Description Usage Arguments Value Examples
Simulate summary statistics according to the (inverse-variance weighted) fixed effects model.
1 2  | generate_data_fe(M = 50 * 10^3, p = 0.01, tau2 = 0.00025,
  resref = NULL, n = rep(50 * 10^3, 10))
 | 
M | 
 number of SNPs for which to simulate summary stats.  | 
p | 
 proportion of SNPs with real non-zero effects.  | 
tau2 | 
 variance of real associated non-zero SNPs.  | 
resref | 
 an optional vector of residual variances to sample from (with replacement) when generating the standard errors for the summary stats. Default is NULL, in which case the residual variances calculated from a Cigarretes Per Day GWAS (Liu, Jiang 2019) are used.  | 
n | 
 vector of sample sizes from the contributing studies.  | 
A list containing:
An Mxk matrix of effect size estimates betajk, 
An Mxk matrix of effect size estimate  variances sjk2, 
M-length vector inverse-variance weighted meta-analysis z-scores meta.z, 
an M-length binary vector indicating real / non-real effect at each SNP Rj,
an M-length binary vector indicating true effect-size at each SNP muj.
1  | generate_data_fe(M=100, p=0.01, tau2=2.5e-4, resref=NULL, n=rep(50*10^3,10)) 
 | 
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.