Last updated: 2024-03-20

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Description:

Version 4 simulation results, comparing power vs. FDR across methods. I vary the threshold for claiming effect variables based on marginal PIP value. Also, I add the time comparison for different methods.

source("/project2/mstephens/yunqiyang/surv-susie/survival-susie/code/post_summary.R")
susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/susie.rds")
svb = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/svb.rds")
bvsnlp = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/bvsnlp.rds")
r2b = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/r2b.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/rss.rds")

1. Results using real correlation structure from data

par(mfrow = c(2,3), cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8)
for (i in 1:5){
  indx = which(susie$simulate.cor_type == "real" & susie$simulate.censor_lvl == censor_lvl[i])
  pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
  pip.survsvb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
  pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
  pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 1, by = 0.01)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.svb = calculate_tpr_vs_fdr(pip.survsvb, is_effect, ts)
  res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
  res.r2b = calculate_tpr_vs_fdr(pip.r2b, is_effect, ts)
  
  plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
       main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
  lines(res.svb[,2], res.svb[,1], type = "l", col = 2)
  lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
  lines(res.rss[,2], res.rss[,1], type = "l", col = 4)
  lines(res.r2b[,2], res.r2b[,1], type = "l", col = 5)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.svb[96,2], res.svb[96, 1])
  points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
  points(res.rss[96,2], res.rss[96, 1])
  points(res.r2b[96,2], res.r2b[96, 1])
  
  legend("topleft", legend = c("susie", "survival.svb", "bvsnlp", "rss", "r2b"), col = c(1,2,3,4,5), lty = 1)
  
}

Version Author Date
8df7643 yunqi yang 2024-03-18

The dots indicate PIP threshold = 0.95

2. Results using independent X, without data from null model.

par(mfrow = c(2,3), cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8)
for (i in 1:5){
  indx = which(susie$simulate.cor_type == "independent" & susie$simulate.censor_lvl == censor_lvl[i])
  pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
  pip.survsvb = unlist(lapply(indx, function(x) svb$svb.pip[[x]]))
  pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$rss.pip[[x]]))
  pip.r2b = unlist(lapply(indx, function(x) r2b$r2b.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 1, by = 0.01)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.svb = calculate_tpr_vs_fdr(pip.survsvb, is_effect, ts)
  res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
  res.r2b = calculate_tpr_vs_fdr(pip.r2b, is_effect, ts)
  
  plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
       main = paste0("Independent, effect 0-3", ",censor=", censor_lvl[i]))
  lines(res.svb[,2], res.svb[,1], type = "l", col = 2)
  lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
  lines(res.rss[,2], res.rss[,1], type = "l", col = 4)
  lines(res.r2b[,2], res.r2b[,1], type = "l", col = 5)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.svb[96,2], res.svb[96, 1])
  points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
  points(res.rss[96,2], res.rss[96, 1])
  points(res.r2b[96,2], res.r2b[96, 1])
  
  legend("topleft", legend = c("susie", "survival.svb", "bvsnlp", "rss", "r2b"), col = c(1,2,3,4,5), lty = 1)
}

Version Author Date
ee93297 yunqi yang 2024-03-18
8df7643 yunqi yang 2024-03-18

The dots indicate PIP threshold = 0.95.

3. Time comparison

res = readRDS("/project2/mstephens/yunqiyang/surv-susie/sim2024/sim_default_iter/time_comparison.rds")
t = unlist(lapply(1:length(res), function(i) mean(res[[i]][,5], , na.rm = TRUE)))
names(t) = c("susie", "bvsnlp", "susie_rss", "svb", "r2b")
print(t)
#      susie     bvsnlp  susie_rss        svb        r2b 
# 345.633915  50.401950   6.528315 134.625127 182.319781

sessionInfo()
# R version 4.2.0 (2022-04-22)
# Platform: x86_64-pc-linux-gnu (64-bit)
# Running under: CentOS Linux 7 (Core)
# 
# Matrix products: default
# BLAS/LAPACK: /software/openblas-0.3.13-el7-x86_64/lib/libopenblas_haswellp-r0.3.13.so
# 
# locale:
#  [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C         LC_TIME=C           
#  [4] LC_COLLATE=C         LC_MONETARY=C        LC_MESSAGES=C       
#  [7] LC_PAPER=C           LC_NAME=C            LC_ADDRESS=C        
# [10] LC_TELEPHONE=C       LC_MEASUREMENT=C     LC_IDENTIFICATION=C 
# 
# attached base packages:
# [1] stats     graphics  grDevices utils     datasets  methods   base     
# 
# other attached packages:
# [1] workflowr_1.7.0
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.8.3     highr_0.9        bslib_0.3.1      compiler_4.2.0  
#  [5] pillar_1.7.0     later_1.3.0      git2r_0.30.1     jquerylib_0.1.4 
#  [9] tools_4.2.0      getPass_0.2-2    digest_0.6.29    jsonlite_1.8.0  
# [13] evaluate_0.15    tibble_3.1.7     lifecycle_1.0.1  pkgconfig_2.0.3 
# [17] rlang_1.0.2      cli_3.3.0        rstudioapi_0.13  yaml_2.3.5      
# [21] xfun_0.30        fastmap_1.1.0    httr_1.4.3       stringr_1.4.0   
# [25] knitr_1.39       sass_0.4.1       fs_1.5.2         vctrs_0.4.1     
# [29] rprojroot_2.0.3  glue_1.6.2       R6_2.5.1         processx_3.8.0  
# [33] fansi_1.0.3      rmarkdown_2.14   callr_3.7.3      magrittr_2.0.3  
# [37] whisker_0.4      ps_1.7.0         promises_1.2.0.1 htmltools_0.5.2 
# [41] ellipsis_0.3.2   httpuv_1.6.5     utf8_1.2.2       stringi_1.7.6   
# [45] crayon_1.5.1