Fits a Bayesian LASSO linear regression model via `BGLR::BGLR` (the "BL"
model, Park & Casella 2008) and returns the posterior mean of the marker
effects. This is the same "B-Lasso" implementation benchmarked in Kim et
al. (2022). Note that this is distinct from `bayes_l_weights`, which uses a
different Bayesian LASSO implementation backed by `qgg`.
Usage
b_lasso_weights(X, y, nIter = 10000, burnIn = 2000, thin = 5, ...)
Arguments
- X
A numeric matrix of predictors.
- y
A numeric response vector.
- nIter
Number of MCMC iterations. Default is 10000.
- burnIn
Number of burn-in iterations. Default is 2000.
- thin
Thinning interval. Default is 5.
- ...
Additional arguments passed through to `BGLR::BGLR`.
Value
A numeric vector of length `ncol(X)` of variant weights.
Details
Defaults for `nIter`, `burnIn`, and `thin` are larger than BGLR's package
defaults to better accommodate high-LD cis-eQTL windows; override to
recover the package defaults.