calcAdjPP: Calculates trait-adjusted posterior probabilities for all...

In jennasimit/flashfm: flexible and shared information fine-mapping

Calculates trait-adjusted posterior probabilities for all traits at sharing parameter kappa

Description

Calculates trait-adjusted posterior probabilities for all traits at sharing parameter kappa

Usage

calcAdjPP(
  qt,
  STR,
  SS,
  tau,
  nsnpspermodel,
  kappa,
  PP,
  beta,
  SSy,
  Sxy,
  xcovo,
  Mx,
  N,
  allVres,
  covY,
  Nqq,
  Nq3,
  Nq4,
  fastapprox,
  NCORES
)

Arguments

qt	vector of trait names
STR	list consisting of vectors of model configurations for each trait
SS	list consisting of lists of model configuration SNPs for each trait
tau	matrix of adjustment terms
nsnpspermodel	list of number of SNPs per model for each model in STR
kappa	single value of sharing parameter kappa
PP	list consisting of vectors of posterior probabilities for the model configurations for each trait
beta	list of joint effect estimates for models in STR; multi.beta output
SSy	matrix of trait cross-products
Sxy	matrix with each column being the cross-product between SNPs and a trait
xcovo	SNP covariance matrix
Mx	vector of SNP means
N	number of individuals with measurements for all traits
allVres	list of variance residuals
covY	covariance matrix of traits
Nqq	matrix of all pair-wise counts of number of individuals with both traits in a pair measured;
Nq3	vector of counts of number of individuals with three traits measured; all triples considered; NULL if M < 4
Nq4	vector of counts of number of individuals with four traits measured; all quadruples considered; NULL if M < 5
fastapprox	logical that is TRUE when fast approximation is used that does not include unequal sample size adjustments; default is FALSE
NCORES	number of cores for parallel computing; recommend NCORES=M, but if on Windows, use NCORES=1;

Value

list of trait-adjusted posterior probabilities for each trait at sharing parameter kappa

Author(s)

Jenn Asimit

jennasimit/flashfm documentation built on July 31, 2022, 7:32 p.m.