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Conclusion: This method doesn’t perform well under the null model, picked one or two variables under the null model. For other 3 scenarios, it performs well.
library(survival)
library(BVSNLP)
Bayesian Variable Selection using Non-Local priors for survival and logistic regression data.
Loading Version 1.1.8
dat = readRDS("./data/sim_dat_censoring.rds")
Main function:
bvs(
X,
resp,
prep = TRUE, # If preprocessing
logT = FALSE,
fixed_cols = NULL,
eff_size = 0.5, # expected effect size
family = c("logistic", "survival"),
hselect = TRUE, # whether the automatic procedure for hyperparameter selection should be run or not.
nlptype = "piMOM",
r = 1,
tau = 0.25,
niter = 30,
mod_prior = c("unif", "beta"),
inseed = NULL,
cplng = FALSE, # This parameter is only used in logistic regression models
ncpu = 4,
parallel.MPI = FALSE
)
Data 1: null model with X independent
# Create survival response
dat[[1]]$y <- with(dat[[1]], Surv(surT, status))
p = 50
X = as.data.frame(dat[[1]][, c(2:(p+1))])
y = dat[[1]]$y
# prep: standardizing non-binary columns and adding intercept
fit1 = bvs(X, resp = y, prep = TRUE, family = "survival", mod_prior = "beta", niter = 100)
# The coefficient vector for the selected model.
# The first component is always for the intercept.
fit1$beta_hat
[1] -0.416437
# The indices of the model with highest posterior probability among all visited
# models, with respect to the columns in the output des_mat.
fit1$HPM
