BayesA via short Gibbs sampler (Meuwissen-Hayes-Goddard 2001)

Per-marker variance with scaled inverse chi-squared prior.

Usage

morie_bayes_ridge_gibbs(
  x,
  y,
  n_iter = 200,
  burn = 50,
  df0 = 4,
  S0 = NULL,
  seed = 0,
  deterministic_seed = NULL
)

Arguments

x: (n x p) marker matrix.
y: Numeric response.
n_iter: Iterations.
burn: Burn-in.
df0: Prior df (default 4).
S0: Prior scale (default anchors to var(y)/p).
seed: Seed.
deterministic_seed: Optional integer; if supplied, RNG state is derived via morie_det_rng() keyed on ("brdgf", deterministic_seed) so Py<->R streams agree on the canonical fixture. When NULL (default) behaviour is unchanged.

Value

list(estimate, beta, beta_se, sigma_j2, sigma2, n_iter, n, p, method).

References

Meuwissen-Hayes-Goddard (2001) Genetics 157:1819.

Examples

morie_bayes_ridge_gibbs(x = rnorm(50), y = rnorm(50))
#> $estimate
#> [1] 0.1446291
#> 
#> $beta
#> [1] 0.1446291
#> 
#> $beta_se
#> [1] 0.1401686
#> 
#> $se
#> [1] 0.1401686
#> 
#> $sigma_j2
#> [1] 0.5497321
#> 
#> $sigma2
#> [1] 0.7255755
#> 
#> $intercept
#> [1] 0.02140593
#> 
#> $n_iter
#> [1] 150
#> 
#> $n
#> [1] 50
#> 
#> $p
#> [1] 1
#> 
#> $method
#> [1] "BayesA short Gibbs (Meuwissen-Hayes-Goddard)"
#>