Last updated: 2019-01-17

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: 12f4313

    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:    .Rhistory
    Ignored:    .Rproj.user/
    Ignored:    .sos/
    Ignored:    data/.DS_Store

Untracked files:
    Untracked:  .gitattributes
    Untracked:  data/sim_gaussian_475.rds
    Untracked:  data/sim_gaussian_75.rds
    Untracked:  output/dscout_gaussian_init.rds
    Untracked:  output/dscout_gaussian_null.rds
    Untracked:  output/dscout_gaussian_z.rds

Unstaged changes:
    Modified:   README.md
    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 12f4313 zouyuxin 2019-01-17 wflow_publish(“analysis/SusieZConverge.Rmd”)
    html 09d6448 zouyuxin 2019-01-17 Build site.
    Rmd e515634 zouyuxin 2019-01-17 wflow_publish(“analysis/SusieZConverge.Rmd”)

Simulation under null

dscout_null = readRDS('output/dscout_gaussian_null.rds')
dscout_null = dscout_null[!is.na(dscout_null$sim_gaussian_null.output.file),]
dscout_null = dscout_null[!is.na(dscout_null$susie_z.output.file),]

The maximum number of iteration is 7. All models converge. There is no false discovry.

Simulation with signals

dscout_gaussian_z = readRDS('output/dscout_gaussian_z.rds')
dscout_gaussian_z = dscout_gaussian_z[!is.na(dscout_gaussian_z$sim_gaussian.output.file),]
dscout_gaussian_z = dscout_gaussian_z[!is.na(dscout_gaussian_z$susie_z.output.file),]
dscout_gaussian_z$NotConverge = dscout_gaussian_z$susie_z.niter == 100

L = 5, effect number = 1

dscout_gaussian_L5 = dscout_gaussian_z[dscout_gaussian_z$susie_z.L == '5',]
dscout_gaussian_L5_E1 = dscout_gaussian_L5[dscout_gaussian_L5$sim_gaussian.effect_num == '1',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L5_E1, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           4
6 0.80           9
7 0.95           6

L = 5, effect number = 2

dscout_gaussian_L5_E2 = dscout_gaussian_L5[dscout_gaussian_L5$sim_gaussian.effect_num == '2',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L5_E2, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           3
6 0.80           7
7 0.95           8

L = 5, effect number = 5

dscout_gaussian_L5_E5 = dscout_gaussian_L5[dscout_gaussian_L5$sim_gaussian.effect_num == '5',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L5_E5, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           1
6 0.80           5
7 0.95           5

L = 5, effect number = 10

dscout_gaussian_L5_E10 = dscout_gaussian_L5[dscout_gaussian_L5$sim_gaussian.effect_num == '10',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L5_E10, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           1
7 0.95           3

L = 5, effect number = 20

dscout_gaussian_L5_E20 = dscout_gaussian_L5[dscout_gaussian_L5$sim_gaussian.effect_num == '20',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L5_E20, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           0
7 0.95           0

L = 20, effect number = 1

dscout_gaussian_L20 = dscout_gaussian_z[dscout_gaussian_z$susie_z.L == '20',]
dscout_gaussian_L20_E1 = dscout_gaussian_L20[dscout_gaussian_L20$sim_gaussian.effect_num == '1',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L20_E1, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           4
6 0.80          19
7 0.95          17

L = 20, effect number = 2

dscout_gaussian_L20_E2 = dscout_gaussian_L20[dscout_gaussian_L20$sim_gaussian.effect_num == '2',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L20_E2, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           3
6 0.80          15
7 0.95          18

L = 20, effect number = 5

dscout_gaussian_L20_E5 = dscout_gaussian_L20[dscout_gaussian_L20$sim_gaussian.effect_num == '5',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L20_E5, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           1
6 0.80           9
7 0.95          11

L = 20, effect number = 10

dscout_gaussian_L20_E10 = dscout_gaussian_L20[dscout_gaussian_L20$sim_gaussian.effect_num == '10',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L20_E10, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           2
7 0.95           6

L = 20, effect number = 20

dscout_gaussian_L20_E20 = dscout_gaussian_L20[dscout_gaussian_L20$sim_gaussian.effect_num == '20',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_L20_E20, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           1
7 0.95           2

Simulation initialize at true

dscout_gaussian_init = readRDS('output/dscout_gaussian_init.rds')
dscout_gaussian_init = dscout_gaussian_init[!is.na(dscout_gaussian_init$sim_gaussian.output.file),]
dscout_gaussian_init = dscout_gaussian_init[!is.na(dscout_gaussian_init$susie_z_init.output.file),]
dscout_gaussian_init$NotConverge = dscout_gaussian_init$susie_z_init.niter == 100

effect number = 1

dscout_gaussian_E1 = dscout_gaussian_init[dscout_gaussian_init$sim_gaussian.effect_num == '1',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_E1, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           0
7 0.95           0

effect number = 2

dscout_gaussian_E2 = dscout_gaussian_init[dscout_gaussian_init$sim_gaussian.effect_num == '2',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_E2, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           0
7 0.95           0

effect number = 5

dscout_gaussian_E5 = dscout_gaussian_init[dscout_gaussian_init$sim_gaussian.effect_num == '5',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_E5, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           1
6 0.80           5
7 0.95           5

effect number = 10

dscout_gaussian_E10 = dscout_gaussian_init[dscout_gaussian_init$sim_gaussian.effect_num == '10',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_E10, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           4
7 0.95           6

effect number = 20

dscout_gaussian_E20 = dscout_gaussian_init[dscout_gaussian_init$sim_gaussian.effect_num == '20',]
converge.summary = aggregate(NotConverge ~ sim_gaussian.pve, dscout_gaussian_E20, sum)
colnames(converge.summary) = c('pve', 'NotConverge')
converge.summary
   pve NotConverge
1 0.01           0
2 0.05           0
3 0.10           0
4 0.20           0
5 0.50           0
6 0.80           1
7 0.95           2

Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.2

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.1.1   Rcpp_1.0.0        digest_0.6.18    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.3  
 [7] git2r_0.24.0      magrittr_1.5      evaluate_0.12    
[10] stringi_1.2.4     whisker_0.3-2     R.oo_1.22.0      
[13] R.utils_2.7.0     rmarkdown_1.11    tools_3.5.1      
[16] stringr_1.3.1     yaml_2.2.0        compiler_3.5.1   
[19] htmltools_0.3.6   knitr_1.20

This reproducible R Markdown analysis was created with workflowr 1.1.1