Extract Coefficients From Bayesian Linear Regression — bayes_alphabet

This function performs Bayesian linear regression using the `gbayes` function from the `qgg` package. It then returns the estimated slopes.

Usage

bayes_alphabet_weights(
  X,
  y,
  method,
  Z = NULL,
  nit = 5000,
  nburn = 1000,
  nthin = 5,
  ...
)

Arguments

X: A numeric matrix of genotypes.
y: A numeric vector of phenotypes.
method: A character string declaring the method/prior to be used. Options are bayesN, bayesL, bayesA, bayesC, or bayesR.
Z: An optional numeric matrix of covariates.

Value

A vector containing the weights to be applied to each genotype in predicting the phenotype.

Details

This function fits a Bayesian linear regression model with a range of priors.

Examples

X <- matrix(rnorm(100000), nrow = 1000)
Z <- matrix(round(runif(3000, 0, 0.8), 0), nrow = 1000)
set1 <- sample(1:ncol(X), 5)
set2 <- sample(1:ncol(X), 5)
sets <- list(set1, set2)
g <- rowSums(X[, c(set1, set2)])
e <- rnorm(nrow(X), mean = 0, sd = 1)
y <- g + e
bayes_l_weights(y = y, X = X, Z = Z)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>   [1]  1.083237e-04 -1.133938e-02  2.923678e-01  1.665039e-03 -1.435670e-02
#>   [6]  1.148714e-03  3.015946e-03  3.465056e-03 -7.170307e-03  8.189214e-03
#>  [11] -8.331499e-03  2.882450e-01  1.335196e-03  7.527821e-03 -1.622969e-02
#>  [16]  3.704074e-04  2.788505e-01 -1.203191e-02  6.779445e-04  1.869667e-04
#>  [21]  1.259040e-02  3.009882e-01  2.753231e-03  1.220761e-04  4.817818e-03
#>  [26] -1.229220e-03  2.898960e-01  2.553293e-03 -1.522890e-03  8.571616e-03
#>  [31]  1.893174e-03  2.784205e-03  2.210253e-03 -1.311894e-03  1.052111e-02
#>  [36]  5.809198e-05  5.949950e-03  2.857641e-01  2.732437e-03  1.015982e-02
#>  [41] -4.286074e-04  2.800882e-03 -3.715099e-03 -1.342973e-02 -3.842873e-03
#>  [46] -1.984293e-03  3.017228e-01 -1.689241e-03  1.651995e-03 -1.502499e-02
#>  [51] -7.578488e-04 -5.532745e-03  8.017282e-04  7.881543e-03  2.870601e-01
#>  [56]  5.683525e-03 -7.670447e-03 -3.677440e-03  7.076280e-04 -2.355097e-03
#>  [61]  7.887919e-03  8.251210e-04  8.029241e-03  3.815260e-04  4.844815e-03
#>  [66]  1.556718e-02 -5.068509e-03  4.202568e-03 -1.887524e-03  4.730536e-03
#>  [71]  1.838760e-02 -1.061450e-03 -9.183914e-03 -7.926353e-03 -8.246118e-03
#>  [76]  5.892330e-03  6.470996e-03  4.381045e-03  3.045632e-01  2.876466e-01
#>  [81]  1.713635e-02 -3.548523e-03 -4.700027e-03  8.417642e-03  1.523007e-03
#>  [86] -9.377391e-05 -5.710638e-03 -2.108213e-03 -2.351105e-03  1.710941e-02
#>  [91]  5.165378e-04  2.872043e-03  3.062107e-03  6.131588e-04  5.060544e-03
#>  [96] -2.751722e-03  7.560829e-04 -5.968460e-03  2.358884e-03 -4.944267e-03
bayes_r_weights(y = y, X = X, Z = Z)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>   [1] -3.200076e-05 -1.462838e-04  2.993759e-01  2.933408e-05 -8.965132e-04
#>   [6] -8.036866e-06 -6.326498e-06  1.077608e-05 -7.625191e-05  7.963982e-05
#>  [11] -1.866826e-05  2.929359e-01  2.535392e-06  8.050913e-05 -2.776364e-04
#>  [16] -2.318144e-05  2.832741e-01 -2.355941e-04  1.025734e-05 -1.357228e-05
#>  [21]  5.733616e-04  3.037351e-01  2.142975e-05 -1.491273e-05  3.762888e-05
#>  [26] -2.069220e-05  2.967140e-01  5.464633e-06 -8.831745e-06  1.439801e-04
#>  [31]  6.598994e-06  3.314401e-05  2.596086e-05 -1.196077e-05  1.744930e-04
#>  [36]  1.647561e-05  4.827182e-05  2.878674e-01  1.136380e-05  7.685536e-05
#>  [41] -1.364160e-05 -1.978383e-05 -7.891388e-06 -3.006452e-04 -2.547552e-05
#>  [46] -1.482003e-05  3.038890e-01 -1.898770e-05  5.113387e-06 -3.544771e-04
#>  [51] -1.892118e-05 -2.344045e-05  7.559471e-06  1.166761e-04  2.876824e-01
#>  [56]  2.426979e-05 -6.749359e-05 -3.147051e-05 -1.043045e-05  1.198814e-05
#>  [61]  7.169793e-05  6.028511e-06  4.188250e-05 -3.301644e-06  3.453106e-05
#>  [66]  3.568293e-04 -3.603410e-05  6.479024e-05 -4.236380e-05  2.396146e-05
#>  [71]  3.151516e-04 -1.204201e-05 -9.200718e-05 -9.534879e-05 -5.406992e-05
#>  [76]  3.030098e-05  1.214784e-05  2.906695e-05  3.084039e-01  2.890246e-01
#>  [81]  9.935783e-04 -4.505704e-05 -1.792459e-05  5.793361e-05  3.811819e-07
#>  [86] -9.963346e-06 -3.154156e-05 -6.175308e-06  5.089690e-06  5.721102e-04
#>  [91]  1.115238e-05  1.307491e-05 -3.565667e-06 -3.407022e-06  3.547867e-05
#>  [96] -4.255496e-05  1.412371e-05 -3.892123e-05  4.572526e-06 -6.256694e-05