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Bayesian regression samplers

morie_bayesian_regression.Rd

Gibbs samplers for Bayesian ridge regression and the Bayesian alphabet used in genomic prediction.

Usage

morie_brr_gibbs(y, X, n_iter = 2000L, burn_in = 500L, nu = 5, nu_beta = 5, R2 = 0.5, 
    seed = 42L)

Arguments

y

numeric response vector

X

predictor matrix

n_iter

number of Gibbs iterations

burn_in

iterations discarded as burn-in

nu

argument nu; not described in the source.

nu_beta

argument nu_beta; not described in the source.

R2

argument R2; not described in the source.

seed

argument seed; not described in the source.

Value

A list holding the fitted quantities for the method called: point estimates, fitted values and any diagnostics it produces. See the individual method descriptions above for the components each returns.

Details

These functions mirror, in R, the implementations in the Python package morie; the two are checked against each other by the parity tests in tests/testthat/.

Examples

set.seed(6)
X <- matrix(rnorm(60), 20, 3)
y <- X %*% c(0.5, -0.3, 0.2) + rnorm(20, 0, 0.3)
morie_brr_gibbs(as.numeric(y), X, n_iter = 200L, burn_in = 50L, seed = 1L)
#> $mu
#> [1] -0.04873372
#> 
#> $beta
#> [1]  0.43778331 -0.36021993  0.09793666
#> 
#> $sigma2
#> [1] 0.1351945
#> 
#> $sigma2_beta
#> [1] 0.3751684
#> 
#> $n_kept
#> [1] 150
#> 
#> $hyper
#> $hyper$S
#> [1] 1.840874
#> 
#> $hyper$S_beta
#> [1] 1.840874
#> 
#> $hyper$nu
#> [1] 5
#> 
#> $hyper$nu_beta
#> [1] 5
#> 
#> $hyper$var_y
#> [1] 0.525964
#> 
#>