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] -1.096363e-02  5.758294e-03  2.889239e-01  2.787574e-01 -3.616785e-03
#>   [6] -2.149238e-03  5.069917e-05  4.299695e-03  2.891974e-01  3.184979e-03
#>  [11] -6.126197e-03 -8.352853e-03  7.844355e-03 -1.084885e-02  2.790876e-01
#>  [16]  6.645184e-03  2.759179e-03  5.272684e-03 -5.301353e-03 -6.879990e-03
#>  [21]  2.922038e-03  5.819544e-03  2.921278e-01 -6.796403e-03 -3.360767e-04
#>  [26] -4.381229e-03 -2.586472e-03 -3.075163e-03  3.985856e-03  9.380014e-03
#>  [31] -1.622411e-03  1.574482e-02  6.710009e-03  1.205327e-02 -1.930455e-03
#>  [36]  6.666151e-03 -3.914394e-03  3.074128e-01 -4.494695e-04  1.825351e-03
#>  [41] -1.063668e-02  6.270706e-03  9.336939e-03 -9.155290e-03 -7.011836e-03
#>  [46] -7.944085e-03  2.833926e-01 -5.489965e-03  1.420181e-02 -3.941899e-03
#>  [51]  1.019772e-02 -4.492743e-03  8.252737e-03  1.038600e-03  2.919847e-01
#>  [56] -9.493432e-03  1.032434e-03  3.351308e-04 -5.558894e-03 -4.632248e-03
#>  [61] -7.879059e-03  7.585499e-03 -6.591717e-03 -8.538366e-03  6.315108e-03
#>  [66] -4.838176e-03  3.929841e-03 -2.862968e-03 -9.869423e-03 -2.545328e-03
#>  [71] -6.529580e-03 -1.283008e-04 -1.822881e-03  1.490022e-02 -5.362045e-03
#>  [76]  3.758677e-03  5.958259e-03 -8.717455e-03 -1.553197e-03  2.732758e-01
#>  [81]  6.412870e-03  5.353829e-03  4.464575e-03  2.840676e-01 -1.662554e-03
#>  [86] -8.167802e-04  1.721870e-03 -1.077578e-02  4.018259e-03 -1.157716e-02
#>  [91] -1.767663e-03  2.566913e-04 -5.005165e-04 -3.682859e-03  3.837916e-03
#>  [96] -2.048878e-03 -7.611078e-04  1.112702e-03  3.270335e-03 -4.524886e-03
bayes_r_weights(y = y, X = X, Z = Z)
#> Type of analysis performed: st-blr-individual-level-dense-ld
#>   [1] -2.133117e-04  8.041089e-06  2.910704e-01  2.791783e-01 -9.005981e-06
#>   [6] -1.955018e-06 -8.223554e-06  5.343673e-05  2.895658e-01  2.658803e-05
#>  [11] -2.909829e-05 -6.713910e-05  7.071121e-05 -7.267854e-05  2.825448e-01
#>  [16]  4.329287e-05  7.151812e-06  3.704482e-05 -1.666896e-05 -5.796511e-05
#>  [21]  1.491559e-05  4.646542e-05  2.919641e-01 -5.090383e-05  1.089144e-05
#>  [26] -1.409469e-05 -1.575028e-05 -2.641406e-05  4.556251e-06  1.163525e-04
#>  [31]  1.244293e-05  2.467120e-04  6.918388e-05  1.027916e-04 -1.433565e-05
#>  [36]  3.111444e-05 -2.360649e-05  3.108984e-01 -1.263923e-05  5.715840e-06
#>  [41] -9.955726e-05  6.990488e-05  9.901282e-05 -6.606345e-05 -4.630917e-05
#>  [46] -5.066945e-05  2.880755e-01 -1.868433e-05  2.033527e-04 -2.529558e-05
#>  [51]  1.817598e-04 -2.214885e-05  4.959859e-05  5.979283e-06  2.951339e-01
#>  [56] -1.362898e-04  1.727142e-05 -2.886735e-05 -6.201760e-05 -2.377006e-05
#>  [61] -1.110116e-04  3.644615e-05 -3.326456e-05 -7.643881e-05  2.731110e-05
#>  [66] -1.836669e-05 -1.935590e-06 -2.801815e-05 -8.565644e-05 -1.229362e-05
#>  [71] -8.854565e-05  5.889313e-06 -3.189746e-05  6.687665e-04 -6.271759e-05
#>  [76]  5.510647e-05  5.666397e-05 -4.035325e-05 -2.934608e-05  2.772525e-01
#>  [81]  3.518970e-05  8.608009e-06  3.833463e-05  2.854068e-01 -6.898540e-06
#>  [86] -1.155280e-05 -2.738276e-06 -3.551246e-04  5.577990e-05 -5.431502e-05
#>  [91] -3.679925e-05 -1.993217e-06 -2.209038e-05 -9.125061e-06  3.526245e-05
#>  [96] -8.794858e-06 -1.797005e-05  6.282158e-06  2.952468e-05 -5.607243e-05