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2024-02-20
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Knit directory: survival-susie/
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pip_calibration <- function(pips, is_effect){
ts = seq(0, 0.9, by = 0.1)
res = matrix(NA, ncol = 3, nrow = length(ts))
colnames(res) = c("range.start", "expected", "empirical")
for (i in 1:length(ts)){
lo = ts[i]
up = lo + 0.1
indx = which(pips >= lo & pips <= up)
res[i, 1] = lo
res[i, 2] = mean(pips[indx])
res[i, 3] = sum(is_effect[indx])/ length(indx)
}
# when there is no pip in the certain range
res2 = na.omit(res)
return(res2)
}
plot_calibration <- function(dat.calibration, method, num_effect){
plot(dat.calibration[,2], dat.calibration[,3], xlim = c(0, 1), ylim = c(0, 1),
main= paste0("Calibration: ", method, ", effect number= ", num_effect), xlab="Expected", ylab="Observed")
abline(a = 0, b = 1, col = "red")
}susie = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie.rds")
survsvb = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/survsvb.rds")
bvsnlp = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/bvsnlp.rds")
rss = readRDS("/project2/mstephens/yunqiyang/surv-susie/dsc202401/susie_rss.rds")1. Use the real correlation of SNPs
par(mfrow = c(3,4))
for (num in 1:3){
indx = which(bvsnlp$simulate.cor_type == "real" & bvsnlp$simulate.num_effect == num)
is_effect = unlist(lapply(indx, function(x) bvsnlp$simulate.is_effect[[x]]))
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
# Calibration plot for pip.susie
calibration.susie = pip_calibration(pip.susie, is_effect)
plot_calibration(calibration.susie, method = "susie", num_effect = num)
# Calibration plot for pip.svb
calibration.survsvb = pip_calibration(pip.survsvb, is_effect)
plot_calibration(calibration.survsvb, method = "svb", num_effect = num)
# Calibration plot for pip.bvsnlp
calibration.bvsnlp = pip_calibration(pip.bvsnlp, is_effect)
plot_calibration(calibration.bvsnlp, method = "bvsnlp", num_effect = num)
# Calibration plot for pip.rss
calibration.rss = pip_calibration(pip.rss, is_effect)
plot_calibration(calibration.rss, method = "susie rss", num_effect = num)
}
2. Near independent SNPs
par(mfrow = c(3,4))
for (num in 1:3){
indx = which(bvsnlp$simulate.cor_type == "independent" & bvsnlp$simulate.num_effect == num)
is_effect = unlist(lapply(indx, function(x) bvsnlp$simulate.is_effect[[x]]))
pip.susie = unlist(lapply(indx, function(x) susie$susie.pip[[x]]))
pip.survsvb = unlist(lapply(indx, function(x) survsvb$svb.pip[[x]]))
pip.bvsnlp = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
pip.rss = unlist(lapply(indx, function(x) bvsnlp$bvsnlp.pip[[x]]))
# Calibration plot for pip.susie
calibration.susie = pip_calibration(pip.susie, is_effect)
plot_calibration(calibration.susie, method = "susie", num_effect = num)
# Calibration plot for pip.svb
calibration.survsvb = pip_calibration(pip.survsvb, is_effect)
plot_calibration(calibration.survsvb, method = "svb", num_effect = num)
# Calibration plot for pip.bvsnlp
calibration.bvsnlp = pip_calibration(pip.bvsnlp, is_effect)
plot_calibration(calibration.bvsnlp, method = "bvsnlp", num_effect = num)
# Calibration plot for pip.rss
calibration.rss = pip_calibration(pip.rss, is_effect)
plot_calibration(calibration.rss, method = "susie rss", num_effect = num)
}
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