This repository is an attempt at diagnosing what is causing stan to run slower on a docker container. Here I have set up four example models.

###Example 1: 8schools 8schools is a simple example taken directly from the stan repository. In total the model estimates 10 parameters.

###Example 2: recruits recruits is a simple count model that also extracts for categorical generated quantity parameters. In total the model estimates 5 parameters + 32 plot random effects.

###Example 3: shrub density shrub density is another count model but with many more parameters & random effects. In total this model has 209 parameters (200 of which are random effect parameters). This model also contains range of generated quantities

###Example 4: shrub density minus generated quantities. is the same as example 3 except with no generated quantities.

##Running Models in Docker First, if you haven't already done so, install docker.

Next open a terminal and run the following:

sudo docker pull jscamac/mwe_stan_docker

The above will download the prebuild image of a virtual machine that contains linux, R and rstan.

Next run the docker image by running:

docker run -it jscamac/mwe_stan_docker

This should run a docker container using th image jscamac/mwe_stan_docker. It should automatically open an R session:

From here the models can be run sequentially by running:

Example 1 8schools

library(rstan)
rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

load('schools_dat.rda')
system.time(fit <-stan(file = '8schools.stan', data = schools_dat, 
                       iter = 2000, pars = c("mu","tau","eta"), chains = 4, seed=12345))

Example 2

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

load('recruit_data.rda')
system.time(fit <-stan(file = 'recruit.stan', data = recruit_data, 
                       iter = 2000, pars =c('alpha','plot_sigma',
                                            'b_otc','b_time','phi',
                                            'pred_count_ctl_t1','pred_count_ctl_t2',
                                            'pred_count_otc_t1', 'pred_count_otc_t2'), chains = 4, seed=12345))

Example 3

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

load('density_data.rda')
system.time(fit <-stan(file = 'density.stan', data = density_data, 
                       iter = 2000, pars =c("alpha_mu", "alpha_sigma","b_transect_sigma",
                                            "b_unburnt","b_severity","b_altitude","b_twi", "b_adult_density",
                                            "pred_count_unburnt","pred_count_burnt","pred_count_severity","pred_count_altitude",
                                            "pred_count_twi","pred_count_adult_density"), chains = 4, seed=12345))

Example 4

rstan_options(auto_write = TRUE)
options(mc.cores = parallel::detectCores())

load('density_data.rda')
system.time(fit <-stan(file = 'density_no_genquants.stan', data = density_data, 
                       iter = 2000, pars =c("alpha_mu", "alpha_sigma","b_transect_sigma",
                                            "b_unburnt","b_severity","b_altitude","b_twi", "b_adult_density"), chains = 4, seed=12345))

These models can also be run locally on your machine by doing the following:

1. Open a terminal and move to a directory you want the repository to be stored then run: git clone git@github.com:jscamac/mwe_stan_docker.git

2. Open an R session within that repository and run the models using the examples above (assuming 'rstan is installed').