Wrapper around the Gibbs Sampler that returns formatted liability estimates for the proband
Usage
Gibbs_estimator(cov, tbl, out, tol = 0.01, burn_in = 1000)
Arguments
- cov
Covariance (kinship matrix times heritability with corrected diagonal) matrix
- tbl
Tibble with lower and upper bounds for the Gibbs sampler
- out
Vector indicating if genetic ans/or full liabilities should be estimated
- tol
Convergence criteria, tolerance
- burn_in
Number of burn-in iterations
Value
Formatted liability estimate(s) and standard error(s) of the mean for the proband.
Examples
# uninformative sampling:
Gibbs_estimator(cov = diag(3), tbl = tibble::tibble(lower = rep(-Inf, 3),
upper = rep(Inf, 3)), out = 1:2, tol = 0.01, burn_in = 1000)
#> # A tibble: 1 × 4
#> genetic_est genetic_se full_est full_se
#> <dbl> <dbl> <dbl> <dbl>
#> 1 -0.00323 0.00323 -0.000767 0.00318