Last updated: 2019-10-09

Checks: 6 0

Knit directory: MMVBVS/

This reproducible R Markdown analysis was created with workflowr (version 1.2.0). The Report tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

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(20191007)

The command set.seed(20191007) 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.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

Repository version: 472e2ac

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:    src/.DS_Store
    Ignored:    src/MMVBVS.so
    Ignored:    src/mmvbvs.o

Untracked files:
    Untracked:  .Rbuildignore
    Untracked:  .Rprofile
    Untracked:  .gitignore
    Untracked:  code/
    Untracked:  data/
    Untracked:  output/

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.

These are the previous versions of the R Markdown and HTML files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view them.

File	Version	Author	Date	Message
Rmd	472e2ac	tk382	2019-10-09	wflow_publish(“analysis/Example.Rmd”, republish = TRUE)
html	055a3b5	tk382	2019-10-09	Build site.
html	4f4fc15	tk382	2019-10-08	Build site.
Rmd	21a1eba	tk382	2019-10-08	wflow_publish(“analysis/Example.Rmd”)
Rmd	a799689	tk382	2019-10-08	start example file

Load Package

library(MMVBVS)
library(ggplot2)

Create Data

beta = c(seq(0.1, 0.5, length=5), rep(0,5))
n = 500; T = length(beta); nu = T+5
Sigma = matrix(0.8, T, T); diag(Sigma) = 1
X = as.numeric(scale(rnorm(n)))
error = MASS::mvrnorm(n, rep(0,T), Sigma)
gamma = c(rep(1,5), rep(0,5))
Y = X %*% t(beta) + error; Y = scale(Y)
for (i in 1:10){
  Y[sample(1:400, 200), i] = NA
}

Run Algorithm

Phi = matrix(0.5, T, T); diag(Phi) = 1
initial_chain = list(beta = rep(0,T), 
                      gamma = rep(0,T), 
                      Sigma = Phi, 
                      sigmabeta = 1)
result = mmvbvs(X = X,
                Y = Y,
                initial_chain = initial_chain,
                Phi = Phi,
                marcor = colMeans(X*Y, na.rm=TRUE),
                sigmabeta = 1,
                niter=100,
                verbose = FALSE)

Analyze Result

Inclusion of each tissue by iterations

plot_gamma(result, title = "")

Version	Author	Date
4f4fc15	tk382	2019-10-08

Trajectory of \(\beta\) by iterations

plot_beta(result, title="")

Version	Author	Date
4f4fc15	tk382	2019-10-08

Posterior Distribution of the coefficients \(\beta\)

beta_dist(result, title="")

Version	Author	Date
4f4fc15	tk382	2019-10-08

Heatmap of Posterior Mean of \(\Sigma\)

plot_sigma(result, title="")

sessionInfo()

R version 3.5.3 (2019-03-11)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Sierra 10.12.6

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] ggplot2_3.1.0 MMVBVS_0.1.0 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.1       compiler_3.5.3   pillar_1.3.1     git2r_0.25.2    
 [5] plyr_1.8.4       workflowr_1.2.0  tools_3.5.3      digest_0.6.18   
 [9] evaluate_0.13    tibble_2.1.1     gtable_0.3.0     pkgconfig_2.0.2 
[13] rlang_0.3.3      yaml_2.2.0       xfun_0.5         withr_2.1.2     
[17] stringr_1.4.0    dplyr_0.8.0.1    knitr_1.22       fs_1.2.7        
[21] rprojroot_1.3-2  grid_3.5.3       tidyselect_0.2.5 reshape_0.8.8   
[25] glue_1.3.1       R6_2.4.0         rmarkdown_1.12   purrr_0.3.2     
[29] reshape2_1.4.3   magrittr_1.5     whisker_0.3-2    backports_1.1.3 
[33] scales_1.0.0     htmltools_0.3.6  MASS_7.3-51.3    assertthat_0.2.1
[37] colorspace_1.4-1 labeling_0.3     stringi_1.4.3    lazyeval_0.2.2  
[41] munsell_0.5.0    crayon_1.3.4

Simulation Example