Last updated: 2018-08-05

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    File Version Author Date Message
    Rmd 28251a2 Jason Willwerscheid 2018-08-05 wflow_publish(“analysis/MASHvFLASHrandom.Rmd”)
    html 9d60b52 Jason Willwerscheid 2018-07-31 Build site.
    Rmd 919319a Jason Willwerscheid 2018-07-31 wflow_publish(“analysis/MASHvFLASHrandom.Rmd”)

Introduction

I repeat the previous experiment, this time using 20000 random tests from the GTEx dataset. An important difference from the “strong” dataset is that many more of these tests will be null.

Results

I again pre-run the experiments and load the results from file.

fl_diag <- readRDS("./output/MASHvFLASHrandom/fldiag.rds")
fl_t <- readRDS("./output/MASHvFLASHrandom/flt.rds")
m_diag <- readRDS("./output/MASHvFLASHrandom/mdiag.rds")
m_t <- readRDS("./output/MASHvFLASHrandom/mt.rds")

Comparisons of mean-squared error and confidence interval coverage are as follows. FLASH again outperforms MASH, but not quite so dramatically as it did on the “strong” tests.

fl_fit_names <- c("pn.zero", "pn.OHF", "ash.zero", "ash.OHF")
method.names <- c(fl_fit_names, "mash")

all_t <- rbind(sapply(fl_t, unlist), unlist(m_t))
rownames(all_t) <- method.names

fl_mse <- sapply(fl_diag, function(x) {sapply(x, function(y) {y$mse})})
all_mse <- rbind(fl_mse, sapply(m_diag, function(x) {x$mse}))
rownames(all_mse) <- method.names

fl_ci <- sapply(fl_diag, function(x) {sapply(x, function(y) {y$ci})})
all_ci <- rbind(fl_ci, sapply(m_diag, function(x) {x$ci}))
rownames(all_ci) <- method.names

plot.order <- c(2, 4, 1, 3, 5)

boxplot(t(all_mse[plot.order, ]), ylim=c(0, 1), ylab = "MSE",
        main = "Mean-squared error")

Expand here to see past versions of res1-1.png:
Version Author Date
9d60b52 Jason Willwerscheid 2018-07-31 

boxplot(t(all_ci[plot.order, ]), ylim=c(0, 1), ylab = "coverage",
        main = "Proportion of 95% CIs containing true value")

Expand here to see past versions of res1-2.png:
Version Author Date
9d60b52 Jason Willwerscheid 2018-07-31

The total time required by each method is as follows.

boxplot(t(all_t[plot.order, ]), ylim=c(0, 60), ylab="minutes",
        main="Time needed for fit and MSE and CI calculations")

Expand here to see past versions of res2-1.png:
Version Author Date
9d60b52 Jason Willwerscheid 2018-07-31

Conclusions

The “OHF” method again gives the best results, but not as convincingly as it did when fitting the “strong” dataset. And point-normal priors again give results that are only slightly worse than ash priors, but take about half of the time to fit.

Code

The code is the same as in the previous experiment (to reproduce the above results, uncomment the commented-out lines).

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 10.13.6

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     

loaded via a namespace (and not attached):
 [1] workflowr_1.0.1   Rcpp_0.12.17      digest_0.6.15    
 [4] rprojroot_1.3-2   R.methodsS3_1.7.1 backports_1.1.2  
 [7] git2r_0.21.0      magrittr_1.5      evaluate_0.10.1  
[10] stringi_1.1.6     whisker_0.3-2     R.oo_1.21.0      
[13] R.utils_2.6.0     rmarkdown_1.8     tools_3.4.3      
[16] stringr_1.3.0     yaml_2.1.17       compiler_3.4.3   
[19] htmltools_0.3.6   knitr_1.20

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