Computes an empirical estimate of the proportion of non-zero effects
(sparsity) from the mr.ash fit. mr.ash fits a mixture model with a
point mass at zero (spike) plus continuous components (slab), and
learns the mixture proportions via variational EM. The sparsity
estimate 1 - pi[1] is the empirical Bayes estimate of the
non-null proportion, which can be used as a data-driven prior for
the inclusion probability parameters (pi for bayesC,
probIn for BayesB) of spike-and-slab Bayesian methods.
Arguments
- weight_results
Named list of weight vectors or matrices as returned by
twas_weights. The mr.ash element should have a"fit"attribute containing the model fit object (setretain_fits = TRUEintwas_weightsto obtain this).