corrcov_CI_bhat: Confidence interval for corrected coverage estimate using...

In corrcoverage: Correcting the Coverage of Credible Sets from Bayesian Genetic Fine Mapping

Description

Obtain confidence interval for corrected coverage estimate using estimated effect sizes and their standard errors

Usage

corrcov_CI_bhat(
  bhat,
  V,
  N0,
  N1,
  Sigma,
  thr,
  W = 0.2,
  nrep = 1000,
  CI = 0.95,
  pp0min = 0.001
)

Arguments

bhat	Estimated effect sizes from single-SNP logistic regressions
V	Variance of estimated effect sizes
N0	Number of controls
N1	Number of cases
Sigma	SNP correlation matrix
thr	Minimum threshold for fine-mapping experiment
W	Prior for the standard deviation of the effect size parameter beta
nrep	The number of simulated posterior probability systems to consider for the corrected coverage estimate (nrep = 1000 default)
CI	The size of the confidence interval (as a decimal)
pp0min	Only average over SNPs with pp0 > pp0min

Value

CI for corrected coverage estimate

Author(s)

Anna Hutchinson

Examples

 # this is a long running example
set.seed(1)
nsnps <- 100
N0 <- 5000 # number of controls
N1 <- 5000 # number of cases

## generate example LD matrix
library(mvtnorm)
nsamples = 1000

simx <- function(nsnps, nsamples, S, maf=0.1) {
    mu <- rep(0,nsnps)
    rawvars <- rmvnorm(n=nsamples, mean=mu, sigma=S)
    pvars <- pnorm(rawvars)
    x <- qbinom(1-pvars, 1, maf)
}

S <- (1 - (abs(outer(1:nsnps,1:nsnps,`-`))/nsnps))^4
X <- simx(nsnps,nsamples,S)
LD <- cor2(X)
maf <- colMeans(X)

varbeta <- Var.data.cc(f = maf, N = N0 + N1, s = N1/(N0+N1))

bhats = rnorm(nsnps,0,0.2) # log OR

corrcov_CI_bhat(bhat = bhats, V = varbeta, N0, N1, Sigma = LD)

corrcoverage documentation built on Dec. 7, 2019, 1:07 a.m.