Last updated: 2019-03-12

workflowr checks: (Click a bullet for more information)

  • ✔ R Markdown file: up-to-date

    Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

  • ✔ Environment: empty

    Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

  • ✔ Seed: set.seed(20190115)

    The command set.seed(20190115) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

  • ✔ Session information: recorded

    Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

  • ✔ Repository version: 8437998

    Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility. The version displayed above was the version of the Git repository at the time these results were generated.

    Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 
    
Ignored files:
    Ignored:    .DS_Store
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .sos/
    Ignored:    data/.DS_Store
    Ignored:    output/.DS_Store

Untracked files:
    Untracked:  analysis/SuSiERobustNum.Rmd
    Untracked:  data/random_data_31.rds
    Untracked:  data/random_data_31_sim_gaussian_35.rds
    Untracked:  data/random_data_31_sim_gaussian_35_get_sumstats_1.rds
    Untracked:  data/small_data_46.rds
    Untracked:  data/small_data_46_sim_gaussian_10.rds
    Untracked:  data/small_data_46_sim_gaussian_10_get_sumstats_2.rds
    Untracked:  docs/figure/test.Rmd/
    Untracked:  figure/
    Untracked:  output/dscoutProblem475.rds
    Untracked:  output/dscoutProblem75.rds
    Untracked:  output/finemap_compare_random_data_null_dscout.rds
    Untracked:  output/finemap_compare_random_data_signal_dscout.rds
    Untracked:  output/finemap_compare_small_data_signal_dscout.rds
    Untracked:  output/finemap_compare_small_data_signal_dscout_RE8.rds
    Untracked:  output/random_data_100_sim_gaussian_null_1_get_sumstats_1_finemap_1.rds
    Untracked:  output/random_data_31_35_fit_em.rds
    Untracked:  output/random_data_76.rds
    Untracked:  output/random_data_76_sim_gaussian_8.rds
    Untracked:  output/random_data_76_sim_gaussian_8_get_sumstats_1.rds
    Untracked:  output/small_data_42_sim_gaussian_36_get_sumstats_2_susie_z_2.rds
    Untracked:  output/small_data_92_sim_gaussian_30_get_sumstats_2_susie_z_2.rds

Unstaged changes:
    Modified:   analysis/Problem475.Rmd
    Modified:   analysis/SuSiEDAP_Power_data31_35.Rmd
    Modified:   analysis/SusieZPerformance.Rmd
    Modified:   analysis/SusieZPerformanceRE3.Rmd
    Modified:   output/dsc_susie_z_v_output.rds
    Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.
Expand here to see past versions:
    File Version Author Date Message
    Rmd 8437998 zouyuxin 2019-03-12 wflow_publish(“analysis/DontStopProblem.Rmd”) 

library(susieR)
library(R.utils)
sourceDirectory("~/Documents/GitHub/susieR/inst/code/susiez_num/")

Data: SuSiE vs DAP: data 31_35 (lower power)

X = readRDS('data/random_data_31.rds')$X
R = cor(X)
data = readRDS('data/random_data_31_sim_gaussian_35.rds')
y = data$Y
beta = data$meta$true_coef
sumstats = readRDS('data/random_data_31_sim_gaussian_35_get_sumstats_1.rds')
zscores = sumstats$sumstats$bhat/sumstats$sumstats$shat
plot(zscores, pch=16, main='z scores')
pos = 1:length(zscores)
points(pos[beta!=0],zscores[beta!=0],col=2,pch=16)

susie_plot(zscores, y = "z", b = beta, main='p values from z scores')

which(beta!=0)
[1] 424 427 523 941 950
fit_1 = susie_z_general_num(zscores, R, lambda = 1e-6, track_fit = TRUE, verbose = TRUE, max_iter = 100, estimate_prior_method = 'EM')
fit_1 = readRDS('output/random_data_31_35_fit_em.rds')

The algorithm fails to stop.

The objective is -1790.4412135.

susie_plot(fit_1, y='PIP', b=beta)

The estimated prior variance at last 10 iterations are

for(i in 90:length(fit_1$trace)){
  print(fit_1$trace[[i]]$V)
}
 [1] 1290.44535    0.00000   42.28013  203.79406   37.97423    0.00000
 [7]   31.53090   24.34893    0.00000    0.00000
 [1] 1293.43575    0.00000   42.28013  202.60485   37.97431    0.00000
 [7]   31.53091   24.34895    0.00000    0.00000
 [1] 1296.44627    0.00000   42.28012  201.41253   37.97438    0.00000
 [7]   31.53092   24.34897    0.00000    0.00000
 [1] 1299.47711    0.00000   42.28012  200.21709   37.97446    0.00000
 [7]   31.53093   24.34898    0.00000    0.00000
 [1] 1302.52853    0.00000   42.28012  199.01852   37.97454    0.00000
 [7]   31.53094   24.34900    0.00000    0.00000
 [1] 1305.60074    0.00000   42.28012  197.81680   37.97462    0.00000
 [7]   31.53095   24.34902    0.00000    0.00000
 [1] 1308.69400    0.00000   42.28012  196.61194   37.97470    0.00000
 [7]   31.53096   24.34904    0.00000    0.00000
 [1] 1311.80855    0.00000   42.28012  195.40392   37.97478    0.00000
 [7]   31.53097   24.34906    0.00000    0.00000
 [1] 1314.94464    0.00000   42.28012  194.19273   37.97486    0.00000
 [7]   31.53098   24.34907    0.00000    0.00000
 [1] 1318.10251    0.00000   42.28012  192.97836   37.97494    0.00000
 [7]   31.53099   24.34909    0.00000    0.00000
 [1] 1321.28244    0.00000   42.28012  191.76081   37.97503    0.00000
 [7]   31.53100   24.34911    0.00000    0.00000

Fit model with initial prior variance 50:

fit_2 = susie_z_general_num(zscores, R, lambda = 1e-6, track_fit = TRUE, verbose = TRUE, scaled_prior_variance = 50, estimate_prior_method = 'EM')
[1] "before estimate sigma2 objective:-1816.93777951728"
[1] "after estimate sigma2 objective:-1816.93777951728"
[1] "before estimate sigma2 objective:-1780.84227231863"
[1] "after estimate sigma2 objective:-1780.84227231863"
[1] "before estimate sigma2 objective:-1780.72942172036"
[1] "after estimate sigma2 objective:-1780.72942172036"
[1] "before estimate sigma2 objective:-1780.72794633462"
[1] "after estimate sigma2 objective:-1780.72794633462"
[1] "before estimate sigma2 objective:-1780.72793597231"
[1] "after estimate sigma2 objective:-1780.72793597231"

The algorithm stops. The objective is -1780.727936.

susie_plot(fit_2, y='PIP', b=beta)

The estimated prior variances are

fit_2$V
 [1] 2520.96470   42.28076   37.95474   31.52849   24.34387    0.00000
 [7]    0.00000    0.00000    0.00000    0.00000
fit_3 = susie_z_general_num(zscores, R, lambda = 1e-6, track_fit = TRUE, verbose = TRUE, s_init = fit_2, scaled_prior_variance = fit_2$V)
[1] "before estimate sigma2 objective:-1780.7279359051"
[1] "after estimate sigma2 objective:-1780.7279359051"
[1] "before estimate sigma2 objective:-1780.72793590511"
[1] "after estimate sigma2 objective:-1780.72793590511"
susie_plot(fit_3, y='PIP', b=beta)

Fit model using ‘optim’:

fit_4 = susie_z_general_num(zscores, R, lambda = 1e-6, track_fit = TRUE, verbose = TRUE, estimate_prior_method = 'optim')
[1] "before estimate sigma2 objective:-1786.68857739904"
[1] "after estimate sigma2 objective:-1786.68857739904"
[1] "before estimate sigma2 objective:-1781.50283517316"
[1] "after estimate sigma2 objective:-1781.50283517316"
[1] "before estimate sigma2 objective:-1780.72803928019"
[1] "after estimate sigma2 objective:-1780.72803928019"
[1] "before estimate sigma2 objective:-1780.72793598391"
[1] "after estimate sigma2 objective:-1780.72793598391"

The objective is -1780.727936.

susie_plot(fit_4, y='PIP', b=beta)

The estimated prior variances are

fit_4$V
 [1] 2520.99866   42.27946   37.95660   31.52854   24.34473    0.00000
 [7]    0.00000    0.00000    0.00000    0.00000

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.3

Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.5/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] R.utils_2.7.0     R.oo_1.22.0       R.methodsS3_1.7.1 susieR_0.7.1.0482

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1 Rcpp_1.0.0      lattice_0.20-38 digest_0.6.18  
 [5] rprojroot_1.3-2 grid_3.5.1      backports_1.1.3 git2r_0.24.0   
 [9] magrittr_1.5    evaluate_0.12   stringi_1.2.4   whisker_0.3-2  
[13] Matrix_1.2-15   rmarkdown_1.11  tools_3.5.1     stringr_1.3.1  
[17] yaml_2.2.0      compiler_3.5.1  htmltools_0.3.6 knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1