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 3.4.3 (2017-11-30)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS High Sierra 10.13.2

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_2.2-2        Rmosek_8.0.69     PolynomF_1.0-1   
 [4] CVXR_0.94-4       REBayes_1.2       Matrix_1.2-12    
 [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] gmp_0.5-13.1      Rcpp_0.12.14      compiler_3.4.3   
 [4] git2r_0.20.0      R.methodsS3_1.7.1 R.utils_2.6.0    
 [7] iterators_1.0.9   tools_3.4.3       digest_0.6.13    
[10] bit_1.1-12        evaluate_0.10.1   lattice_0.20-35  
[13] foreach_1.4.4     parallel_3.4.3    yaml_2.1.16      
[16] Rmpfr_0.6-1       ECOSolveR_0.3-2   stringr_1.2.0    
[19] knitr_1.17        rprojroot_1.3-1   bit64_0.9-7      
[22] grid_3.4.3        R6_2.2.2          rmarkdown_1.8    
[25] magrittr_1.5      MASS_7.3-47       backports_1.1.2  
[28] codetools_0.2-15  htmltools_0.3.6   scs_1.1-1        
[31] stringi_1.1.6     pscl_1.5.2        doParallel_1.0.11
[34] truncnorm_1.0-7   R.oo_1.21.0