Marginal PP for models sharing information between diseases

Source: R/marginalpp.R

Calculate marginal model posterior probabilities for each disease

marginalpp.models(M, ABF, pr, kappa, p0)

Arguments

M

list of model matrices for diseases 1, 2, ..., n
ABF

list of log(ABF) vectors for diseases 1, 2, ...
pr

list of prior probabilities for the models in M
kappa

single value or vector of values to consider for the sharing scale parameter
p0

prior probability of the null model

Value

list of:

  • single.pp: list of pp for each model in M[[i]] for disease i

  • shared.pp: list of pp for each model in M[[i]] for disease i, M (not quite as input, reordered so null model is first row

  • ABF: not quite as input, repordered so null model is first

  • M: reordered so null model is first row

  • kappa: as supplied

Details

Given a list of model matrices and log ABFs, this function calculates the marginal model posterior probabilities for each disease without ever calculating the joint Bayes Factors for all cross-disease model configurations, which would require large amounts of memory.