Last updated: 2018-12-11

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I randomly generate 20 positive definite correlation matrices, V. The sample size is 4000.

\[ \hat{z}_j|z_j \sim N_{5}(z_j, V_j) \] \[ z_j \sim \frac{1}{2}\delta_{0} + \frac{1}{2}N(0, \left(\begin{matrix} 1 & 0_{1\times 4} \\ 0_{4\times 1} & 0_{4\times 4} \end{matrix}\right)) \]

library(ggplot2)
Summary = readRDS('../output/diff_v_signal/summary.rds')

Time

The total running time for each matrix is

Time = Summary[,c('DSC','estimate', 'estimate.DSC_TIME')]
ggplot(Time, aes(x = DSC, y=estimate.DSC_TIME, group = estimate, color = estimate)) + geom_line()

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mash log likelihood

loglike = Summary[Summary$summary == 'mashloglik', c('DSC','estimate', 'summary.score', 'summary')]
ggplot(loglike, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()

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loglike_ora = loglike[-which(loglike$estimate == 'oracle'), ]
ggplot(loglike_ora, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()

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RRMSE

RRMSE = Summary[Summary$summary == 'RRMSE', c('DSC','estimate', 'summary.score', 'summary')]
ggplot(RRMSE, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()

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RRMSE_ora = RRMSE[-which(RRMSE$estimate == 'oracle'), ]
ggplot(RRMSE_ora, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()

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ROC

ROCdir = readRDS('../output/diff_v_signal/ROCdir.rds')
par(mfrow=c(1,2))
for(i in 1:20){
  ind = which(ROCdir$DSC == i)
  ROC.seq = readRDS(paste("../output/diff_v_signal/", ROCdir$ROC.output.file[ind[1]], '.rds', sep=""))
  plot(ROC.seq$data[,'FPR'], ROC.seq$data[,'TPR'], type='l', xlab = 'FPR', ylab='TPR',
       main=paste0('Data ', i, ' True Pos vs False Pos'), cex=1.5, lwd = 1.5, col = 'cyan')
  
  ROC.seq = readRDS(paste("../output/diff_v_signal/", ROCdir$ROC.output.file[ind[2]], '.rds', sep=""))
  lines(ROC.seq$data[,'FPR'], ROC.seq$data[,'TPR'], col='olivedrab', lwd = 1.5)
  
  ROC.seq = readRDS(paste("../output/diff_v_signal/", ROCdir$ROC.output.file[ind[3]], '.rds', sep=""))
  lines(ROC.seq$data[,'FPR'], ROC.seq$data[,'TPR'], col='purple', lwd = 1.5)
  ROC.seq = readRDS(paste("../output/diff_v_signal/", ROCdir$ROC.output.file[ind[4]], '.rds', sep=""))
  lines(ROC.seq$data[,'FPR'], ROC.seq$data[,'TPR'], col='red', lwd = 1.5)
  ROC.seq = readRDS(paste("../output/diff_v_signal/", ROCdir$ROC.output.file[ind[5]], '.rds', sep=""))
  lines(ROC.seq$data[,'FPR'], ROC.seq$data[,'TPR'], col='chartreuse3', lwd = 1.5)
  
  legend('bottomright', c('oracle','identity','simple', 'current', 'mle'),col=c('cyan','olivedrab','purple','red','chartreuse3'),
           lty=c(1,1,1,1,1), lwd=c(1.5,1.5,1.5,1.5,1.5))
}

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Session information

sessionInfo()
R version 3.5.1 (2018-07-02)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS  10.14.1

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

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.0        compiler_3.5.1    pillar_1.3.0     
 [4] git2r_0.23.0      plyr_1.8.4        workflowr_1.1.1  
 [7] bindr_0.1.1       R.methodsS3_1.7.1 R.utils_2.7.0    
[10] tools_3.5.1       digest_0.6.18     evaluate_0.12    
[13] tibble_1.4.2      gtable_0.2.0      pkgconfig_2.0.2  
[16] rlang_0.3.0.1     yaml_2.2.0        bindrcpp_0.2.2   
[19] withr_2.1.2       stringr_1.3.1     dplyr_0.7.6      
[22] knitr_1.20        rprojroot_1.3-2   grid_3.5.1       
[25] tidyselect_0.2.5  glue_1.3.0        R6_2.3.0         
[28] rmarkdown_1.10    purrr_0.2.5       magrittr_1.5     
[31] whisker_0.3-2     backports_1.1.2   scales_1.0.0     
[34] htmltools_0.3.6   assertthat_0.2.0  colorspace_1.3-2 
[37] labeling_0.3      stringi_1.2.4     lazyeval_0.2.1   
[40] munsell_0.5.0     crayon_1.3.4      R.oo_1.22.0

This reproducible R Markdown analysis was created with workflowr 1.1.1