run susie_rss storing some additional information for coloc
runsusie(
d,
suffix = 1,
p = NULL,
trimz = NULL,
r2.prune = NULL,
maxit = 100,
repeat_until_convergence = TRUE,
s_init = NULL,
...
)coloc dataset, must include LD (signed correlation matrix)
suffix label that will be printed with any error messages
prior probability a snp is causal (equivalent to p1 or p2 in coloc.abf). By default, this is set to NULL, upon which we will set a small null_weight to pass to susie_rss() (see vignette a06 for details why). You can override this by setting p as you would p1 or p2 in a coloc function, but note that you may miss some true signals that way. Also note that neither of these options correspond to the susie_rss() defaults, because our goal here is not fine mapping alone.
used to trim datasets for development purposes
sometimes SuSiE can return multiple signals in high LD. if you set r2.prune to a value between 0 and 1, sets with index SNPs with LD greater than r2.prune
maximum number of iterations for the first run of susie_rss(). If susie_rss() does not report convergence, runs will be extended assuming repeat_until_convergence=TRUE. Most users will not need to change this default.
keep running until susie_rss() indicates convergence. Default TRUE. If FALSE, susie_rss() will run with maxit iterations, and if not converged, runsusie() will error. Most users will not need to change this default.
used internally to extend runs that haven't converged. don't use.
arguments passed to susie_rss. In particular, if you want to match some coloc defaults, set
prior_variance=0.2^2 (if a case-control trait) or (0.15/sd(Y))^2 if a quantitative trait
estimate_prior_variance=FALSE
otherwise susie_rss will estimate the prior variance itself
results of a susie_rss run, with some added dimnames
# NOT RUN {
library(coloc)
data(coloc_test_data)
result=runsusie(coloc_test_data$D1)
summary(result)
# }
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