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## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(echo = TRUE,comment = "#",fig.width = 5,
fig.height = 4,fig.align = "center",
eval = FALSE)
## -----------------------------------------------------------------------------
# library(ashr)
# library(mashr)
# set.seed(1)
# simdata = simple_sims2(1000,1)
# true.U1 = cbind(c(1,1,0,0,0),c(1,1,0,0,0),rep(0,5),rep(0,5),rep(0,5))
# true.U2 = cbind(rep(0,5),rep(0,5),c(0,0,1,1,1),c(0,0,1,1,1),c(0,0,1,1,1))
# U.true = list(true.U1 = true.U1, true.U2 = true.U2)
## ---- collapse = TRUE---------------------------------------------------------
# data = mash_set_data(simdata$Bhat, simdata$Shat)
# m.1by1 = mash_1by1(data)
# strong = get_significant_results(m.1by1)
# U.c = cov_canonical(data)
# U.pca = cov_pca(data,5,strong)
# U.ed = cov_ed(data,U.pca,strong)
#
# # Computes covariance matrices based on extreme deconvolution,
# # initialized from PCA.
# m.c = mash(data, U.c)
# m.ed = mash(data, U.ed)
# m.c.ed = mash(data, c(U.c,U.ed))
# m.true = mash(data, U.true)
#
# print(get_loglik(m.c),digits = 10)
# print(get_loglik(m.ed),digits = 10)
# print(get_loglik(m.c.ed),digits = 10)
# print(get_loglik(m.true),digits = 10)
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