marginalpp.old: Marginal PP for models sharing information between diseases

In jennasimit/MTFM: Multinomial fine-mapping

Description

Calculate marginal model posterior probabilities for each disease

Usage

marginalpp.old(STR, ABF, PP, pr, kappa, p0, N0, ND)

Arguments

STR	list of models for diseases 1, 2, ..., n, each given in the form of a character vector, with entries "snp1%snp2%snp3". The null model is given by "1" OR "0". It is assumed that all elements of ABF, PP and pr below follow this same order.
ABF	list of log(ABF) vectors for diseases 1, 2, ...
PP	list of posterior probability 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

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.

Value

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

Author(s)

Chris Wallace

jennasimit/MTFM documentation built on Aug. 15, 2019, 6:14 p.m.