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Knit directory: survival-susie/
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\[ ABF(H_1/H_0)=\sqrt\frac{V}{V+W}\exp\{\frac{z^2}{2}\frac{W}{V+W}\}, \] where \(V\) is the variance of estimated regression coefficient, and \(W\) is variance in the normal prior, \(N(0,W)\).
# Function to calculate approximate BF based on Wakefield approximation
# @param z: zscore of the regression coefficient
# @param s: standard deviation of the estimated coefficient
compute_abf <- function(z, s, prior_variance){
abf <- sqrt(s^2/(s^2+prior_variance))*exp(z^2/2*(prior_variance/(s^2+prior_variance)))
return(abf)
}
compute_approx_post_var <- function(z, s, prior_variance){
post_var <- 1/(1/s^2 + 1/prior_variance)
return(post_var)
}
# @param post_var: posterior variance
# @param s: standard deviation of the estimated coefficient
# @param bhat: estimated beta effect
compute_approx_post_mean <- function(post_var, s, bhat){
mu <- post_var/(s^2)*bhat
return(mu)
}dat = readRDS("./data/sim_dat_simple.rds")
library(survival)
# Modified Karl's code for intercept part
devtools::load_all("/Users/nicholeyang/Desktop/logisticsusie")ℹ Loading logisticsusiesurv_uni_fun <- function(x, y, o, prior_variance, estimate_intercept = 0, ...){
fit <- coxph(y~ x + o)
bhat <- summary(fit)$coefficients[1, 1]
sd <- summary(fit)$coefficients[1, 3]
zscore <- summary(fit)$coefficients[1, 4]
bf <- compute_abf(zscore, sd, prior_variance)
var <- compute_approx_post_var(zscore, sd, prior_variance)
mu <- compute_approx_post_mean(var, sd, bhat)
lbf <- log(bf)
return(list(mu = mu, var=var, lbf=lbf, intercept=0))
}
fit_coxph <- ser_from_univariate(surv_uni_fun)\(\log T_i =\beta_0+\epsilon_i\) and \(\beta_0 = 1\).
X = as.matrix(dat[[1]][, c(2:3)])
## Create survival object. status == 2 is death
y <- with(dat[[1]], Surv(surT, status == 2))fit1 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/2, 2), tol = 1e-3, maxit = 100, estimate_intercept = TRUE, ser_function = fit_coxph)Warning in ibss_from_ser(X, y, L = 10, prior_variance = 1, prior_weights =
rep(1/2, : Maximum number of iterations reached7.056 sec elapsedfit1$alpha [,1] [,2]
[1,] 0.7644728 0.2355272
[2,] 0.6077619 0.3922381
[3,] 0.5201106 0.4798894
[4,] 0.7071450 0.2928550
[5,] 0.4063704 0.5936296
[6,] 0.4362467 0.5637533
[7,] 0.5102112 0.4897888
[8,] 0.8054618 0.1945382
[9,] 0.8126784 0.1873216
[10,] 0.8152894 0.1847106\(\beta_0 = 1, \beta_1 = 3\)
X = as.matrix(dat[[2]][, c(2:3)])
## Create survival object. status == 2 is death
y <- with(dat[[2]], Surv(surT, status == 2))fit2 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/2, 2), tol = 1e-3, maxit = 100, estimate_intercept = TRUE, ser_function = fit_coxph)0.188 sec elapsedfit2$alpha [,1] [,2]
[1,] 1 1.475791e-10
[2,] 1 1.475791e-10
[3,] 1 1.475791e-10
[4,] 1 1.475791e-10
[5,] 1 1.475791e-10
[6,] 1 1.475791e-10
[7,] 1 1.475791e-10
[8,] 1 1.475791e-10
[9,] 1 1.475791e-10
[10,] 1 1.475791e-10\(\beta_0 = 1, \beta_1 = 3\) and \(cor(x_1,x_2)=0.9\).
X = as.matrix(dat[[3]][, c(2:3)])
## Create survival object. status == 2 is death
y <- with(dat[[3]], Surv(surT, status == 2))fit3 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/2, 2), tol = 1e-3, maxit = 100, estimate_intercept = TRUE, ser_function = fit_coxph)2.947 sec elapsedfit3$alpha [,1] [,2]
[1,] 0.1553077 0.8446923
[2,] 0.6345619 0.3654381
[3,] 0.6348456 0.3651544
[4,] 0.6348509 0.3651491
[5,] 0.6345786 0.3654214
[6,] 0.6341502 0.3658498
[7,] 0.6337550 0.3662450
[8,] 0.6335615 0.3664385
[9,] 0.6336429 0.3663571
[10,] 0.6339523 0.3660477\(\beta_0 = 1, \beta_1 = 3, \beta_2 = 2\) and \(cor(x_1,x_2)=0.9\).
X = as.matrix(dat[[4]][, c(2:3)])
## Create survival object. status == 2 is death
y <- with(dat[[4]], Surv(surT, status == 2))fit4 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/2, 2), tol = 1e-3, maxit = 100, estimate_intercept = TRUE, ser_function = fit_coxph)Warning in ibss_from_ser(X, y, L = 10, prior_variance = 1, prior_weights =
rep(1/2, : Maximum number of iterations reached6.832 sec elapsedfit4$alpha [,1] [,2]
[1,] 0.9526665552 0.04733344
[2,] 0.0001298666 0.99987013
[3,] 0.4833317337 0.51666827
[4,] 0.4895864377 0.51041356
[5,] 0.4936979043 0.50630210
[6,] 0.4963614006 0.50363860
[7,] 0.4981962018 0.50180380
[8,] 0.4995617786 0.50043822
[9,] 0.5006569051 0.49934309
[10,] 0.5015948479 0.49840515
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] logisticsusie_0.0.0.9004 testthat_3.1.0 survival_3.2-11
[4] workflowr_1.6.2
loaded via a namespace (and not attached):
[1] tidyselect_1.1.1 xfun_0.27 bslib_0.4.1 remotes_2.4.1
[5] purrr_0.3.4 splines_4.1.1 lattice_0.20-44 generics_0.1.2
[9] vctrs_0.3.8 usethis_2.1.3 htmltools_0.5.2 yaml_2.2.1
[13] utf8_1.2.2 rlang_1.0.6 pkgbuild_1.2.0 jquerylib_0.1.4
[17] later_1.3.0 pillar_1.6.4 glue_1.4.2 withr_2.5.0
[21] sessioninfo_1.1.1 matrixStats_0.63.0 lifecycle_1.0.1 stringr_1.4.0
[25] tictoc_1.1 devtools_2.4.2 evaluate_0.14 memoise_2.0.1
[29] knitr_1.36 callr_3.7.0 fastmap_1.1.0 httpuv_1.6.3
[33] ps_1.6.0 fansi_0.5.0 Rcpp_1.0.8.3 promises_1.2.0.1
[37] cachem_1.0.6 desc_1.4.0 pkgload_1.2.3 jsonlite_1.7.2
[41] fs_1.5.0 digest_0.6.28 stringi_1.7.5 dplyr_1.0.7
[45] processx_3.5.2 rprojroot_2.0.2 grid_4.1.1 cli_3.1.0
[49] tools_4.1.1 magrittr_2.0.1 sass_0.4.4 tibble_3.1.5
[53] crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3 ellipsis_0.3.2
[57] Matrix_1.5-3 prettyunits_1.1.1 rmarkdown_2.11 rstudioapi_0.13
[61] R6_2.5.1 git2r_0.28.0 compiler_4.1.1