Problems interfacing with Stan

[ZachBroeren]

Hi!

Been messing around with positron this afternoon and I really like it so far. I know it's in the beta and not everything is flushed out yet, but I just wanted to draw attention that positron is having some difficulty interfacing with stan. I first was working through a model utilizing the brms package and got the error "Error in sink():" at type = "output". I tried to trouble shoot it by uninstalling and reinstalling rstan from the source to no avail. I also checked it with an older different model that calls on stan directly with the stan() function and in RStudio it is running all fine but in Positron it is breaking as well. I should also note, that the brms model that was breaking in positron worked in RStudio and I downloaded the stan extension to positron to try to fix it. I could definitely be missing something, hence what is causing the problem in positron, but I am curious if any one else is having similar problems.

Either way, I am really excited to see where positron can go!

[juliasilge]

Thanks for trying out Positron @ZachBroeren!

Can you share a small reproducible example of a Stan model that works in RStudio but does not work in Positron?

[ZachBroeren]

Hi, yes, here is a zip file that includes R code, a stan file, as well as some data for that stan file/R code. I just ran this on RStudio and it ran fine, but in positron both did not run.

PositronStan.zip

[juliasilge]

You might want to check out this guidance on how to create a great reprex. Can you try creating a small reproducible example of a model that doesn't work in Positron?

I just tried this and it works for me in Positron:

library(brms)
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.21.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#> 
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#> 
#>     ar

fit1 <- brm(
    count ~ zBase * Trt + (1|patient),
    data = epilepsy, family = poisson(),
    prior = prior(normal(0, 10), class = b) + prior(cauchy(0, 2), class = sd),
    silent = 2
)
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 4.4e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.44 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: 1.154 seconds (Warm-up)
#> Chain 1:                0.799 seconds (Sampling)
#> Chain 1:                1.953 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 2.7e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.27 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)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 1.129 seconds (Warm-up)
#> Chain 2:                0.817 seconds (Sampling)
#> Chain 2:                1.946 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 1.4e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.14 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: 1.148 seconds (Warm-up)
#> Chain 3:                0.817 seconds (Sampling)
#> Chain 3:                1.965 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 1.6e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.16 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: 1.216 seconds (Warm-up)
#> Chain 4:                0.993 seconds (Sampling)
#> Chain 4:                2.209 seconds (Total)
#> Chain 4:

summary(fit1)
#>  Family: poisson 
#>   Links: mu = log 
#> Formula: count ~ zBase * Trt + (1 | patient) 
#>    Data: epilepsy (Number of observations: 236) 
#>   Draws: 4 chains, each with iter = 2000; warmup = 1000; thin = 1;
#>          total post-warmup draws = 4000
#> 
#> Multilevel Hyperparameters:
#> ~patient (Number of levels: 59) 
#>               Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> sd(Intercept)     0.58      0.07     0.47     0.73 1.00      899     1519
#> 
#> Regression Coefficients:
#>            Estimate Est.Error l-95% CI u-95% CI Rhat Bulk_ESS Tail_ESS
#> Intercept      1.78      0.12     1.55     2.00 1.01      565     1164
#> zBase          0.71      0.12     0.47     0.95 1.00      656     1319
#> Trt1          -0.28      0.17    -0.62     0.03 1.00      526     1213
#> zBase:Trt1     0.02      0.16    -0.30     0.34 1.01      751     1298
#> 
#> Draws were sampled using sampling(NUTS). For each parameter, Bulk_ESS
#> and Tail_ESS are effective sample size measures, and Rhat is the potential
#> scale reduction factor on split chains (at convergence, Rhat = 1).

^{Created on 2024-07-01 with reprex v2.1.0}

[ZachBroeren]

Finally figured out the problem - I needed to download the latest version of RTools. It's now running in positron. Thank you for all the help!

[juliasilge]

Glad to hear it! 🙌

[ZachBroeren]

Thank you, I apologize for not using reprex() for the past code - I hope this is more specifically what I should be doing. I re-ran your code with the epilepsy dataset and unfortunately I am getting an error with the model. I've checked out some stan forums where the advice was to uninstall and reinstall "rstan" and "StanHeaders" but that does appear to help here.

library(brms)
#> Loading required package: Rcpp
#> Loading 'brms' package (version 2.21.0). Useful instructions
#> can be found by typing help('brms'). A more detailed introduction
#> to the package is available through vignette('brms_overview').
#>
#> Attaching package: 'brms'
#> The following object is masked from 'package:stats':
#>
#> ar

fit1 <- brm(
count ~ zBase * Trt + (1|patient),
data = epilepsy, family = poisson(),
prior = prior(normal(0, 10), class = b) + prior(cauchy(0, 2), class = sd),
silent = 2
)
#> Error in sink(type = "output") : invalid connection
#> Error in compileCode(f, code, language = language, verbose = verbose): Error in system(paste(MAKE, p1(paste("-f", shQuote(makefiles))), "compilers"), : 'make' not foundCalls: -> .shlib_internal -> systemExecution halted
#> Error in sink(type = "output"): invalid connection

Created on 2024-07-01 with reprex v2.1.0