Open pavilaalo opened 2 years ago
I am having the same issue using R 4.2.1 for Windows 10 and rstanarm 2.21.3; no stan_surv
function is present in the installation. I uninstalled rstanarm 2.21.3, updated my packages, then reinstalled rstanarm 2.21.2 using the method noted above and got no resolution. Here is the code to reproduce the issue:
> remove.packages('rstanarm')
> remotes::install_github('stan-dev/rstanarm@feature/survival', build_vignettes = FALSE)
> library(rstanarm)
Loading required package: Rcpp
This is rstanarm version 2.21.2
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
> ?stan_surv
No documentation for ‘stan_surv’ in specified packages and libraries:
you could try ‘??stan_surv’
This suggest no stan_surv
function is present. But then I tried this:
> library(survival)
> data(mgus)
> surv <- stan_surv(Surv(futime, death) ~ mspike, data = mgus,
basehaz = "weibull", chains = 4, seed = 1234)
SAMPLING FOR MODEL 'surv' NOW (CHAIN 1).
Chain 1:
Chain 1: Gradient evaluation took 0 seconds
Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 1: Adjust your expectations accordingly!
Chain 1:
Chain 1:
Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 1:
Chain 1: Elapsed Time: 2.387 seconds (Warm-up)
Chain 1: 2.232 seconds (Sampling)
Chain 1: 4.619 seconds (Total)
Chain 1:
SAMPLING FOR MODEL 'surv' NOW (CHAIN 2).
Chain 2:
Chain 2: Gradient evaluation took 0 seconds
Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 2: Adjust your expectations accordingly!
Chain 2:
Chain 2:
Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
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Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 2:
Chain 2: Elapsed Time: 2.497 seconds (Warm-up)
Chain 2: 2.24 seconds (Sampling)
Chain 2: 4.737 seconds (Total)
Chain 2:
SAMPLING FOR MODEL 'surv' NOW (CHAIN 3).
Chain 3:
Chain 3: Gradient evaluation took 0 seconds
Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 3: Adjust your expectations accordingly!
Chain 3:
Chain 3:
Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 3:
Chain 3: Elapsed Time: 2.439 seconds (Warm-up)
Chain 3: 2.132 seconds (Sampling)
Chain 3: 4.571 seconds (Total)
Chain 3:
SAMPLING FOR MODEL 'surv' NOW (CHAIN 4).
Chain 4:
Chain 4: Gradient evaluation took 0 seconds
Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0 seconds.
Chain 4: Adjust your expectations accordingly!
Chain 4:
Chain 4:
Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
Chain 4:
Chain 4: Elapsed Time: 2.609 seconds (Warm-up)
Chain 4: 2.349 seconds (Sampling)
Chain 4: 4.958 seconds (Total)
Chain 4:
Warning messages:
1: In status < 0 || status > 3 :
'length(x) = 241 > 1' in coercion to 'logical(1)'
2: In status < 0 || status > 3 :
'length(x) = 241 > 1' in coercion to 'logical(1)'
3: In status < 0 || status > 3 :
'length(x) = 241 > 1' in coercion to 'logical(1)'
4: In status < 0 || status > 3 :
'length(x) = 241 > 1' in coercion to 'logical(1)'
> summary(surv)
Model Info:
function: stan_surv
baseline hazard: weibull
formula: Surv(futime, death) ~ mspike
algorithm: sampling
sample: 4000 (posterior sample size)
priors: see help('prior_summary')
observations: 241
events: 225 (93.4%)
right censored: 16 (6.6%)
delayed entry: no
Estimates:
mean sd 10% 50% 90%
(Intercept) -10.2 0.6 -11.0 -10.2 -9.3
mspike -0.1 0.2 -0.3 -0.1 0.1
weibull-shape 1.2 0.1 1.1 1.2 1.3
MCMC diagnostics
mcse Rhat n_eff
(Intercept) 0.0 1.0 1265
mspike 0.0 1.0 1571
weibull-shape 0.0 1.0 1144
log-posterior 0.0 1.0 1185
For each parameter, mcse is Monte Carlo standard error, n_eff is a crude measure of effective sample size, and Rhat is the potential scale reduction factor on split chains (at convergence Rhat=1).
Which suggests the model works. Have not tested more complex models yet.
The git repo doesn't have the *.Rd files committed, so you'll need to run roxygen2 to build the documentation files.
git clone -b feature/survival https://github.com/stan-dev/rstanarm.git
R -e 'roxygen2::roxygenize("rstanarm"); devtools::install("rstanarm", build_vignettes = FALSE)'
Hi all, I got same problem and would like to figure out the solution. I used an example to test if the stan_surv works and found the following comment
``` stan_surv_exponential <- stan_surv(formula = Surv(time, status) ~ x,
+ data = leukemia,
+ basehaz = "exp")
Error in stan_surv(formula = Surv(time, status) ~ x, data = leukemia, :
could not find function "stan_surv"
I downloaded the rstanarm-feature-survival from Github branch and installed it to the library locally. The thing is rstanarm-feature-survival is installed in the library, but when I call the rstanarm pacakge, it didn't work because of the name (stan_surv.R is in the rstanarm-feature-survival, but not in the rstanarm). So I copied the stan_surv.R from rstanarm-feature-survival to rstanarm in the library, it didn't work as well. I've also already tried the methods discussed above, but none of them works, I would much appreciate it if anyone could share the solution that works. Thank you!
Summary:
Unable to use stan_surv function.
Description:
After updating the last release of rstanarm (2.21.3) I am not able to find the stan_surv function for Bayesian survival models. I was using them in the previous version 2.21.2 without any problem.
I tried the instructions in https://github.com/stan-dev/rstanarm#survival-analysis-version but it didn't work. It worked using remotes::install_github('stan-dev/rstanarm@feature/survival')
Reproducible Steps:
If applicable, the steps required to reproduce the issue. If you have a reproducible example, please include it.
RStanARM Version:
2.21.3
R Version:
R 4.1.2
Operating System:
macOS Big Sur 11.4