The included database contains convenience functions to access data, model code and information for individual posteriors.
To install the package, you can either clone this repository and run the following snippet to install the package or install the package and access it from Github directly (see below).
remotes::install("rpackage/")To install only the R package and then access the posteriors remotely, just install the package from GitHub.
remotes::install_github("MansMeg/posterior", subdir = "rpackage/")To load the package, just run.
library(posteriordb)First we create the posterior database to use, here we can use the database locally (if the repo is cloned).
my_pdb <- pdb_local()The above code requires that your working directory is in the main
folder of the cloned repository. Otherwise we can use the path
argument.
We can also simply use the github repository directly to access the data.
my_pdb <- pdb_github()Independent of the posterior database used, the following works for all.
To list the posteriors available in the database, use
posterior_names().
pos <- posterior_names(my_pdb)
head(pos)## [1] "arK-arK"
## [2] "arma-arma11"
## [3] "eight_schools-eight_schools_centered"
## [4] "eight_schools-eight_schools_noncentered"
## [5] "garch-garch11"
## [6] "gp_pois_regr-gp_pois_regr"
In the same fashion, we can list data and models included in the database as
mn <- model_names(my_pdb)
head(mn)## [1] "arK" "arma11"
## [3] "eight_schools_centered" "eight_schools_noncentered"
## [5] "garch11" "gp_pois_regr"
dn <- data_names(my_pdb)
head(dn)## [1] "arK" "arma" "eight_schools" "garch"
## [5] "gp_pois_regr" "irt_2pl"
We can also get all information on each individual posterior as a tibble with
pos <- posteriors_tbl_df(my_pdb)
head(pos)## # A tibble: 6 x 7
## name model_name gold_standard_na… data_name added_by added_date keywords
## <chr> <chr> <chr> <chr> <chr> <date> <chr>
## 1 arK-arK arK arK-arK arK Mans Ma… 2019-11-19 stan_be…
## 2 arma-ar… arma11 arma-arma11 arma Mans Ma… 2019-11-19 stan_be…
## 3 eight_s… eight_schoo… eight_schools-ei… eight_sc… Mans Ma… 2019-08-12 stan_be…
## 4 eight_s… eight_schoo… eight_schools-ei… eight_sc… Mans Ma… 2019-08-12 stan_be…
## 5 garch-g… garch11 garch-garch11 garch Mans Ma… 2019-11-19 stan_be…
## 6 gp_pois… gp_pois_regr gp_pois_regr-gp_… gp_pois_… Mans Ma… 2019-11-20 stan_be…
The posterior’s name is made up of the data and model fitted to the data. Together, these two uniquely define a posterior distribution. To access a posterior object we can use the model name.
po <- posterior("eight_schools-eight_schools_centered", my_pdb)From the posterior object, we can access data, model code (i.e., Stan code in this case) and a lot of other useful information.
dat <- get_data(po)
str(dat)## List of 3
## $ J : int 8
## $ y : int [1:8] 28 8 -3 7 -1 1 18 12
## $ sigma: int [1:8] 15 10 16 11 9 11 10 18
code <- stan_code(po)
code##
## data {
## int <lower=0> J; // number of schools
## real y[J]; // estimated treatment
## real<lower=0> sigma[J]; // std of estimated effect
## }
## parameters {
## real theta[J]; // treatment effect in school j
## real mu; // hyper-parameter of mean
## real<lower=0> tau; // hyper-parameter of sdv
## }
## model {
## tau ~ cauchy(0, 5); // a non-informative prior
## theta ~ normal(mu, tau);
## y ~ normal(theta , sigma);
## mu ~ normal(0, 5);
## }
We can also access the paths to data after they have been unzipped and
copied to the cache directory set in pdb (the R temp directory by
default).
dfp <- data_file_path(po)
dfp## [1] "/var/folders/9h/yf354vb917z6gr6mz7bfb1d40000gn/T//RtmpEe7cnB/posteriordb_cache/data/data/eight_schools.json"
scfp <- stan_code_file_path(po)
scfp## [1] "/var/folders/9h/yf354vb917z6gr6mz7bfb1d40000gn/T//RtmpEe7cnB/posteriordb_cache/models/stan/eight_schools_centered.stan"
We can also access information regarding the model and the data used to compute the posterior.
data_info(po)## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
model_info(po)## Model: eight_schools_centered
## A centered hiearchical model for 8 schools
Note that the references are referencing to BibTeX items that can be
found in content/references/references.bib.
We can access most of the posterior information as a tbl_df using
tbl <- posteriors_tbl_df(my_pdb)
head(tbl)## # A tibble: 6 x 7
## name model_name gold_standard_na… data_name added_by added_date keywords
## <chr> <chr> <chr> <chr> <chr> <date> <chr>
## 1 arK-arK arK arK-arK arK Mans Ma… 2019-11-19 stan_be…
## 2 arma-ar… arma11 arma-arma11 arma Mans Ma… 2019-11-19 stan_be…
## 3 eight_s… eight_schoo… eight_schools-ei… eight_sc… Mans Ma… 2019-08-12 stan_be…
## 4 eight_s… eight_schoo… eight_schools-ei… eight_sc… Mans Ma… 2019-08-12 stan_be…
## 5 garch-g… garch11 garch-garch11 garch Mans Ma… 2019-11-19 stan_be…
## 6 gp_pois… gp_pois_regr gp_pois_regr-gp_… gp_pois_… Mans Ma… 2019-11-20 stan_be…
In addition, we can also access a list of posteriors with
filter_posteriors(). The filtering function follows dplyr filter
semantics based on the posterior tibble.
pos <- filter_posteriors(my_pdb, data_name == "eight_schools")
pos## [[1]]
## Posterior
##
## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
##
## Model: eight_schools_centered
## A centered hiearchical model for 8 schools
##
## [[2]]
## Posterior
##
## Data: eight_schools
## The 8 schools dataset of Rubin (1981)
##
## Model: eight_schools_noncentered
## A non-centered hiearchical model for 8 schools
To access the gold standard posterior we can use the function
gold_standard_info() to access information on how the gold standard
posterior was computed. To access gold standard posterior draws we use
gold_standard_draws().
gs_info <- gold_standard_info(po)
gs_info## Posterior: eight_schools-eight_schools_noncentered
## Method: stan_sampling (rstan 2.19.2)
## Arguments:
## chains: 10
## iter: 20000
## warmup: 10000
## thin: 10
## seed: 4711
## adapt_delta: 0.95
gsd <- gold_standard_draws(po)
gsd## Posterior: eight_schools-eight_schools_noncentered
## # A tibble: 10 x 10
## variable mean median sd mad q5 q95 rhat ess_bulk ess_tail
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 theta[1] 6.23 5.59 5.72 4.48 -1.56 16.4 1.00 10116. 9946.
## 2 theta[2] 5.03 4.90 4.67 4.16 -2.40 12.8 1.000 9908. 9992.
## 3 theta[3] 3.99 4.26 5.29 4.44 -4.71 11.8 1.000 10329. 9872.
## 4 theta[4] 4.78 4.68 4.84 4.29 -2.80 12.7 1.00 10448. 10079.
## 5 theta[5] 3.68 3.97 4.70 4.19 -4.47 10.9 1.00 10324. 9788.
## 6 theta[6] 3.99 4.13 4.83 4.30 -4.12 11.5 1.000 10195. 9682.
## 7 theta[7] 6.40 5.89 5.12 4.37 -0.888 15.6 1.00 9505. 9991.
## 8 theta[8] 4.89 4.77 5.28 4.44 -3.23 13.5 1.00 9869. 9515.
## 9 mu 4.44 4.47 3.36 3.34 -1.09 10.0 1.000 10374. 9871.
## 10 tau 3.60 2.72 3.26 2.53 0.250 9.92 1.00 9866. 10035.
The function gold_standard_draws() returns a posterior draws_list
object that can be summarized and transformed using the posterior
package.
draws_df <- posterior::as_draws_df(gsd$draws)
head(draws_df)## # A tibble: 6 x 13
## .chain .iteration .draw `theta[1]` `theta[2]` `theta[3]` `theta[4]` `theta[5]`
## <int> <int> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1 1 1 3.83 3.23 4.68 1.89 5.95
## 2 1 2 2 1.99 3.66 -0.296 -0.115 2.16
## 3 1 3 3 -3.57 5.64 3.70 7.68 8.97
## 4 1 4 4 9.40 17.4 8.35 10.9 4.76
## 5 1 5 5 2.01 3.43 3.74 1.32 2.78
## 6 1 6 6 5.95 6.08 7.68 6.25 7.22
## # … with 5 more variables: `theta[6]` <dbl>, `theta[7]` <dbl>,
## # `theta[8]` <dbl>, mu <dbl>, tau <dbl>