Usage
sample_posterior_v(y, G, min_ac = 1L, q_shape = c(2, 100), p_shape = c(10,
2), omega_shape = c(2, 9), variant_weights = NULL,
estimate_phi = !is.null(variant_weights), samples = 10000,
burn = as.integer(samples/10), just_Z = FALSE)
Arguments
y
Logical vector of case (TRUE) control (FALSE) status.
G
Integer matrix of variant counts per individual, one column per individual and one row per variant.
min_ac
Minimum allele count per individual to be considered to have a pathogenic combination of variants. '1' could correspond to a dominant inheritance hypothesis whereas '2' could correspond to a recessive inheritance hypothesis.
q_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 min_ac variants.
p_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 min_ac variants.
omega_shape
Beta shape hyper-priors for prior on rate of pathogenicity amongst variants.
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 variant_weights.
samples
Number of samples to draw.
burn
Number of samples to drop from the start of the chain.
just_Z
Logical value determining whether to only return the matrix of Z-samples.