Hi!
This was actually a call to the community to test this. It is really straightforward to do so. Anyone with Catalina problems should join in resolving the matter.
Now, I went ahead and installed the latest R 4.0.0 RC on my macOS Catalina. As CRAN now provides binary packages pre-build with the new toolchain (based on clang macOS High Sierra toolchain) this promises to solve all problems… and there is a lot of light here:
- IT WORKS TO DOWNLOAD PRE-BUILD RSTAN BASED PACKAGES AND RUN THEM JUST FINE ON CATALINA
- compling a Stan model with
stan_model works fine
- sampling a Stan model with
Sampling works just fine - with/without running chains in parallel
- sampling with my own packages OncoBayes2 + RBesT works only fine when avoiding parallel sampling strangely
- sampling with rstanarm in parallel does work
@bgoodri while there is some oddity going on with OncoBayes2 & RBesT for parallel sampling, I think this all looks very promising!
All I have in my ~/.R/Makevars is:
CFLAGS+=-O3 -march=native -mtune=native
CCFLAGS+=-O3 -march=native -mtune=native
CXXFLAGS+=-O3 -march=native -mtune=native
CXX14FLAGS+=-O3 -march=native -mtune=native
CXX14FLAGS += -DSTAN_THREADS
CXX14FLAGS += -arch x86_64 -ftemplate-depth-256
CXX14FLAGS += -DBOOST_MATH_PROMOTE_DOUBLE_POLICY=false
Below is the R script with the key outputs as produced by my R session:
pkgs <- c("rstan", "RBesT", "OncoBayes2", "rstanarm")
for(p in pkgs) {
if(!require(p, character.only=TRUE))
install.packages(p)
require(p, character.only=TRUE)
}
## ok
example_model("combo2_trial")
options(mc.cores=4)
example_model("combo3")
## fails with
## Error in unserialize(node$con) : error reading from connection
## Calls: <Anonymous> -> slaveLoop -> makeSOCKmaster
## Execution halted
options(mc.cores=1)
## ok
set.seed(34563)
map_AS <- gMAP(cbind(r, n-r) ~ 1 | study,
family=binomial,
data=AS,
tau.dist="HalfNormal", tau.prior=1,
beta.prior=2)
map_AS
forest_plot(map_AS)
save_stan_model <- function(fit) {
model_name <- attr(fit, "model_name")
stan_model_file <- paste0(model_name, ".stan")
cat(paste0(get_stancode(fit), "\n"), file=stan_model_file)
stan_model_file
}
library(rstan)
stan_model_file <- save_stan_model(map_AS$fit)
sm <- stan_model(stan_model_file)
fit_AS <- sampling(sm, data=map_AS$fit.data, control=list(adapt_delta=0.9), cores=4)
print(fit_AS, pars="theta_pred")
## rstanarm - all ok
data(roaches)
roaches$roach1 <- roaches$roach1 / 100
roaches$log_exposure2 <- log(roaches$exposure2)
post <- stan_gamm4(
y ~ s(roach1) + treatment + log_exposure2,
random = ~(1 | senior),
data = roaches,
family = neg_binomial_2,
QR = TRUE,
cores = 2,
chains = 2,
adapt_delta = 0.99,
seed = 12345)
R session:
> sessionInfo()
R version 4.0.0 RC (2020-04-21 r78267)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Catalina 10.15.4
Matrix products: default
BLAS: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRblas.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib
locale:
[1] C/UTF-8/C/C/C/C
attached base packages:
[1] stats graphics datasets grDevices utils methods base
other attached packages:
[1] rstan_2.19.3 ggplot2_3.3.0 StanHeaders_2.19.2 abind_1.4-5
[5] tidyr_1.0.2 dplyr_0.8.5 tibble_3.0.1 OncoBayes2_0.6-4
[9] RBesT_1.6-0 Rcpp_1.0.4.6
loaded via a namespace (and not attached):
[1] tidyselect_1.0.0 purrr_0.3.4 reshape2_1.4.4 tcltk_4.0.0
[5] colorspace_1.4-1 vctrs_0.2.4 stats4_4.0.0 loo_2.2.0
[9] rlang_0.4.5 pkgbuild_1.0.6 pillar_1.4.3 withr_2.2.0
[13] glue_1.4.0 matrixStats_0.56.0 lifecycle_0.2.0 plyr_1.8.6
[17] stringr_1.4.0 munsell_0.5.0 gtable_0.3.0 mvtnorm_1.1-0
[21] codetools_0.2-16 labeling_0.3 inline_0.3.15 callr_3.4.3
[25] ps_1.3.2 parallel_4.0.0 fansi_0.4.1 bayesplot_1.7.1
[29] rstantools_2.0.0 scales_1.1.0 backports_1.1.6 checkmate_2.0.0
[33] farver_2.0.3 gridExtra_2.3 digest_0.6.25 stringi_1.4.6
[37] processx_3.4.2 grid_4.0.0 cli_2.0.2 tools_4.0.0
[41] magrittr_1.5 Formula_1.2-3 crayon_1.3.4 pkgconfig_2.0.3
[45] ellipsis_0.3.0 prettyunits_1.1.1 ggridges_0.5.2 assertthat_0.2.1
[49] R6_2.4.1 compiler_4.0.0
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