Last updated: 2023-02-12
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Knit directory: survival-susie/
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I simulated data in 4 different scenarios, and apply both coxph model and susie procedure. The data simulation procedure is available at https://yunqiyang0215.github.io/survival-susie/sim_survival.html
Susie works in 1,2,4 scenarios, but not scenario 3.
\[ ABF(H_1/H_0)=\sqrt\frac{V}{V+W}\exp\{\frac{z^2}{2}\frac{W}{V+W}\}, \] 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 \(\beta_0 = 1\).
## Create survival object. status == 2 is death
dat[[1]]$y <- with(dat[[1]], Surv(surT, status == 2))
# Fit cox ph. Cox ph model with select multiple significant predictors..
cox1 <- coxph(y ~ .-status, data = dat[[1]])Warning in coxph.fit(X, Y, istrat, offset, init, control, weights = weights, :
Ran out of iterations and did not convergep = 50
X = as.matrix(dat[[1]][, c(2:(p+1))])
y = dat[[1]]$y
# IBSS of susie
t1 <- proc.time()
fit1 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/p, p), tol = 1e-3, maxit = 500, estimate_intercept = TRUE, ser_function = fit_coxph)Warning in ibss_from_ser(X, y, L = 10, prior_variance = 1, prior_weights =
rep(1/p, : Maximum number of iterations reached785.503 sec elapsedt2 <- proc.time()
t2 - t1 user system elapsed
766.294 10.191 785.546 t(apply(fit1$alpha, 1, function(x) sort(x, decreasing = TRUE))) [,1] [,2] [,3] [,4] [,5]
[1,] 0.91899980 0.0092403541 0.0083458706 0.0066814279 0.0047772034
[2,] 0.11956439 0.0729481312 0.0602207982 0.0456422318 0.0392203857
[3,] 0.06147715 0.0589132929 0.0530900237 0.0522839102 0.0521153945
[4,] 0.02603267 0.0237425101 0.0233931596 0.0226486155 0.0223099792
[5,] 0.42839269 0.2512457454 0.0544716657 0.0382282059 0.0271930768
[6,] 0.54442937 0.0577160692 0.0375068827 0.0326125133 0.0304433803
[7,] 0.99663542 0.0009915338 0.0001776357 0.0001305569 0.0001048597
[8,] 0.82950575 0.0351643177 0.0228999712 0.0202086171 0.0161099566
[9,] 0.99244549 0.0010955863 0.0004578005 0.0004369227 0.0003917363
[10,] 0.58541031 0.0789575892 0.0625817470 0.0500121756 0.0328155520
[,6] [,7] [,8] [,9] [,10]
[1,] 3.776749e-03 3.733407e-03 3.567408e-03 3.454518e-03 3.256584e-03
[2,] 3.705943e-02 3.644532e-02 3.464566e-02 3.182917e-02 3.031351e-02
[3,] 5.154482e-02 4.087462e-02 3.488312e-02 3.362618e-02 3.278618e-02
[4,] 2.170864e-02 2.148999e-02 2.143084e-02 2.115388e-02 2.106524e-02
[5,] 2.686396e-02 1.775095e-02 1.513247e-02 1.397575e-02 1.243980e-02
[6,] 2.358777e-02 2.193766e-02 1.839086e-02 1.778223e-02 1.641336e-02
[7,] 9.206334e-05 9.162115e-05 8.139799e-05 7.790874e-05 7.351158e-05
[8,] 9.690895e-03 6.294084e-03 5.437893e-03 5.274808e-03 4.668549e-03
[9,] 3.207675e-04 3.065673e-04 2.739026e-04 2.410195e-04 1.857029e-04
[10,] 2.228639e-02 1.582705e-02 1.494970e-02 1.333483e-02 1.119926e-02
[,11] [,12] [,13] [,14] [,15]
[1,] 2.802130e-03 2.355420e-03 2.243332e-03 2.061476e-03 0.0019382771
[2,] 2.888766e-02 2.767799e-02 2.631295e-02 2.389300e-02 0.0234105126
[3,] 2.529966e-02 2.496239e-02 2.440685e-02 2.341794e-02 0.0222720629
[4,] 2.086020e-02 2.085458e-02 2.075692e-02 2.046430e-02 0.0204469869
[5,] 1.232105e-02 1.103063e-02 8.837923e-03 7.843161e-03 0.0071320379
[6,] 1.613733e-02 1.565441e-02 1.401207e-02 1.353971e-02 0.0132063742
[7,] 6.419446e-05 5.678118e-05 5.252856e-05 5.171947e-05 0.0000481767
[8,] 4.124213e-03 3.852764e-03 3.803361e-03 3.455704e-03 0.0030122358
[9,] 1.805087e-04 1.756190e-04 1.718652e-04 1.698530e-04 0.0001619678
[10,] 1.102541e-02 1.012314e-02 1.008288e-02 7.955547e-03 0.0073063371
[,16] [,17] [,18] [,19] [,20]
[1,] 1.892862e-03 1.806083e-03 1.566959e-03 1.425524e-03 1.319524e-03
[2,] 2.295071e-02 2.284932e-02 2.277992e-02 2.232681e-02 2.225969e-02
[3,] 2.225291e-02 2.153259e-02 2.092026e-02 2.034739e-02 2.010942e-02
[4,] 2.040658e-02 2.034664e-02 2.012627e-02 2.011776e-02 2.004417e-02
[5,] 6.447835e-03 6.281893e-03 5.389096e-03 5.368551e-03 5.288201e-03
[6,] 1.186261e-02 1.057700e-02 9.512305e-03 8.835741e-03 7.491466e-03
[7,] 4.578446e-05 4.412812e-05 4.354801e-05 4.302276e-05 4.268603e-05
[8,] 2.947736e-03 2.831000e-03 2.713871e-03 2.610476e-03 2.389867e-03
[9,] 1.617269e-04 1.549210e-04 1.506845e-04 1.481317e-04 1.396832e-04
[10,] 6.980427e-03 6.488257e-03 6.449254e-03 6.237541e-03 5.697804e-03
[,21] [,22] [,23] [,24] [,25]
[1,] 1.281744e-03 1.073212e-03 9.561250e-04 0.0009122852 8.762018e-04
[2,] 2.032948e-02 1.634102e-02 1.548257e-02 0.0142425772 1.349642e-02
[3,] 1.607457e-02 1.531304e-02 1.523835e-02 0.0143941707 1.419581e-02
[4,] 1.989761e-02 1.986575e-02 1.985744e-02 0.0198125703 1.978416e-02
[5,] 3.755166e-03 3.609394e-03 3.462716e-03 0.0030430666 2.916343e-03
[6,] 7.040801e-03 6.223758e-03 6.198470e-03 0.0061562744 5.816892e-03
[7,] 4.202234e-05 4.158203e-05 4.129733e-05 0.0000411205 4.053116e-05
[8,] 1.607409e-03 1.344795e-03 1.118922e-03 0.0010726257 8.268222e-04
[9,] 1.391862e-04 1.339847e-04 1.135070e-04 0.0001001624 9.894161e-05
[10,] 3.848784e-03 3.559147e-03 2.755587e-03 0.0026834583 2.154703e-03
[,26] [,27] [,28] [,29] [,30]
[1,] 8.518280e-04 8.237542e-04 7.927720e-04 7.729285e-04 5.850497e-04
[2,] 1.152119e-02 1.124109e-02 1.108781e-02 1.101837e-02 1.067606e-02
[3,] 1.285917e-02 1.206381e-02 1.196145e-02 1.190851e-02 1.159272e-02
[4,] 1.976795e-02 1.970331e-02 1.967518e-02 1.942752e-02 1.922791e-02
[5,] 2.151709e-03 1.675486e-03 1.606189e-03 1.580083e-03 1.502310e-03
[6,] 5.118461e-03 4.345918e-03 3.455868e-03 3.130471e-03 2.961220e-03
[7,] 3.928663e-05 3.925020e-05 3.855902e-05 3.663299e-05 3.600418e-05
[8,] 8.204985e-04 7.521447e-04 6.591014e-04 5.033484e-04 4.599117e-04
[9,] 9.488316e-05 9.411921e-05 9.109178e-05 8.290628e-05 7.366384e-05
[10,] 2.152688e-03 2.012201e-03 1.794211e-03 1.359301e-03 1.262696e-03
[,31] [,32] [,33] [,34] [,35]
[1,] 5.430455e-04 5.363286e-04 5.148869e-04 4.771497e-04 3.515594e-04
[2,] 1.012530e-02 9.366180e-03 9.227424e-03 6.898339e-03 6.775335e-03
[3,] 1.079819e-02 1.053209e-02 1.050394e-02 1.043307e-02 1.022009e-02
[4,] 1.920953e-02 1.911693e-02 1.906522e-02 1.906294e-02 1.902369e-02
[5,] 1.398343e-03 1.277679e-03 1.052791e-03 8.842818e-04 8.756149e-04
[6,] 2.795346e-03 2.525960e-03 2.154546e-03 2.033345e-03 1.865190e-03
[7,] 3.573549e-05 3.561987e-05 3.551708e-05 3.507824e-05 3.483076e-05
[8,] 4.100610e-04 3.493510e-04 3.029593e-04 2.951152e-04 2.852825e-04
[9,] 7.152391e-05 7.032810e-05 6.968144e-05 6.954759e-05 6.861870e-05
[10,] 1.169062e-03 9.567253e-04 8.338493e-04 8.091802e-04 7.919996e-04
[,36] [,37] [,38] [,39] [,40]
[1,] 3.441030e-04 3.237195e-04 3.135194e-04 2.878373e-04 2.611610e-04
[2,] 6.632515e-03 6.506266e-03 6.296718e-03 6.094298e-03 5.419148e-03
[3,] 9.846491e-03 9.676966e-03 9.567831e-03 8.982537e-03 8.936683e-03
[4,] 1.896319e-02 1.883386e-02 1.880452e-02 1.879002e-02 1.877770e-02
[5,] 8.669562e-04 8.588893e-04 7.579992e-04 6.978820e-04 6.697418e-04
[6,] 1.789669e-03 1.644004e-03 1.633017e-03 1.599993e-03 1.249892e-03
[7,] 3.409800e-05 3.390341e-05 3.369820e-05 3.282662e-05 3.266831e-05
[8,] 2.789760e-04 2.455660e-04 2.264010e-04 2.111803e-04 1.970664e-04
[9,] 6.545202e-05 6.536349e-05 6.255823e-05 6.179552e-05 6.163367e-05
[10,] 7.310933e-04 6.939954e-04 6.435941e-04 5.380368e-04 5.339865e-04
[,41] [,42] [,43] [,44] [,45]
[1,] 2.587231e-04 2.363220e-04 2.356425e-04 2.145967e-04 1.834289e-04
[2,] 5.359536e-03 5.130125e-03 4.981906e-03 4.787654e-03 4.555958e-03
[3,] 8.785263e-03 8.733567e-03 8.218159e-03 7.792635e-03 7.638156e-03
[4,] 1.874820e-02 1.868427e-02 1.862216e-02 1.859249e-02 1.833615e-02
[5,] 6.104045e-04 5.984956e-04 5.152577e-04 4.885139e-04 3.819366e-04
[6,] 1.220743e-03 1.177888e-03 1.110771e-03 9.488152e-04 9.487655e-04
[7,] 3.265741e-05 3.262573e-05 3.241099e-05 3.227644e-05 3.199689e-05
[8,] 1.638034e-04 1.616728e-04 1.542288e-04 1.262766e-04 1.192129e-04
[9,] 5.810671e-05 5.739052e-05 5.679881e-05 5.616771e-05 5.597475e-05
[10,] 4.646112e-04 4.456891e-04 4.420603e-04 3.541724e-04 3.529668e-04
[,46] [,47] [,48] [,49] [,50]
[1,] 1.824226e-04 1.598658e-04 1.471429e-04 1.376218e-04 1.201159e-04
[2,] 3.789132e-03 3.481435e-03 3.091897e-03 2.424021e-03 2.382660e-03
[3,] 5.932939e-03 5.820615e-03 5.311060e-03 4.999840e-03 4.552083e-03
[4,] 1.831140e-02 1.827400e-02 1.823770e-02 1.806690e-02 1.805980e-02
[5,] 3.317082e-04 2.642613e-04 1.904421e-04 1.837099e-04 1.579229e-04
[6,] 8.831307e-04 8.174523e-04 5.648726e-04 4.785746e-04 4.607887e-04
[7,] 3.120427e-05 3.050631e-05 3.048372e-05 2.902448e-05 2.747717e-05
[8,] 9.034509e-05 8.145823e-05 5.293577e-05 4.785256e-05 3.861252e-05
[9,] 5.571874e-05 5.400771e-05 4.992098e-05 4.906107e-05 4.744776e-05
[10,] 2.653716e-04 2.385979e-04 1.602397e-04 1.476508e-04 1.231530e-04
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
fit1$alpha [,1] [,2] [,3] [,4] [,5]
[1,] 3.733407e-03 5.430455e-04 2.356425e-04 1.073212e-03 8.237542e-04
[2,] 2.424021e-03 6.506266e-03 2.295071e-02 1.548257e-02 1.108781e-02
[3,] 6.147715e-02 2.034739e-02 9.567831e-03 1.022009e-02 1.607457e-02
[4,] 2.044699e-02 1.805980e-02 2.075692e-02 1.906522e-02 1.868427e-02
[5,] 1.837099e-04 8.588893e-04 1.103063e-02 2.916343e-03 1.675486e-03
[6,] 4.607887e-04 1.644004e-03 1.320637e-02 6.156274e-03 3.455868e-03
[7,] 4.202234e-05 3.048372e-05 6.419446e-05 3.282662e-05 3.241099e-05
[8,] 4.785256e-05 2.951152e-04 2.610476e-03 8.268222e-04 7.521447e-04
[9,] 3.065673e-04 6.861870e-05 5.571874e-05 1.135070e-04 9.488316e-05
[10,] 1.476508e-04 8.091802e-04 6.980427e-03 2.154703e-03 1.794211e-03
[,6] [,7] [,8] [,9] [,10]
[1,] 9.122852e-04 1.376218e-04 5.148869e-04 0.0001598658 1.281744e-03
[2,] 5.359536e-03 3.182917e-02 3.464566e-02 0.0729481312 6.296718e-03
[3,] 2.153259e-02 7.638156e-03 1.050394e-02 0.0045520833 2.440685e-02
[4,] 1.906294e-02 2.011776e-02 2.148999e-02 0.0260326695 1.989761e-02
[5,] 6.978820e-04 5.389096e-03 2.686396e-02 0.0544716657 3.317082e-04
[6,] 1.633017e-03 8.835741e-03 2.358777e-02 0.0326125133 9.487655e-04
[7,] 2.902448e-05 7.790874e-05 1.048597e-04 0.0001776357 3.483076e-05
[8,] 1.970664e-04 3.803361e-03 1.610996e-02 0.0202086171 1.192129e-04
[9,] 9.894161e-05 5.400771e-05 7.366384e-05 0.0000703281 1.698530e-04
[10,] 5.380368e-04 1.008288e-02 3.281555e-02 0.0625817470 3.541724e-04
[,11] [,12] [,13] [,14] [,15]
[1,] 0.918999799 1.824226e-04 0.0020614756 1.201159e-04 2.587231e-04
[2,] 0.119564385 3.705943e-02 0.0101252973 3.922039e-02 2.232681e-02
[3,] 0.009846491 7.792635e-03 0.0222529110 4.999840e-03 5.932939e-03
[4,] 0.020860202 2.374251e-02 0.0183361450 2.264862e-02 1.978416e-02
[5,] 0.428392692 1.243980e-02 0.0015023095 7.843161e-03 1.397575e-02
[6,] 0.544429367 1.839086e-02 0.0031304713 1.353971e-02 2.193766e-02
[7,] 0.996635416 9.206334e-05 0.0000336982 5.171947e-05 4.053116e-05
[8,] 0.829505745 9.690895e-03 0.0005033484 3.852764e-03 2.947736e-03
[9,] 0.992445490 6.255823e-05 0.0001549210 4.744776e-05 4.992098e-05
[10,] 0.585410309 2.228639e-02 0.0013593014 1.102541e-02 6.237541e-03
[,16] [,17] [,18] [,19] [,20]
[1,] 9.240354e-03 2.878373e-04 8.518280e-04 3.515594e-04 1.892862e-03
[2,] 5.130125e-03 1.124109e-02 1.424258e-02 4.564223e-02 4.981906e-03
[3,] 3.362618e-02 1.079819e-02 1.190851e-02 8.733567e-03 3.488312e-02
[4,] 1.831140e-02 1.879002e-02 1.877770e-02 2.085458e-02 1.883386e-02
[5,] 5.152577e-04 1.277679e-03 3.462716e-03 3.822821e-02 6.104045e-04
[6,] 1.177888e-03 2.961220e-03 7.040801e-03 3.750688e-02 1.249892e-03
[7,] 4.302276e-05 3.050631e-05 3.227644e-05 1.305569e-04 3.265741e-05
[8,] 1.542288e-04 4.100610e-04 1.118922e-03 2.289997e-02 1.616728e-04
[9,] 4.369227e-04 5.616771e-05 9.411921e-05 6.954759e-05 1.619678e-04
[10,] 4.456891e-04 1.169062e-03 2.755587e-03 5.001218e-02 4.646112e-04
[,21] [,22] [,23] [,24] [,25]
[1,] 3.441030e-04 1.938277e-03 8.762018e-04 7.927720e-04 7.729285e-04
[2,] 6.022080e-02 2.284932e-02 2.389300e-02 3.481435e-03 2.767799e-02
[3,] 5.311060e-03 1.206381e-02 1.159272e-02 3.278618e-02 1.285917e-02
[4,] 2.339316e-02 1.976795e-02 1.806690e-02 2.012627e-02 1.967518e-02
[5,] 2.719308e-02 1.232105e-02 6.447835e-03 3.819366e-04 6.281893e-03
[6,] 3.044338e-02 1.641336e-02 1.057700e-02 8.831307e-04 1.401207e-02
[7,] 4.354801e-05 4.158203e-05 3.561987e-05 2.747717e-05 4.268603e-05
[8,] 5.274808e-03 4.668549e-03 2.389867e-03 9.034509e-05 3.012236e-03
[9,] 6.545202e-05 1.339847e-04 1.001624e-04 9.109178e-05 8.290628e-05
[10,] 1.582705e-02 1.012314e-02 5.697804e-03 2.653716e-04 6.488257e-03
[,26] [,27] [,28] [,29] [,30]
[1,] 4.777203e-03 3.135194e-04 5.850497e-04 2.145967e-04 3.256584e-03
[2,] 3.789132e-03 1.634102e-02 1.067606e-02 3.031351e-02 6.094298e-03
[3,] 5.154482e-02 1.053209e-02 1.523835e-02 8.218159e-03 2.529966e-02
[4,] 2.046430e-02 2.040658e-02 1.880452e-02 2.034664e-02 1.970331e-02
[5,] 4.885139e-04 3.755166e-03 3.043067e-03 1.513247e-02 1.052791e-03
[6,] 9.488152e-04 6.223758e-03 5.118461e-03 1.613733e-02 1.865190e-03
[7,] 3.600418e-05 3.928663e-05 3.266831e-05 7.351158e-05 4.129733e-05
[8,] 1.262766e-04 1.344795e-03 8.204985e-04 6.294084e-03 2.455660e-04
[9,] 2.410195e-04 6.163367e-05 6.968144e-05 5.739052e-05 2.739026e-04
[10,] 3.529668e-04 3.848784e-03 2.152688e-03 1.494970e-02 6.939954e-04
[,31] [,32] [,33] [,34] [,35]
[1,] 2.355420e-03 1.471429e-04 0.0018060833 4.771497e-04 2.363220e-04
[2,] 9.366180e-03 2.341051e-02 0.0092274244 2.631295e-02 1.152119e-02
[3,] 2.092026e-02 8.785263e-03 0.0104330656 8.936683e-03 1.439417e-02
[4,] 1.922791e-02 2.004417e-02 0.0182377004 2.106524e-02 1.911693e-02
[5,] 1.398343e-03 8.837923e-03 0.0016061886 1.775095e-02 1.580083e-03
[6,] 2.795346e-03 9.512305e-03 0.0043459180 1.778223e-02 2.525960e-03
[7,] 3.551708e-05 9.162115e-05 0.0000340980 5.252856e-05 3.663299e-05
[8,] 4.599117e-04 4.124213e-03 0.0003493510 5.437893e-03 6.591014e-04
[9,] 1.857029e-04 5.679881e-05 0.0001617269 6.536349e-05 5.597475e-05
[10,] 1.262696e-03 1.119926e-02 0.0008338493 1.333483e-02 2.012201e-03
[,36] [,37] [,38] [,39] [,40]
[1,] 2.611610e-04 6.681428e-03 9.561250e-04 1.425524e-03 0.0022433325
[2,] 3.644532e-02 4.555958e-03 2.382660e-03 2.277992e-02 0.0134964184
[3,] 8.982537e-03 5.309002e-02 5.891329e-02 1.196145e-02 0.0222720629
[4,] 2.115388e-02 1.859249e-02 2.230998e-02 1.985744e-02 0.0198657540
[5,] 7.132038e-03 1.579229e-04 5.984956e-04 2.151709e-03 0.0036093938
[6,] 1.565441e-02 4.785746e-04 8.174523e-04 6.198470e-03 0.0058168916
[7,] 4.817670e-05 8.139799e-05 3.120427e-05 3.573549e-05 0.0000392502
[8,] 3.455704e-03 3.861252e-05 1.638034e-04 1.072626e-03 0.0016074089
[9,] 5.810671e-05 1.095586e-03 7.152391e-05 1.396832e-04 0.0001506845
[10,] 7.955547e-03 1.231530e-04 4.420603e-04 2.683458e-03 0.0035591474
[,41] [,42] [,43] [,44] [,45]
[1,] 3.237195e-04 1.566959e-03 0.0005363286 1.319524e-03 1.834289e-04
[2,] 2.032948e-02 6.632515e-03 0.0222596919 1.101837e-02 2.888766e-02
[3,] 9.676966e-03 2.341794e-02 0.0141958136 1.531304e-02 5.820615e-03
[4,] 1.942752e-02 1.920953e-02 0.0189631936 1.981257e-02 2.143084e-02
[5,] 5.288201e-03 8.842818e-04 0.2512457454 8.756149e-04 5.368551e-03
[6,] 7.491466e-03 1.599993e-03 0.0577160692 2.154546e-03 1.186261e-02
[7,] 4.412812e-05 3.262573e-05 0.0009915338 3.507824e-05 4.112050e-05
[8,] 2.831000e-03 2.789760e-04 0.0351643177 3.029593e-04 2.713871e-03
[9,] 6.179552e-05 1.391862e-04 0.0001718652 1.481317e-04 4.906107e-05
[10,] 7.306337e-03 7.919996e-04 0.0789575892 9.567253e-04 6.449254e-03
[,46] [,47] [,48] [,49] [,50]
[1,] 3.776749e-03 8.345871e-03 2.802130e-03 3.454518e-03 3.567408e-03
[2,] 3.091897e-03 4.787654e-03 6.898339e-03 6.775335e-03 5.419148e-03
[3,] 5.228391e-02 5.211539e-02 2.496239e-02 2.010942e-02 4.087462e-02
[4,] 2.170864e-02 1.874820e-02 1.902369e-02 1.862216e-02 1.827400e-02
[5,] 1.904421e-04 7.579992e-04 6.697418e-04 2.642613e-04 8.669562e-04
[6,] 5.648726e-04 1.220743e-03 2.033345e-03 1.110771e-03 1.789669e-03
[7,] 4.578446e-05 3.855902e-05 3.390341e-05 5.678118e-05 3.199689e-05
[8,] 5.293577e-05 2.264010e-04 2.111803e-04 8.145823e-05 2.852825e-04
[9,] 3.207675e-04 3.917363e-04 1.805087e-04 4.578005e-04 1.756190e-04
[10,] 1.602397e-04 6.435941e-04 5.339865e-04 2.385979e-04 7.310933e-04\(\beta_0 = 1, \beta_1 = 3\)
dat[[2]]$y <- with(dat[[2]], Surv(surT, status == 2))
X = as.matrix(dat[[2]][, c(2:(p+1))])
y = dat[[2]]$y
# IBSS of susie
t1 <- proc.time()
fit2 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/p, p), tol = 1e-3, maxit = 500, estimate_intercept = TRUE, ser_function = fit_coxph)5.214 sec elapsedt2 <- proc.time()
t2 - t1 user system elapsed
5.077 0.067 5.253
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
fit2$alpha [,1] [,2] [,3] [,4] [,5] [,6]
[1,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[2,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[3,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[4,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[5,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[6,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[7,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[8,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[9,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[10,] 1 1.703997e-25 5.945257e-25 1.755176e-25 2.288233e-25 1.925916e-25
[,7] [,8] [,9] [,10] [,11]
[1,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[2,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[3,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[4,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[5,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[6,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[7,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[8,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[9,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[10,] 5.36994e-25 3.17988e-25 3.553885e-25 3.521652e-25 3.119528e-24
[,12] [,13] [,14] [,15] [,16]
[1,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[2,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[3,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[4,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[5,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[6,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[7,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[8,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[9,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[10,] 2.389125e-25 2.004097e-25 1.27905e-24 1.499504e-25 2.233957e-25
[,17] [,18] [,19] [,20] [,21]
[1,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[2,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[3,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[4,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[5,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[6,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[7,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[8,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[9,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[10,] 1.639732e-25 1.773557e-25 2.724362e-25 1.837206e-25 2.497231e-25
[,22] [,23] [,24] [,25] [,26]
[1,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[2,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[3,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[4,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[5,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[6,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[7,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[8,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[9,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[10,] 2.040575e-25 2.688967e-25 1.760976e-25 2.269901e-25 2.485756e-25
[,27] [,28] [,29] [,30] [,31]
[1,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[2,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[3,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[4,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[5,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[6,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[7,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[8,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[9,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[10,] 1.659991e-25 2.987126e-25 3.808359e-25 1.991809e-25 1.852187e-25
[,32] [,33] [,34] [,35] [,36]
[1,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[2,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[3,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[4,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[5,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[6,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[7,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[8,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[9,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[10,] 7.54603e-25 2.572113e-25 2.262302e-25 4.615932e-25 3.172465e-25
[,37] [,38] [,39] [,40] [,41]
[1,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[2,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[3,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[4,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[5,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[6,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[7,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[8,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[9,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[10,] 1.674966e-25 2.50397e-25 2.041832e-25 2.388949e-25 3.086044e-25
[,42] [,43] [,44] [,45] [,46]
[1,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[2,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[3,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[4,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[5,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[6,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[7,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[8,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[9,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[10,] 1.825289e-25 3.22661e-24 1.782184e-25 7.896422e-25 1.758257e-24
[,47] [,48] [,49] [,50]
[1,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[2,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[3,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[4,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[5,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[6,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[7,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[8,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[9,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25
[10,] 1.830873e-25 4.541854e-25 4.223712e-25 2.048776e-25dat[[3]]$y <- with(dat[[3]], Surv(surT, status == 2))
X = as.matrix(dat[[3]][, c(2:(p+1))])
y = dat[[3]]$y
# IBSS of susie
t1 <- proc.time()
fit3 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/p, p), tol = 1e-3, maxit = 500, estimate_intercept = TRUE, ser_function = fit_coxph)114.5 sec elapsedt2 <- proc.time()
t2 - t1 user system elapsed
111.469 1.580 114.533 t(apply(fit3$alpha, 1, function(x) sort(x, decreasing = TRUE))) [,1] [,2] [,3] [,4] [,5]
[1,] 0.99999698 1.052155e-06 4.521763e-07 3.791215e-07 3.688574e-07
[2,] 0.04443501 4.139252e-02 3.742983e-02 3.220094e-02 2.857274e-02
[3,] 0.04436356 4.154946e-02 3.756040e-02 3.225919e-02 2.864139e-02
[4,] 0.04432799 4.162538e-02 3.762326e-02 3.228693e-02 2.867464e-02
[5,] 0.04435819 4.155399e-02 3.756318e-02 3.225975e-02 2.864391e-02
[6,] 0.04446539 4.130870e-02 3.735780e-02 3.216708e-02 2.853727e-02
[7,] 0.04463249 4.092365e-02 3.703517e-02 3.201986e-02 2.836848e-02
[8,] 0.92633850 1.367061e-02 1.322881e-02 7.705824e-03 5.771116e-03
[9,] 0.83350885 3.348414e-02 2.173540e-02 2.169557e-02 1.381444e-02
[10,] 0.04459936 4.107726e-02 3.716962e-02 3.208576e-02 2.842870e-02
[,6] [,7] [,8] [,9] [,10]
[1,] 1.063574e-07 9.170682e-08 8.482000e-08 7.789812e-08 7.787209e-08
[2,] 2.809040e-02 2.703073e-02 2.629240e-02 2.623714e-02 2.548382e-02
[3,] 2.817898e-02 2.707243e-02 2.636357e-02 2.630041e-02 2.553564e-02
[4,] 2.822183e-02 2.709351e-02 2.639781e-02 2.633078e-02 2.556036e-02
[5,] 2.818167e-02 2.707741e-02 2.636494e-02 2.630149e-02 2.553598e-02
[6,] 2.804340e-02 2.701702e-02 2.625283e-02 2.620160e-02 2.545350e-02
[7,] 2.782602e-02 2.691994e-02 2.607692e-02 2.604442e-02 2.532393e-02
[8,] 4.622791e-03 4.122711e-03 3.444368e-03 3.368677e-03 2.588945e-03
[9,] 9.078574e-03 6.196018e-03 4.853759e-03 4.756574e-03 4.133717e-03
[10,] 2.790948e-02 2.692429e-02 2.614984e-02 2.610961e-02 2.538195e-02
[,11] [,12] [,13] [,14] [,15]
[1,] 4.892535e-08 3.776524e-08 3.320419e-08 2.748700e-08 2.508287e-08
[2,] 2.453986e-02 2.169659e-02 2.118537e-02 2.028791e-02 1.991780e-02
[3,] 2.456731e-02 2.170575e-02 2.119356e-02 2.028942e-02 1.993350e-02
[4,] 2.458057e-02 2.171013e-02 2.119738e-02 2.029025e-02 1.994108e-02
[5,] 2.456838e-02 2.170591e-02 2.119343e-02 2.028995e-02 1.993401e-02
[6,] 2.452569e-02 2.169142e-02 2.118008e-02 2.028818e-02 1.990953e-02
[7,] 2.445715e-02 2.166829e-02 2.115860e-02 2.028478e-02 1.987079e-02
[8,] 1.789485e-03 1.485237e-03 1.193223e-03 1.162873e-03 1.125601e-03
[9,] 4.011405e-03 3.939442e-03 3.716092e-03 3.661610e-03 2.937007e-03
[10,] 2.448385e-02 2.167802e-02 2.116969e-02 2.028289e-02 1.988524e-02
[,16] [,17] [,18] [,19] [,20]
[1,] 2.280982e-08 1.956384e-08 1.939523e-08 1.707932e-08 9.872685e-09
[2,] 1.962502e-02 1.917252e-02 1.855645e-02 1.835742e-02 1.808267e-02
[3,] 1.962055e-02 1.916672e-02 1.848118e-02 1.836582e-02 1.806021e-02
[4,] 1.961837e-02 1.916388e-02 1.844508e-02 1.836971e-02 1.804940e-02
[5,] 1.962045e-02 1.916653e-02 1.847947e-02 1.836565e-02 1.805986e-02
[6,] 1.962743e-02 1.917554e-02 1.859793e-02 1.835192e-02 1.809534e-02
[7,] 1.963794e-02 1.918936e-02 1.878641e-02 1.832964e-02 1.815066e-02
[8,] 1.078771e-03 7.863945e-04 7.811649e-04 7.793910e-04 6.400288e-04
[9,] 2.722111e-03 1.769252e-03 1.712252e-03 1.412523e-03 1.297458e-03
[10,] 1.963354e-02 1.918458e-02 1.870874e-02 1.834122e-02 1.812647e-02
[,21] [,22] [,23] [,24] [,25]
[1,] 8.975284e-09 8.220208e-09 7.095732e-09 6.725587e-09 4.910950e-09
[2,] 1.771616e-02 1.764584e-02 1.763599e-02 1.726930e-02 1.719357e-02
[3,] 1.768678e-02 1.765772e-02 1.763319e-02 1.726446e-02 1.719536e-02
[4,] 1.767268e-02 1.766332e-02 1.763174e-02 1.726209e-02 1.719614e-02
[5,] 1.768623e-02 1.765760e-02 1.763281e-02 1.726426e-02 1.719514e-02
[6,] 1.773256e-02 1.763841e-02 1.763676e-02 1.727173e-02 1.719198e-02
[7,] 1.780552e-02 1.764275e-02 1.760811e-02 1.728327e-02 1.718696e-02
[8,] 5.789253e-04 4.731607e-04 4.591056e-04 4.527058e-04 3.487912e-04
[9,] 1.229887e-03 1.186325e-03 1.124523e-03 1.121907e-03 1.105537e-03
[10,] 1.777453e-02 1.764286e-02 1.762293e-02 1.727925e-02 1.719021e-02
[,26] [,27] [,28] [,29] [,30]
[1,] 4.359662e-09 4.114918e-09 2.878084e-09 2.286150e-09 2.286049e-09
[2,] 1.709352e-02 1.706796e-02 1.690906e-02 1.670320e-02 1.637994e-02
[3,] 1.709357e-02 1.704394e-02 1.688009e-02 1.669816e-02 1.636479e-02
[4,] 1.709362e-02 1.703243e-02 1.686617e-02 1.669573e-02 1.635751e-02
[5,] 1.709371e-02 1.704358e-02 1.687951e-02 1.669803e-02 1.636449e-02
[6,] 1.709382e-02 1.708155e-02 1.692518e-02 1.670592e-02 1.638837e-02
[7,] 1.714128e-02 1.709386e-02 1.699722e-02 1.671830e-02 1.642615e-02
[8,] 3.296368e-04 2.142778e-04 2.129617e-04 1.882316e-04 1.432112e-04
[9,] 1.050810e-03 1.012467e-03 9.010285e-04 8.981399e-04 8.382903e-04
[10,] 1.711564e-02 1.709300e-02 1.696669e-02 1.671269e-02 1.641044e-02
[,31] [,32] [,33] [,34] [,35]
[1,] 2.009247e-09 1.990324e-09 1.890739e-09 1.743024e-09 1.472155e-09
[2,] 1.636796e-02 1.628005e-02 1.624300e-02 1.608356e-02 1.606931e-02
[3,] 1.635187e-02 1.624958e-02 1.621377e-02 1.605409e-02 1.602636e-02
[4,] 1.634414e-02 1.623492e-02 1.619969e-02 1.603994e-02 1.600577e-02
[5,] 1.635159e-02 1.624886e-02 1.621307e-02 1.605346e-02 1.602549e-02
[6,] 1.637696e-02 1.629671e-02 1.625898e-02 1.609985e-02 1.609320e-02
[7,] 1.641665e-02 1.637226e-02 1.633134e-02 1.620067e-02 1.617334e-02
[8,] 1.427012e-04 1.284169e-04 7.811385e-05 7.608845e-05 6.859669e-05
[9,] 7.149877e-04 6.938952e-04 6.679215e-04 6.672474e-04 6.257890e-04
[10,] 1.639992e-02 1.634147e-02 1.630181e-02 1.615561e-02 1.614285e-02
[,36] [,37] [,38] [,39] [,40]
[1,] 1.427190e-09 9.416325e-10 8.647769e-10 5.951679e-10 5.181345e-10
[2,] 1.596913e-02 1.595689e-02 1.588084e-02 1.572801e-02 1.568565e-02
[3,] 1.594671e-02 1.594426e-02 1.585002e-02 1.567643e-02 1.566843e-02
[4,] 1.594177e-02 1.593228e-02 1.583523e-02 1.566008e-02 1.565168e-02
[5,] 1.594634e-02 1.594364e-02 1.584943e-02 1.567528e-02 1.566781e-02
[6,] 1.598264e-02 1.596214e-02 1.589801e-02 1.575643e-02 1.569459e-02
[7,] 1.604408e-02 1.598696e-02 1.597473e-02 1.588518e-02 1.573681e-02
[8,] 6.855061e-05 5.918835e-05 5.752368e-05 4.495573e-05 3.465002e-05
[9,] 6.168079e-04 5.691247e-04 5.449257e-04 4.826782e-04 4.743063e-04
[10,] 1.601931e-02 1.597766e-02 1.594243e-02 1.583224e-02 1.572137e-02
[,41] [,42] [,43] [,44] [,45]
[1,] 5.038005e-10 4.434817e-10 4.051437e-10 3.886707e-10 1.647665e-10
[2,] 1.554825e-02 1.547688e-02 1.537992e-02 1.528228e-02 1.520010e-02
[3,] 1.554534e-02 1.542838e-02 1.534190e-02 1.525063e-02 1.517178e-02
[4,] 1.554396e-02 1.540511e-02 1.532363e-02 1.523543e-02 1.516394e-02
[5,] 1.554538e-02 1.542727e-02 1.534108e-02 1.524994e-02 1.517140e-02
[6,] 1.555006e-02 1.550356e-02 1.540093e-02 1.529976e-02 1.522659e-02
[7,] 1.562479e-02 1.555723e-02 1.549555e-02 1.537876e-02 1.534568e-02
[8,] 3.436165e-05 3.347509e-05 2.811367e-05 2.204717e-05 9.321038e-06
[9,] 4.530960e-04 4.074441e-04 3.690268e-04 3.149585e-04 2.945255e-04
[10,] 1.557500e-02 1.555378e-02 1.545637e-02 1.534607e-02 1.529565e-02
[,46] [,47] [,48] [,49] [,50]
[1,] 9.665311e-11 7.985264e-11 6.460685e-11 5.430768e-11 1.432724e-11
[2,] 1.518805e-02 1.480966e-02 1.467105e-02 1.459447e-02 1.338326e-02
[3,] 1.515251e-02 1.481452e-02 1.466007e-02 1.455842e-02 1.336782e-02
[4,] 1.512970e-02 1.481667e-02 1.465474e-02 1.454112e-02 1.336044e-02
[5,] 1.515156e-02 1.481392e-02 1.465965e-02 1.455766e-02 1.336767e-02
[6,] 1.519694e-02 1.480540e-02 1.467669e-02 1.461446e-02 1.339217e-02
[7,] 1.523710e-02 1.479221e-02 1.470451e-02 1.470356e-02 1.343067e-02
[8,] 9.187843e-06 7.408400e-06 5.722453e-06 5.528439e-06 5.116560e-07
[9,] 2.675404e-04 2.647668e-04 2.561531e-04 2.319128e-04 1.777891e-04
[10,] 1.522077e-02 1.480469e-02 1.469406e-02 1.466702e-02 1.341360e-02
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
\(\beta_0 = 1, \beta_1 = 3, \beta_2 = 1.5\) and \(cor=0.9\).
dat[[4]]$y <- with(dat[[4]], Surv(surT, status == 2))
X = as.matrix(dat[[4]][, c(2:(p+1))])
y = dat[[4]]$y
# IBSS of susie
t1 <- proc.time()
fit4 <- ibss_from_ser(X, y, L = 10, prior_variance = 1., prior_weights = rep(1/p, p), tol = 1e-3, maxit = 500, estimate_intercept = TRUE, ser_function = fit_coxph)59.407 sec elapsedt2 <- proc.time()
t2 - t1 user system elapsed
37.118 0.765 59.411
Warning: The above code chunk cached its results, but it won’t be re-run if previous chunks it depends on are updated. If you need to use caching, it is highly recommended to also set knitr::opts_chunk$set(autodep = TRUE) at the top of the file (in a chunk that is not cached). Alternatively, you can customize the option dependson for each individual chunk that is cached. Using either autodep or dependson will remove this warning. See the knitr cache options for more details.
fit4$alpha [,1] [,2] [,3] [,4] [,5]
[1,] 1.000000e+00 5.462073e-15 5.878062e-15 7.908404e-13 3.127903e-13
[2,] 1.218094e-08 9.999294e-01 5.309247e-08 8.740058e-08 2.705689e-08
[3,] 3.570948e-02 2.492915e-02 1.878042e-02 1.531881e-02 2.510584e-02
[4,] 3.586999e-02 2.487214e-02 1.867543e-02 1.535015e-02 2.493102e-02
[5,] 3.601484e-02 2.481580e-02 1.857759e-02 1.537833e-02 2.476811e-02
[6,] 3.607353e-02 2.479168e-02 1.853702e-02 1.538973e-02 2.470057e-02
[7,] 3.602649e-02 2.481151e-02 1.856952e-02 1.538060e-02 2.475469e-02
[8,] 3.590179e-02 2.486105e-02 1.865406e-02 1.535633e-02 2.489548e-02
[9,] 3.575831e-02 2.491369e-02 1.874858e-02 1.532830e-02 2.505292e-02
[10,] 3.566541e-02 2.494570e-02 1.880832e-02 1.531010e-02 2.515243e-02
[,6] [,7] [,8] [,9] [,10]
[1,] 4.813312e-13 2.964555e-13 1.268710e-13 3.736764e-15 3.248483e-14
[2,] 1.125053e-07 2.812564e-08 4.376611e-08 1.942261e-07 6.965236e-06
[3,] 1.551654e-02 3.150950e-02 3.160673e-02 1.636298e-02 1.748556e-02
[4,] 1.561327e-02 3.118398e-02 3.122083e-02 1.629688e-02 1.758284e-02
[5,] 1.570246e-02 3.088257e-02 3.086631e-02 1.623514e-02 1.767215e-02
[6,] 1.573916e-02 3.075812e-02 3.072073e-02 1.620951e-02 1.770879e-02
[7,] 1.570974e-02 3.085780e-02 3.083735e-02 1.623009e-02 1.767940e-02
[8,] 1.563274e-02 3.111811e-02 3.114319e-02 1.628349e-02 1.760231e-02
[9,] 1.554582e-02 3.141089e-02 3.148955e-02 1.634306e-02 1.751497e-02
[10,] 1.549043e-02 3.159682e-02 3.171085e-02 1.638067e-02 1.745912e-02
[,11] [,12] [,13] [,14] [,15]
[1,] 5.386879e-11 1.589717e-14 5.108270e-13 1.322122e-13 4.482215e-15
[2,] 2.528000e-06 4.434115e-08 5.476186e-06 3.117614e-09 1.473406e-07
[3,] 4.067655e-02 3.983076e-02 1.962509e-02 2.862063e-02 1.928395e-02
[4,] 4.148250e-02 3.925963e-02 1.963784e-02 2.843965e-02 1.920721e-02
[5,] 4.224721e-02 3.873593e-02 1.964788e-02 2.826890e-02 1.913414e-02
[6,] 4.256798e-02 3.852110e-02 1.965154e-02 2.819753e-02 1.910345e-02
[7,] 4.231046e-02 3.869322e-02 1.964865e-02 2.825469e-02 1.912816e-02
[8,] 4.164821e-02 3.914513e-02 1.964017e-02 2.840231e-02 1.919152e-02
[9,] 4.091941e-02 3.965781e-02 1.962909e-02 2.856558e-02 1.926102e-02
[10,] 4.046486e-02 3.998595e-02 1.962125e-02 2.866774e-02 1.930427e-02
[,16] [,17] [,18] [,19] [,20]
[1,] 8.461749e-13 2.437675e-15 3.151304e-11 2.500937e-13 2.406229e-13
[2,] 1.529384e-06 3.858431e-06 7.734469e-08 9.606877e-07 3.948449e-07
[3,] 2.046743e-02 1.566720e-02 1.570248e-02 2.196391e-02 1.659927e-02
[4,] 2.072471e-02 1.564193e-02 1.575828e-02 2.183533e-02 1.669588e-02
[5,] 2.096617e-02 1.561763e-02 1.580897e-02 2.171570e-02 1.678485e-02
[6,] 2.106674e-02 1.560734e-02 1.582962e-02 2.166619e-02 1.682143e-02
[7,] 2.098604e-02 1.561563e-02 1.581307e-02 2.170594e-02 1.679209e-02
[8,] 2.077709e-02 1.563676e-02 1.576938e-02 2.180932e-02 1.671528e-02
[9,] 2.054490e-02 1.565974e-02 1.571939e-02 2.192509e-02 1.662849e-02
[10,] 2.039888e-02 1.567394e-02 1.568719e-02 2.199839e-02 1.657312e-02
[,21] [,22] [,23] [,24] [,25]
[1,] 1.361300e-14 3.295044e-13 5.074858e-12 2.486214e-14 2.842661e-14
[2,] 2.955632e-07 1.584879e-07 7.812304e-07 3.423552e-07 2.307278e-06
[3,] 1.454931e-02 1.783813e-02 1.614177e-02 1.431941e-02 1.764240e-02
[4,] 1.456969e-02 1.777431e-02 1.614754e-02 1.436978e-02 1.760731e-02
[5,] 1.458773e-02 1.771513e-02 1.615237e-02 1.441550e-02 1.757425e-02
[6,] 1.459495e-02 1.769068e-02 1.615422e-02 1.443410e-02 1.756046e-02
[7,] 1.458918e-02 1.771027e-02 1.615274e-02 1.441919e-02 1.757154e-02
[8,] 1.457366e-02 1.776136e-02 1.614861e-02 1.437980e-02 1.760016e-02
[9,] 1.455548e-02 1.781872e-02 1.614351e-02 1.433470e-02 1.763184e-02
[10,] 1.454355e-02 1.785511e-02 1.614003e-02 1.430562e-02 1.765172e-02
[,26] [,27] [,28] [,29] [,30]
[1,] 5.100067e-14 2.228931e-13 2.222310e-11 1.464532e-13 2.765254e-13
[2,] 1.941303e-06 6.785370e-08 1.867813e-08 1.620806e-07 3.857104e-07
[3,] 1.683264e-02 1.473248e-02 1.646160e-02 2.477907e-02 1.665124e-02
[4,] 1.697244e-02 1.476730e-02 1.646880e-02 2.461047e-02 1.675940e-02
[5,] 1.710258e-02 1.479877e-02 1.647418e-02 2.445268e-02 1.685955e-02
[6,] 1.715649e-02 1.481155e-02 1.647603e-02 2.438713e-02 1.690089e-02
[7,] 1.711325e-02 1.480131e-02 1.647453e-02 2.443980e-02 1.686774e-02
[8,] 1.700072e-02 1.477419e-02 1.646999e-02 2.457630e-02 1.678122e-02
[9,] 1.687478e-02 1.474303e-02 1.646378e-02 2.472837e-02 1.668393e-02
[10,] 1.679509e-02 1.472288e-02 1.645922e-02 2.482424e-02 1.662212e-02
[,31] [,32] [,33] [,34] [,35]
[1,] 2.772113e-12 2.315126e-15 4.059533e-16 1.010916e-12 4.558022e-13
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[6,] 1.654817e-02 2.221730e-02 1.555556e-02 1.792415e-02 1.905106e-02
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[2,] 1.535883e-07 1.473406e-07 1.293445e-07 1.125053e-07 8.740058e-08
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[5,] 1.615438e-02 1.615237e-02 1.580897e-02 1.570246e-02 1.561763e-02
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[7,] 1.615757e-02 1.615274e-02 1.581307e-02 1.570974e-02 1.561563e-02
[8,] 1.614861e-02 1.612353e-02 1.576938e-02 1.563676e-02 1.563274e-02
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[10,] 1.614003e-02 1.605927e-02 1.568719e-02 1.567394e-02 1.549183e-02
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[2,] 4.376611e-08 2.978091e-08 2.812564e-08 2.705689e-08 1.942955e-08
[3,] 1.548843e-02 1.547214e-02 1.538672e-02 1.532269e-02 1.531881e-02
[4,] 1.550905e-02 1.547435e-02 1.544302e-02 1.537503e-02 1.535015e-02
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[7,] 1.554485e-02 1.549922e-02 1.545945e-02 1.542685e-02 1.538060e-02
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[10,] 1.549043e-02 1.546203e-02 1.537145e-02 1.531010e-02 1.530842e-02
[,46] [,47] [,48] [,49] [,50]
[1,] 3.736764e-15 2.437675e-15 2.315126e-15 1.549666e-15 4.059533e-16
[2,] 1.867813e-08 1.230095e-08 1.218094e-08 1.109067e-08 3.117614e-09
[3,] 1.473248e-02 1.454931e-02 1.431941e-02 1.417202e-02 1.271087e-02
[4,] 1.476730e-02 1.456969e-02 1.436978e-02 1.419213e-02 1.275616e-02
[5,] 1.479877e-02 1.458773e-02 1.441550e-02 1.421056e-02 1.279768e-02
[6,] 1.481155e-02 1.459495e-02 1.443410e-02 1.421812e-02 1.281471e-02
[7,] 1.480131e-02 1.458918e-02 1.441919e-02 1.421206e-02 1.280106e-02
[8,] 1.477419e-02 1.457366e-02 1.437980e-02 1.419612e-02 1.276522e-02
[9,] 1.474303e-02 1.455548e-02 1.433470e-02 1.417805e-02 1.272456e-02
[10,] 1.472288e-02 1.454355e-02 1.430562e-02 1.416647e-02 1.269854e-02
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 codetools_0.2-18 evaluate_0.14
[29] memoise_2.0.1 knitr_1.36 callr_3.7.0 fastmap_1.1.0
[33] httpuv_1.6.3 ps_1.6.0 fansi_0.5.0 Rcpp_1.0.8.3
[37] promises_1.2.0.1 cachem_1.0.6 desc_1.4.0 pkgload_1.2.3
[41] jsonlite_1.7.2 fs_1.5.0 digest_0.6.28 stringi_1.7.5
[45] dplyr_1.0.7 processx_3.5.2 rprojroot_2.0.2 grid_4.1.1
[49] cli_3.1.0 tools_4.1.1 magrittr_2.0.1 sass_0.4.4
[53] tibble_3.1.5 crayon_1.4.1 whisker_0.4 pkgconfig_2.0.3
[57] ellipsis_0.3.2 Matrix_1.5-3 prettyunits_1.1.1 rmarkdown_2.11
[61] rstudioapi_0.13 R6_2.5.1 git2r_0.28.0 compiler_4.1.1