Last updated: 2018-03-05

Code version: 373cde0

We simulate dataset with N=400, R = 10 and 50.

library(mashr)

Loading required package: ashr

set.seed(2018)
data.10 = simple_sims(100, ncond = 10, err_sd = 0.1)

data.50 = simple_sims(100, ncond = 50, err_sd = 0.1)

mash.data.10 = mash_set_data(Bhat = data.10$Bhat, Shat = data.10$Shat)
mash.data.10.1 = mash_set_data(Bhat = data.10$Bhat, Shat = data.10$Shat, alpha=1)

mash.data.50 = mash_set_data(Bhat = data.50$Bhat, Shat = data.50$Shat)
mash.data.50.1 = mash_set_data(Bhat = data.50$Bhat, Shat = data.50$Shat, alpha=1)

U.c.10 = cov_canonical(mash.data.10)
U.c.50 = cov_canonical(mash.data.50)

mash.10 = mash(mash.data.10, U.c.10, verbose = FALSE)

Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
               consider reducing rtol

Warning in mixIP(matrix_lik = structure(c(0.825501423556077,
0.0919617211603192, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.

mash.50 = mash(mash.data.50, U.c.50, verbose = FALSE)

Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
               consider reducing rtol

Warning in mixIP(matrix_lik = structure(c(0.0183400395288875,
0.0241762811815978, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.

par(mfcol=c(2,1))
barplot(get_estimated_pi(mash.10), las=2, cex.names = 0.7, main='R=10, n = 400')
barplot(get_estimated_pi(mash.50), las=2, cex.names = 0.7, main='R=50, n = 400')

par(mfcol=c(1,1))

Separate the conditions to subgroups:

mash.data.50.sub1 = mash_set_data(data.50$Bhat[,1:25], data.50$Shat[,1:25])
U.c.50.sub1 = cov_canonical(mash.data.50.sub1)
mash.50.sub1 = mash(mash.data.50.sub1, U.c.50.sub1, verbose = FALSE)

Warning in REBayes::KWDual(A, rep(1, k), normalize(w), control = control): estimated mixing distribution has some negative values:
               consider reducing rtol

Warning in mixIP(matrix_lik = structure(c(0.0183400395288872,
0.210931087030779, : Optimization step yields mixture weights that are
either too small, or negative; weights have been corrected and renormalized
after the optimization.

barplot(get_estimated_pi(mash.50.sub1), las=2, cex.names = 0.7)

sessionInfo()

R version 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.4/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] mashr_0.2-6 ashr_2.2-7 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.15      knitr_1.20        magrittr_1.5     
 [4] REBayes_1.2       MASS_7.3-47       doParallel_1.0.11
 [7] pscl_1.5.2        SQUAREM_2017.10-1 lattice_0.20-35  
[10] foreach_1.4.4     plyr_1.8.4        stringr_1.3.0    
[13] tools_3.4.3       parallel_3.4.3    grid_3.4.3       
[16] rmeta_2.16        git2r_0.20.0      htmltools_0.3.6  
[19] iterators_1.0.9   assertthat_0.2.0  yaml_2.1.17      
[22] rprojroot_1.2     digest_0.6.13     Matrix_1.2-12    
[25] codetools_0.2-15  evaluate_0.10.1   rmarkdown_1.8    
[28] stringi_1.1.6     compiler_3.4.3    Rmosek_8.0.69    
[31] backports_1.1.2   mvtnorm_1.0-7     truncnorm_1.0-8  

This R Markdown site was created with workflowr