Last updated: 2023-05-08
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Knit directory: survival-susie/
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Version 4 simulation results, comparing power vs. FDR across 3 methods. I vary the threshold for claiming effect variables based on marginal PIP value.
Difference between v3 and v4 simulation:
Only susie has been changed, not the other two methods.
Used corrected ABF instead of original Wakefeld ABF.
Computed susie credible sets.
Conclusion:
After using corrected ABF, susie performance is improved. Now it’s better than bvsnlp almost all the time.
Power of the credible sets and coverage of credible sets also look fine.
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)
}
# @param dat_indx: the indx for the data from dsc
calculate_cs_coverage = function(dat_indx){
contain_status = c()
for (indx in dat_indx){
cs = susie$susie.cs[[indx]]$cs
true_effect = which(susie$simulate.is_effect[[indx]] == 1)
if (!is.null(cs)){
for (j in 1:length(cs)){
res = ifelse(sum(true_effect %in% unlist(cs[j]))!= 0, 1, 0)
contain_status = c(contain_status, res)
}
}
}
coverage = sum(contain_status)/length(contain_status)
return(coverage)
}
# @param dat_indx: the indx for the data from dsc
# @p: number of variables in each simulation replicate.
get_cs_effect = function(dat_indx, p){
cs_effect = c()
for (indx in dat_indx){
effect = rep(0, p)
cs_effect_indx = c(unlist(susie$susie.cs[[indx]]$cs))
effect[cs_effect_indx] = 1
cs_effect = c(cs_effect, effect)
}
return(cs_effect)
}
susie = readRDS("./data/dsc3/susie.cs.rds")
survsvb = readRDS("./data/dsc3/survsvb.rds")
bvsnlp = readRDS("./data/dsc3/bvsnlp.rds")
par(mfrow = c(2,3), cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8)
for (i in 1:5){
indx = which(susie$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) survsvb$survivalsvb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
ts = seq(from = 0, to = 1, by = 0.01)
res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
res.svb = calculate_tpr_vs_fdr(pip.survsvb, is_effect, ts)
res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0,1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
main = paste0("Real correlation, effect 0-3", ",censor=", censor_lvl[i]))
lines(res.svb[,2], res.svb[,1], type = "l", col = 2)
lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
points(res.susie[96,2], res.susie[96, 1])
points(res.svb[96,2], res.svb[96, 1])
points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
legend("topleft", legend = c("susie", "survival.svb", "bvsnlp"), col = c(1,2,3), lty = 1)
}
Version | Author | Date |
---|---|---|
f07fc2e | yunqiyang0215 | 2023-05-08 |
The dots indicate PIP threshold = 0.95
par(mfrow = c(2,3),cex.axis = 1.5)
censor_lvl = c(0, 0.2, 0.4, 0.6, 0.8)
for (i in 1:5){
indx = which(susie$simulate.cor_type == "independent" & susie$simulate.censor_lvl == censor_lvl[i] & susie$simulate.num_effect != 0)
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$survivalsvb.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.svb = calculate_tpr_vs_fdr(pip.survsvb, 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, 0.1), ylim = c(0, 1), xlab = "FDR", ylab = "Power",
main = paste0("X independent, effect 1-3", ",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)
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])
legend("bottomright", legend = c("susie", "survival.svb", "bvsnlp"), col = c(1,2,3), lty = 1)
}
Version | Author | Date |
---|---|---|
f07fc2e | yunqiyang0215 | 2023-05-08 |
The dots indicate PIP threshold = 0.95.
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$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$survivalsvb.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.svb = calculate_tpr_vs_fdr(pip.survsvb, 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("X 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)
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])
legend("bottomright", legend = c("susie", "survival.svb", "bvsnlp"), col = c(1,2,3), lty = 1)
}
Version | Author | Date |
---|---|---|
f07fc2e | yunqiyang0215 | 2023-05-08 |
The dots indicate PIP threshold = 0.95.
For independent X, susie has ~1-1.5% less power than bvsnlp when fdr is around 0.1.
par(mfrow = c(1,2))
censor_lvl = 0.8
indx = which(susie$simulate.cor_type == "real" & susie$simulate.censor_lvl == censor_lvl)
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
is_effect = unlist(lapply(indx, function(x) susie$simulate.is_effect[[x]]))
ts = seq(from = 0, to = 1, by = 0.01)
res.susie = calculate_tpr_vs_fdr(pip.susie, is_effect, ts)
res.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0, 0.4), ylim = c(0,0.2), xlab = "FDR", ylab = "Power", main = paste0("Real X, effect 0-3", ",censor=", censor_lvl))
lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
points(res.susie[96,2], res.susie[96, 1])
points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
legend("topleft", legend = c("susie", "bvsnlp"), col = c(1,3), lty = 1)
#####
indx = which(susie$simulate.cor_type == "independent" & susie$simulate.censor_lvl == censor_lvl)
pip.susie = unlist(lapply(indx, function(x) susie$susie.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.bvsnlp = calculate_tpr_vs_fdr(pip.bvsnlp, is_effect, ts)
plot(res.susie[,2], res.susie[,1], type = "l", xlim = c(0, 0.15), xlab = "FDR", ylab = "Power", main = paste0("X independent, effect 0-3", ",censor=", censor_lvl))
lines(res.bvsnlp[,2], res.bvsnlp[,1], type = "l", col = 3)
points(res.susie[96,2], res.susie[96, 1])
points(res.bvsnlp[96,2], res.bvsnlp[96, 1])
legend("bottomright", legend = c("susie", "bvsnlp"), col = c(1,3), lty = 1)
Version | Author | Date |
---|---|---|
f07fc2e | yunqiyang0215 | 2023-05-08 |
Among the 1000 null data, susie only wrongly output 1 credible set as below:
indx = which(susie$simulate.num_effect == 0)
res = lapply(indx, function(x) is.null(susie$susie.cs[[x]]$cs))
which(res == FALSE)
susie$susie.cs[[indx[756]]]$cs
# [1] 756
# $L1
# [1] 178 190 203 215 230 234 269 279 281 285 287 290 296 315 321 323 326 332 343
# [20] 347 407 413 414 419 428 433 436 439 440 446 450 452 453 454 457 459 461 462
# [39] 464 470 478 482 500 517 522 527 531 563 564 567 568 573
coverage = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(coverage) = c("effect:1", "effect:2", "effect:3")
rownames(coverage) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
for (j in 1:5){
dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
coverage[j, i] = calculate_cs_coverage(dat_indx)
}
}
coverage
# effect:1 effect:2 effect:3
# censor:0 0.9934211 0.9787986 0.9679803
# censor:0.2 1.0000000 0.9734848 0.9624060
# censor:0.4 0.9767442 0.9776119 0.9752747
# censor:0.6 0.9823009 0.9874477 0.9444444
# censor:0.8 0.9670330 0.9906542 0.9678571
power_cs = matrix(NA, ncol = 3, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("effect:1", "effect:2", "effect:3")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:3){
for (j in 1:5){
dat_indx = which(susie$simulate.num_effect == i & susie$simulate.censor_lvl == censoring[j])
cs_effect = get_cs_effect(dat_indx, p = 1000)
is_effect = unlist(lapply(dat_indx, function(x) susie$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$simulate.censor_lvl == censoring[j])
coverage[j, i] = calculate_cs_coverage(dat_indx)
}
}
coverage
# real correlation independent
# censor:0 0.9515738 1.0000000
# censor:0.2 0.9461538 0.9976526
# censor:0.4 0.9525066 1.0000000
# censor:0.6 0.9311295 0.9972752
# censor:0.8 0.9507042 1.0000000
power_cs = matrix(NA, ncol = 2, nrow = 5)
censoring = c(0, 0.2, 0.4, 0.6, 0.8)
colnames(power_cs) = c("real correlation", "independent")
rownames(power_cs) = c("censor:0", "censor:0.2", "censor:0.4", "censor:0.6", "censor:0.8")
for (i in 1:2){
for (j in 1:5){
dat_indx = which(susie$simulate.num_effect != 0 & susie$simulate.cor_type == cor_type[i] & susie$simulate.censor_lvl == censoring[j])
cs_effect = get_cs_effect(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
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_0.5.2
# [37] httpuv_1.6.3 utf8_1.2.2 stringi_1.7.5 cachem_1.0.6
# [41] crayon_1.4.1