Last updated: 2018-12-10
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Unstaged changes:
Modified: analysis/EstimateCorMaxMVSample.Rmd
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I randomly generate 20 positive definite correlation matrices, V. The sample size is 4000.
\[ \hat{z}_j|z_j \sim N_{5}(z_j, V_j) \] \[ z_j \sim \delta_{0} \]
library(ggplot2)
Summary = readRDS('../output/diff_v_signal/summary.rds')
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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7a0bba6 | zouyuxin | 2018-12-09 |
loglike = Summary[Summary$summary == 'mashloglik', c('DSC','estimate', 'summary.score', 'summary')]
ggplot(loglike, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()
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7a0bba6 | zouyuxin | 2018-12-09 |
loglike_ora = loglike[-which(loglike$estimate == 'oracle'), ]
ggplot(loglike_ora, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()
RRMSE = Summary[Summary$summary == 'RRMSE', c('DSC','estimate', 'summary.score', 'summary')]
ggplot(RRMSE, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()
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7a0bba6 | zouyuxin | 2018-12-09 |
RRMSE_ora = RRMSE[-which(RRMSE$estimate == 'oracle'), ]
ggplot(RRMSE_ora, aes(x = DSC, y=summary.score, group = estimate, color = estimate)) + geom_line()
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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7a0bba6 | zouyuxin | 2018-12-09 |
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
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