Last updated: 2023-05-29

Checks: 7 0

Knit directory: survival-susie/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks 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(20230201)

The command set.seed(20230201) 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.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 6d574a9

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 6d574a9. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

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:    data/.DS_Store

Unstaged changes:
    Modified:   analysis/run_ser_simple_dat.Rmd
    Modified:   analysis/ser_survival.Rmd
    Modified:   data/dsc3/susie.lbf.rds

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 repository in which changes were made to the R Markdown (analysis/susie.convergence.Rmd) and HTML (docs/susie.convergence.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 6d574a9 yunqiyang0215 2023-05-29 wflow_publish("analysis/susie.convergence.Rmd")
html 8b1b149 yunqiyang0215 2023-05-29 Build site.
html ad2c57c yunqiyang0215 2023-05-29 Build site.
Rmd d32de66 yunqiyang0215 2023-05-29 wflow_publish("analysis/susie.convergence.Rmd")

Description:

Compare results of using different convergence criteria for survival susie.

  1. The norm-2 difference between bhat is < tol.

  2. Sum of log-BF stop increasing.


calculate_tpr_vs_fdr <- function(pip, is_effect, ts){
  res <- matrix(NA, nrow = length(ts), ncol = 2)
  colnames(res) = c("tpr", "fdr")
  for (i in 1:length(ts)){
    pred_pos = pip >= ts[i]
    tp = pip >= ts[i] & is_effect == 1
    fp = pip >= ts[i] & is_effect == 0
    tpr = sum(tp)/sum(is_effect)
    fdr = sum(fp)/sum(pred_pos)
    res[i, ] = c(tpr, fdr)
  }
  return(res)
}

# coverage: the proportion of CSs that contain an effect variable
# @param dat_indx: the indx for the data from dsc
# @param res.cs: credible sets from dsc
calculate_cs_coverage = function(res.cs, res.is_effect, dat_indx){
  contain_status = c()
  for (indx in dat_indx){
     cs = res.cs[[indx]]$cs
     true_effect = which(res.is_effect[[indx]] >= 1)
     if (!is.null(cs)){
       for (j in 1:length(cs)){
         res = ifelse(sum(true_effect %in% unlist(cs[j])) ==  1, 1, 0)
         contain_status = c(contain_status, res)
        }
     }
  }
  coverage = sum(contain_status)/length(contain_status)
  return(coverage)
}

# @param res.cs: credible sets from dsc
# @param dat_indx: the indx for the data from dsc
# @p: number of variables in each simulation replicate. 
get_cs_effect = function(res.cs, dat_indx, p){
  cs_effect = c()
  for (indx in dat_indx){
    effect = rep(0, p)
    cs_effect_indx = c(unlist(res.cs[[indx]]$cs))
    effect[cs_effect_indx] = 1
    cs_effect = c(cs_effect, effect)
  }
  return(cs_effect)
}
susie = readRDS("./data/dsc3/susie.cs.rds")
susie.lbf = readRDS("./data/dsc3/susie.lbf.rds")
bvsnlp = readRDS("./data/dsc3/bvsnlp.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$susie.pip[[x]]))
  pip.susie.lbf = unlist(lapply(indx, function(x) susie.lbf$susie_lbf.pip[[x]]))
  pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$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.susie.lbf = calculate_tpr_vs_fdr(pip.susie.lbf, is_effect, ts)
  res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, 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.susie.lbf[,2], res.susie.lbf[,1], type = "l", col = 2)
  lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.susie.lbf[96,2], res.susie.lbf[96, 1])
  points(res.bvsnlp[96,2], res.bvsnlp[96, 1])

  
  legend("topleft", legend = c("susie", "susie.lbf", "bvsnlp"), col = c(1,2,3), lty = 1)
}

Version Author Date
8b1b149 yunqiyang0215 2023-05-29
ad2c57c yunqiyang0215 2023-05-29

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] & susie$simulate.num_effect != 0)
  
  pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
  pip.susie.lbf = unlist(lapply(indx, function(x) susie.lbf$susie_lbf.pip[[x]]))
  pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$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.susie.lbf = calculate_tpr_vs_fdr(pip.susie.lbf, is_effect, ts)
  res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
  
  plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,0.2), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
       main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
  lines(res.susie.lbf[,2], res.susie.lbf[,1], type = "l", col = 2)
  lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.susie.lbf[96,2], res.susie.lbf[96, 1])
  points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
  legend("topleft", legend = c("susie", "susie.lbf", "bvsnlp"), col = c(1,2,3), lty = 1)
}

Version Author Date
8b1b149 yunqiyang0215 2023-05-29
ad2c57c yunqiyang0215 2023-05-29

The dots indicate PIP threshold = 0.95.

3. Results using independent X, with 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$susie.pip[[x]]))
  pip.susie.lbf = unlist(lapply(indx, function(x) susie.lbf$susie_lbf.pip[[x]]))
  pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$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.susie.lbf = calculate_tpr_vs_fdr(pip.susie.lbf, is_effect, ts)
  res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
  
  plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0, 0.2), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
       main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
  lines(res.susie.lbf[,2], res.susie.lbf[,1], type = "l", col = 2)
  lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.susie.lbf[96,2], res.susie.lbf[96, 1])
  points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
  legend("topleft", legend = c("susie", "susie.lbf", "bvsnlp"), col = c(1,2,3), lty = 1)
}

Version Author Date
8b1b149 yunqiyang0215 2023-05-29
ad2c57c yunqiyang0215 2023-05-29

The dots indicate PIP threshold = 0.95.

5. Assess Susie CS

coverage = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
  for (j in 1:5){
    dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
  }
}

coverage
#             effect:1  effect:2  effect:3
# censor:0   0.9934211 0.9717314 0.9630542
# censor:0.2 1.0000000 0.9696970 0.9473684
# censor:0.4 0.9767442 0.9664179 0.9697802
# censor:0.6 0.9823009 0.9832636 0.9417989
# censor:0.8 0.9670330 0.9906542 0.9607143
power_cs = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
  for (j in 1:5){
    dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
    power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
    power_cs[j, i] = power
  }
}

power_cs
#            effect:1  effect:2  effect:3
# censor:0      0.755 0.6992481 0.6600000
# censor:0.2    0.765 0.6450000 0.6510851
# censor:0.4    0.630 0.6641604 0.5950000
# censor:0.6    0.555 0.5925000 0.5976628
# censor:0.8    0.440 0.5300000 0.4533333
coverage = matrix(NA, ncol = 2, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
cor_type = c("real", "independent")
colnames(coverage) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:2){
  for (j in 1:5){
    dat_indx = which(susie$simulate.num_effect != 0 & susie$simulate.cor_type == cor_type[i] & susie$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie$susie.cs, susie$simulate.is_effect, dat_indx)
  }
}

coverage
#            real correlation independent
# censor:0          0.9418886   1.0000000
# censor:0.2        0.9282051   0.9976526
# censor:0.4        0.9393140   1.0000000
# censor:0.6        0.9256198   0.9972752
# censor:0.8        0.9436620   1.0000000
power_cs = matrix(NA, ncol = 2, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("real correlation", "independent")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:2){
  for (j in 1:5){
    dat_indx = which(susie$simulate.num_effect != 0 & susie$simulate.cor_type == cor_type[i] & susie$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(susie$susie.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) susie$simulate.is_effect[[x]]))
    power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
    power_cs[j, i] = power
  }
}

power_cs
#            real correlation independent
# censor:0          0.6644407   0.7133333
# censor:0.2        0.6277129   0.7083333
# censor:0.4        0.6110184   0.6366667
# censor:0.6        0.5676127   0.6100000
# censor:0.8        0.4516667   0.5016667

6. Assess Susie.lbf cs

coverage = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
  for (j in 1:5){
    dat_indx = which(susie.lbf$simulate.num_effect == i & susie.lbf$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie.lbf$susie_lbf.cs, susie.lbf$simulate.is_effect, dat_indx)
  }
}

coverage
#             effect:1  effect:2  effect:3
# censor:0   0.9934641 0.9718310 0.9585366
# censor:0.2 0.9934641 0.9619772 0.9452736
# censor:0.4 0.9923664 0.9589552 0.9673913
# censor:0.6 0.9745763 0.9703390 0.9421053
# censor:0.8 0.9680851 0.9906103 0.9633700
power_cs = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
  for (j in 1:5){
    dat_indx = which(susie.lbf$simulate.num_effect == i & susie.lbf$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(susie.lbf$susie_lbf.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) susie.lbf$simulate.is_effect[[x]]))
    power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
    power_cs[j, i] = power
  }
}

power_cs
#            effect:1  effect:2  effect:3
# censor:0      0.760 0.6942356 0.6616667
# censor:0.2    0.760 0.6300000 0.6494157
# censor:0.4    0.650 0.6591479 0.6016667
# censor:0.6    0.575 0.5775000 0.5943239
# censor:0.8    0.455 0.5300000 0.4416667
coverage = matrix(NA, ncol = 2, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
cor_type = c("real", "independent")
colnames(coverage) = c("real correlation", "independent")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:2){
  for (j in 1:5){
    dat_indx = which(susie.lbf$simulate.num_effect != 0 & susie.lbf$simulate.cor_type == cor_type[i] & susie.lbf$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(susie.lbf$susie_lbf.cs, susie.lbf$simulate.is_effect, dat_indx)
  }
}

coverage
#            real correlation independent
# censor:0          0.9371981   1.0000000
# censor:0.2        0.9222798   0.9930556
# censor:0.4        0.9366755   1.0000000
# censor:0.6        0.9194444   0.9919786
# censor:0.8        0.9492754   0.9967105
power_cs = matrix(NA, ncol = 2, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("real correlation", "independent")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:2){
  for (j in 1:5){
    dat_indx = which(susie.lbf$simulate.num_effect != 0 & susie.lbf$simulate.cor_type == cor_type[i] & susie.lbf$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(susie.lbf$susie_lbf.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) susie.lbf$simulate.is_effect[[x]]))
    power = sum(cs_effect ==1 & is_effect == 1)/sum(is_effect)
    power_cs[j, i] = power
  }
}

power_cs
#            real correlation independent
# censor:0          0.6560935   0.7216667
# censor:0.2        0.6076795   0.7150000
# censor:0.4        0.6110184   0.6466667
# censor:0.6        0.5525876   0.6183333
# censor:0.8        0.4416667   0.5050000

sessionInfo()
# R version 4.1.1 (2021-08-10)
# Platform: x86_64-apple-darwin20.6.0 (64-bit)
# Running under: macOS Monterey 12.0.1
# 
# Matrix products: default
# BLAS:   /usr/local/Cellar/openblas/0.3.18/lib/libopenblasp-r0.3.18.dylib
# LAPACK: /usr/local/Cellar/r/4.1.1_1/lib/R/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] workflowr_1.6.2
# 
# loaded via a namespace (and not attached):
#  [1] Rcpp_1.0.8.3     highr_0.9        pillar_1.6.4     compiler_4.1.1  
#  [5] bslib_0.4.1      later_1.3.0      jquerylib_0.1.4  git2r_0.28.0    
#  [9] tools_4.1.1      digest_0.6.28    jsonlite_1.7.2   evaluate_0.14   
# [13] lifecycle_1.0.1  tibble_3.1.5     pkgconfig_2.0.3  rlang_1.0.6     
# [17] cli_3.1.0        rstudioapi_0.13  yaml_2.2.1       xfun_0.27       
# [21] fastmap_1.1.0    stringr_1.4.0    knitr_1.36       fs_1.5.0        
# [25] vctrs_0.3.8      sass_0.4.4       rprojroot_2.0.2  glue_1.4.2      
# [29] R6_2.5.1         fansi_0.5.0      rmarkdown_2.11   magrittr_2.0.1  
# [33] whisker_0.4      promises_1.2.0.1 ellipsis_0.3.2   htmltools_0.5.5 
# [37] httpuv_1.6.3     utf8_1.2.2       stringi_1.7.5    cachem_1.0.6    
# [41] crayon_1.4.1