Last updated: 2023-05-19

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Description:

Compare results for susie and susierss.


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")
rss = readRDS("./data/dsc3/susie.rss.rds")
rss.varY = readRDS("./data/dsc3/susie.rss.varY.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$susie.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$susie_rss.pip[[x]]))
  pip.rss.varY = unlist(lapply(indx, function(x) rss.varY$susie_rss_varY.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 0.99, by = 0.01)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
  res.rss.varY = calculate_tpr_vs_fdr(pip.rss.varY, 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.rss[,2], res.rss[,1], type = "l", col = 2)
  lines(res.rss.varY[,2], res.rss.varY[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.rss[96,2], res.rss[96, 1])
  points(res.rss.varY[96,2], res.rss.varY[96, 1])
  
  legend("topleft", legend = c("susie", "susie.rss.varY=1", "susie.rss.varY"), col = c(1,2,3), lty = 1)
}

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.rss = unlist(lapply(indx, function(x) rss$susie_rss.pip[[x]]))
  pip.rss.varY = unlist(lapply(indx, function(x) rss.varY$susie_rss_varY.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 0.99, by = 0.01)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
  res.rss.varY = calculate_tpr_vs_fdr(pip.rss.varY, 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.rss[,2], res.rss[,1], type = "l", col = 2)
  lines(res.rss.varY[,2], res.rss.varY[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.rss[96,2], res.rss[96, 1])
  points(res.rss.varY[96,2], res.rss.varY[96, 1])
  
  legend("topleft", legend = c("susie", "susie.rss.varY=1", "susie.rss.varY"), col = c(1,2,3), lty = 1)
  
}

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$susie.pip[[x]]))
  pip.rss = unlist(lapply(indx, function(x) rss$susie_rss.pip[[x]]))
  pip.rss.varY = unlist(lapply(indx, function(x) rss.varY$susie_rss_varY.pip[[x]]))
  is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
  
  ts = seq(from = 0, to = 0.99, by = 0.01)
  res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
  res.rss = calculate_tpr_vs_fdr(pip.rss, is_effect, ts)
  res.rss.varY = calculate_tpr_vs_fdr(pip.rss.varY, 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.rss[,2], res.rss[,1], type = "l", col = 2)
  lines(res.rss.varY[,2], res.rss.varY[,1], type = "l", col = 3)
  
  points(res.susie[96,2], res.susie[96, 1])
  points(res.rss[96,2], res.rss[96, 1])
  points(res.rss.varY[96,2], res.rss.varY[96, 1])
  
  legend("topleft", legend = c("susie", "susie.rss.varY=1", "susie.rss.varY"), col = c(1,2,3), lty = 1)
}

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

5. Assess Susie.rss 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(rss$simulate.num_effect == i & rss$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
  }
}

coverage
#             effect:1  effect:2  effect:3
# censor:0   0.9797297 0.8823529 0.9029650
# censor:0.2 0.9500000 0.8867188 0.8715084
# censor:0.4 0.9523810 0.8888889 0.8892308
# censor:0.6 0.9732143 0.9579439 0.8311688
# censor:0.8 0.9523810 0.9437500 0.9009901
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(rss$simulate.num_effect == i & rss$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(rss$susie_rss.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) rss$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      0.725 0.6165414 0.5766667
# censor:0.2    0.760 0.5775000 0.5375626
# censor:0.4    0.600 0.5814536 0.4916667
# censor:0.6    0.545 0.5150000 0.4273790
# censor:0.8    0.400 0.3875000 0.3100000
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(rss$simulate.num_effect != 0 & rss$simulate.cor_type == cor_type[i] & rss$simulate.censor_lvl == censoring[j])
    coverage[j, i] = calculate_cs_coverage(rss$susie_rss.cs, rss$simulate.is_effect, dat_indx)
  }
}

coverage
#            real correlation independent
# censor:0          0.8246914   1.0000000
# censor:0.2        0.7918782   0.9973684
# censor:0.4        0.8108108   1.0000000
# censor:0.6        0.8079268   0.9967320
# censor:0.8        0.8590308   0.9954338
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(rss$simulate.num_effect != 0 & rss$simulate.cor_type == cor_type[i] & rss$simulate.censor_lvl == censoring[j])
    cs_effect = get_cs_effect(rss$susie_rss.cs, dat_indx, p = 1000)
    is_effect = unlist(lapply(dat_indx, function(x) rss$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          0.5859766   0.6433333
# censor:0.2        0.5442404   0.6316667
# censor:0.4        0.5242070   0.5550000
# censor:0.6        0.4440735   0.5083333
# censor:0.8        0.3383333   0.3633333

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