Perform "extreme deconvolution" (Bovy et al) on a subset of the data

cov_ed(data, Ulist_init, subset = NULL, algorithm = c("bovy", "teem"), ...)

Arguments

data: a mash data object
Ulist_init: a named list of covariance matrices to use to initialize ED; default is to use matrices from PCs
subset: a subset of data to be used when ED is run (set to NULL for all the data)
algorithm: algorithm to run ED
...: other arguments to be passed to ED algorith, see extreme_deconvolution for algorithm 'bovy', or teem_wrapper for algorithm 'teem'

Details

Runs the extreme deconvolution algorithm from Bovy et al (Annals of Applied Statistics) to estimate data-driven covariance matrices. It can be initialized with, for example running cov_pca with, say, 5 PCs.

Examples

if (FALSE) {
data = mash_set_data(Bhat = cbind(c(1,2),c(3,4)), Shat = cbind(c(1,1),c(1,1)))
U_pca = cov_pca(data,2)
U_x = apply(data$Bhat, 2, function(x) x - mean(x))
U_xx = t(U_x) %*% U_x / nrow(U_x)
cov_ed(data,c(U_pca, list(xx = U_xx)))
}