
Numeric vector of proposal SDs for the different temperature chains.
Tune temperatures
Description
Tune temperatures using interval bisection to minimimise Kullback-Liebler divergence between adjacent power posteriors
Usage
tune_temperatures(number_of_temperatures, return_temperatures = FALSE, ...)
Arguments
number_of_temperatures |
Integer value giving number of tuned temperatures (including 0 and 1) to obtain. |
return_temperatures |
Logical value determining whether to return just the numeric vector of tuned temperatures or to return the |
... |
Other arguments to pass to |
Value
If return_temperatures == TRUE, a numeric vector of tuned temperatures, otherwise an object of class BeviMed_m.