corrcoverage (version 1.2.1)

corrcov_CI_bhat: Confidence interval for corrected coverage estimate using estimated effect sizes and their standard errors

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

Examples

# NOT RUN {
# }
# NOT RUN {
 # 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)
# }
# NOT RUN {
# }