CASH on Poisson thinning data by seqgendiff::poisthin

Introduction

In order to perform a large-scale simulation, we explore the seqgendiff::poisthin function developed by David.

library(seqgendiff)
source("../code/count_to_summary.R")
source("../code/gdash_lik.R")

counts <- readRDS("../data/liver.sim.rds")

More flexible effects

Instead of \(g = \pi_0\delta_0 + (1 - \pi_0)N(0, \sigma^2)\), we would like to add effects following \(g = \pi_0\delta_0 + \pi_1N(0, \sigma_1^2) + \pi_2N(0, \sigma_2^2)\).

The data is simulated by Poisson thinning, not by normal mean models.

Session information

sessionInfo()

R version 3.4.2 (2017-09-28)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.1

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] ashr_2.1-27       Rmosek_7.1.3      PolynomF_0.94    
 [4] cvxr_0.0.0.9009   REBayes_0.85      Matrix_1.2-11    
 [7] SQUAREM_2017.10-1 EQL_1.0-0         ttutils_1.0-1    
[10] seqgendiff_0.1.0 

loaded via a namespace (and not attached):
 [1] Rcpp_0.12.13      knitr_1.17        magrittr_1.5     
 [4] MASS_7.3-47       pscl_1.5.2        doParallel_1.0.11
 [7] lattice_0.20-35   foreach_1.4.3     stringr_1.2.0    
[10] tools_3.4.2       parallel_3.4.2    grid_3.4.2       
[13] git2r_0.19.0      iterators_1.0.8   htmltools_0.3.6  
[16] yaml_2.1.14       rprojroot_1.2     digest_0.6.12    
[19] codetools_0.2-15  evaluate_0.10.1   rmarkdown_1.6    
[22] stringi_1.1.5     compiler_3.4.2    backports_1.1.1  
[25] truncnorm_1.0-7