bevimed_m {BeviMed}R Documentation

Perform inference under model gamma = 1 conditional on mode of inheritance


Sample from posterior distribution of parameters under model gamma = 1 and conditional on mode of inheritance, set via the min_ac argument.


  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,
  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)),



Logical vector of case (TRUE) control (FALSE) status.


Integer matrix of variant counts per individual, one row per individual and one column per variant.


Integer vector with a length equalling the number of individuals or length 1 (in which case the given value is used for all individuals) giving the minimum number of alleles at pathogenic variant sites each individual requires in order to classify as having a ‘pathogenic allele configuration’. Thus, this parameter encodes the mode of inheritance. For instance, setting this parameter to 1 corresponds to dominant inheritance. If there are differences in ploidy between individuals in the locus, it is necessary to set it on an sample level basis - e.g. to ensure sex is accounted for if the locus lies on the X chromosome.


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 min_ac variants.


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 min_ac variants.


Beta shape hyper-priors for prior on rate of pathogenicity amongst variants.


Number of samples to draw from each chain.


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).


Maximum number of blocks of samples_per_chain samples to draw before either the confidence interval for the marginal likelihood under the model gamma = 1 is sufficiently small or terminating the sampling. This parameter is ignored if unless stop_early==TRUE.


Number of samples to drop from the start of the chain.


Numeric vector of temperatures of power posteriors. One chain will be created for each element of the vector at the corresponding temperature.


Integer value - if greater than 0, the temperatures argument is ignored, and instead tune_temps tuned temperatures are used instead.


Logical value determining whether to store the z-vectors for each chain, which uses alot of memory, particularly if samples_per_chain, k and length(temperatures) are large.


Logical value determining whether to store the x variable determined by success samples of z. Potentially uses alot of memory, particularly if samples_per_chain, k and length(temperatures) are large.


Logical value determining whether to return raw output of MCMC routine only.


Number of swaps between adjacent tempered chains to perform per update cycle.


Logical value determining whether to use a simulated annealing optimisation run to tune the initial values of z.


Logical value determining whether the proposal SDs of the Metropolis-Hastings estimated parameters should be tuned for a target acceptance range.


Integer value giving number of samples to draw when estimatating the acceptance rate of the omega/phi proposals.


Vector of log-odds off-sets for rates of pathogenicity of individual variants relative to the global rate, omega.


Boolean value determining whether weights should be standardised by subtracting their mean and dividing by their sample standard deviation. If FALSE, weights are untransformed.


Mean for normal prior on scaling factor phi.


SD for normal prior on scaling factor phi. Setting to 0 causes the weights to be fixed and not estimated.


Number of tandem variant updates to make per update cycle.


Other arguments to be passed to stop_chain and/or tune_proposal_sds.


A BeviMed_m object is a list containing elements:


An object of class BeviMed_m.


Greene et al., A Fast Association Test for Identifying Pathogenic Variants Involved in Rare Diseases, The American Journal of Human Genetics (2017),

See Also

bevimed_m, prob_association_m

[Package BeviMed version 5.8 Index]