Calculate marginal model posterior probabilities for each disease
marginalpp(STR, ABF, pr, kappa, p0, N0, ND, nsnps, I0 = as.list(rep(0, length(ND))))
| STR | list of models for diseases 1, 2, ..., n, each given in
the form of a character vector, with entries
|
|---|---|
| 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. the value of kappa=1 must be included, and if not will be prepended. |
| p0 | prior probability of the null model |
| N0 | number of shared controls |
| ND | list of number of cases for a set of diseases |
| nsnps | number of snps in region |
| I0 | list of number of controls for a set of diseases that are only used as controls for a specific disease (ie not shared) ... |
list of: - single.pp: list of pp for each model in
STR[[i]] for disease i - shared.pp: list of pp for each model
in STR[[i]] for disease i, - STR: not quite as input,
reordered so null model is first row - ABF: not quite as
input, repordered so null model is first row - kappa: as
supplied
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.