Last updated: 2018-05-15

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Expand here to see past versions:
    File Version Author Date Message
    html e05bc83 LSun 2018-05-12 Update to 1.0
    rmd cc0ab83 Lei Sun 2018-05-11 update
    html 0f36d99 LSun 2017-12-21 Build site.
    html 6c9268e LSun 2017-11-22 Build site.
    rmd 1a77d56 LSun 2017-11-22 wflow_publish(c(“analysis/cash_sim_1.rmd”, “analysis/cash_sim_2.rmd”, “analysis/cash_sim_3.rmd”,
    html cd38d36 LSun 2017-11-17 Build site.
    rmd b452d8a LSun 2017-11-17 more simulations
    html 3328cdb LSun 2017-11-15 Build site.
    html 277696b LSun 2017-11-14 Build site.
    rmd db1eccb LSun 2017-11-14 wflow_publish(“analysis/cash_sim_4.rmd”)

Using smaller, faster, exploratory simulations

Instead of doing a simulation with \(1K\) runs, each run with \(10K\) samples, we are downsizing it so that simulations can be done faster while still show a pattern. Right now, \(100\) runs, each run with \(5K\) samples seem adequate.

We don’t want the sample size to be too small. Small sample sizes appear to work less well with Gaussian derivatives fitting.

We are experimenting with different effect size distributions, and see how the average pFDP look with all data sets across all nominal \(q\) value levels.

Frequentist pFDR accuracy with all data sets

source("../code/gdash_lik.R")
z.mat = readRDS("../output/z_null_liver_777.rds")
se.mat = readRDS("../output/sebetahat_null_liver_777.rds")

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.8, 0.2), g.sd = c(0, 1), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.8, 0.2), g.sd = c(0, 2), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.8, 0.2), g.sd = c(0, 1), relative_to_noise = FALSE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.8, 0.1, 0.1), g.sd = c(0, 1, 2), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.8, 0.1, 0.1), g.sd = c(0, 1, 2), relative_to_noise = FALSE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.9, 0.1), g.sd = c(0, 1), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.9, 0.1), g.sd = c(0, 2), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.9, 0.1), g.sd = c(0, 1), relative_to_noise = FALSE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.9, 0.05, 0.05), g.sd = c(0, 1, 2), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.9, 0.05, 0.05), g.sd = c(0, 1, 2), relative_to_noise = FALSE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.85, 0.10, 0.05), g.sd = c(0, 1, 2), relative_to_noise = TRUE)

Simulation

cashSim(z.mat, se.mat,
        nsim = 100, ngene = 1000,
        g.pi = c(0.85, 0.10, 0.05), g.sd = c(0, 1, 2), relative_to_noise = FALSE)

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 10.13.4

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    CVXR_0.95        
[5] REBayes_1.2       Matrix_1.2-12     SQUAREM_2017.10-1 EQL_1.0-0        
[9] ttutils_1.0-1    

loaded via a namespace (and not attached):
 [1] gmp_0.5-13.1      Rcpp_0.12.16      compiler_3.4.3   
 [4] git2r_0.21.0      workflowr_1.0.1   R.methodsS3_1.7.1
 [7] R.utils_2.6.0     iterators_1.0.9   tools_3.4.3      
[10] digest_0.6.15     bit_1.1-12        evaluate_0.10.1  
[13] lattice_0.20-35   foreach_1.4.4     yaml_2.1.18      
[16] parallel_3.4.3    Rmpfr_0.6-1       ECOSolveR_0.4    
[19] stringr_1.3.0     knitr_1.20        rprojroot_1.3-2  
[22] bit64_0.9-7       grid_3.4.3        R6_2.2.2         
[25] rmarkdown_1.9     magrittr_1.5      whisker_0.3-2    
[28] MASS_7.3-47       backports_1.1.2   codetools_0.2-15 
[31] htmltools_0.3.6   scs_1.1-1         stringi_1.1.6    
[34] pscl_1.5.2        doParallel_1.0.11 truncnorm_1.0-7  
[37] R.oo_1.21.0

This reproducible R Markdown analysis was created with workflowr 1.0.1