posterior

The posterior R package is intended to provide useful tools for both users and developers of packages for fitting Bayesian models or working with output from Bayesian models. The primary goals of the package are to:

Efficiently convert between many different useful formats of draws (samples) from posterior or prior distributions.
Provide consistent methods for operations commonly performed on draws, for example, subsetting, binding, or mutating draws.
Provide various summaries of draws in convenient formats.
Provide lightweight implementations of state of the art posterior inference diagnostics.

If you are new to posterior we recommend starting with these vignettes:

The posterior R package: an introduction to the package and its main functionality
rvar: The Random Variable Datatype: an overview of the new random variable datatype

Installation

You can install the latest official release version via

install.packages("posterior")

or build the developmental version directly from GitHub via

# install.packages("remotes")
remotes::install_github("stan-dev/posterior")

Examples

Here we offer a few examples of using the package. For a more detailed overview see the vignette The posterior R package.

library("posterior")
#> This is posterior version 1.6.0.9000
#> 
#> Attaching package: 'posterior'
#> The following objects are masked from 'package:stats':
#> 
#>     mad, sd, var
#> The following objects are masked from 'package:base':
#> 
#>     %in%, match

To demonstrate how to work with the posterior package, we will use example posterior draws obtained from the eight schools hierarchical meta-analysis model described in Gelman et al. (2013). Essentially, we have an estimate per school (theta[1] through theta[8]) as well as an overall mean (mu) and standard deviation across schools (tau).

Draws formats

eight_schools_array <- example_draws("eight_schools")
print(eight_schools_array, max_variables = 3)
#> # A draws_array: 100 iterations, 4 chains, and 10 variables
#> , , variable = mu
#> 
#>          chain
#> iteration   1    2     3   4
#>         1 2.0  3.0  1.79 6.5
#>         2 1.5  8.2  5.99 9.1
#>         3 5.8 -1.2  2.56 0.2
#>         4 6.8 10.9  2.79 3.7
#>         5 1.8  9.8 -0.03 5.5
#> 
#> , , variable = tau
#> 
#>          chain
#> iteration   1    2    3   4
#>         1 2.8 2.80  8.7 3.8
#>         2 7.0 2.76  2.9 6.8
#>         3 9.7 0.57  8.4 5.3
#>         4 4.8 2.45  4.4 1.6
#>         5 2.8 2.80 11.0 3.0
#> 
#> , , variable = theta[1]
#> 
#>          chain
#> iteration     1     2    3     4
#>         1  3.96  6.26 13.3  5.78
#>         2  0.12  9.32  6.3  2.09
#>         3 21.25 -0.97 10.6 15.72
#>         4 14.70 12.45  5.4  2.69
#>         5  5.96  9.75  8.2 -0.91
#> 
#> # ... with 95 more iterations, and 7 more variables

The draws for this example come as a draws_array object, that is, an array with dimensions iterations x chains x variables. We can easily transform it to another format, for instance, a data frame with additional meta information.

eight_schools_df <- as_draws_df(eight_schools_array)
print(eight_schools_df)
#> # A draws_df: 100 iterations, 4 chains, and 10 variables
#>      mu tau theta[1] theta[2] theta[3] theta[4] theta[5] theta[6]
#> 1  2.01 2.8     3.96    0.271    -0.74      2.1    0.923      1.7
#> 2  1.46 7.0     0.12   -0.069     0.95      7.3   -0.062     11.3
#> 3  5.81 9.7    21.25   14.931     1.83      1.4    0.531      7.2
#> 4  6.85 4.8    14.70    8.586     2.67      4.4    4.758      8.1
#> 5  1.81 2.8     5.96    1.156     3.11      2.0    0.769      4.7
#> 6  3.84 4.1     5.76    9.909    -1.00      5.3    5.889     -1.7
#> 7  5.47 4.0     4.03    4.151    10.15      6.6    3.741     -2.2
#> 8  1.20 1.5    -0.28    1.846     0.47      4.3    1.467      3.3
#> 9  0.15 3.9     1.81    0.661     0.86      4.5   -1.025      1.1
#> 10 7.17 1.8     6.08    8.102     7.68      5.6    7.106      8.5
#> # ... with 390 more draws, and 2 more variables
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

Different formats are preferable in different situations and hence posterior supports multiple formats and easy conversion between them. For more details on the available formats see help("draws"). All of the formats are essentially base R object classes and can be used as such. For example, a draws_matrix object is just a matrix with a little more consistency and additional methods.

Summarizing draws

Computing summaries of posterior or prior draws and convergence diagnostics for posterior draws is one of the most common tasks when working with Bayesian models fit using Markov Chain Monte Carlo (MCMC) methods. The posterior package provides a flexible interface for this purpose via summarise_draws():

# summarise_draws or summarize_draws
summarise_draws(eight_schools_df)
#> # A tibble: 10 × 10
#>    variable  mean median    sd   mad      q5   q95  rhat ess_bulk ess_tail
#>    <chr>    <dbl>  <dbl> <dbl> <dbl>   <dbl> <dbl> <dbl>    <dbl>    <dbl>
#>  1 mu        4.18   4.16  3.40  3.57  -0.854  9.39  1.02     558.     322.
#>  2 tau       4.16   3.07  3.58  2.89   0.309 11.0   1.01     246.     202.
#>  3 theta[1]  6.75   5.97  6.30  4.87  -1.23  18.9   1.01     400.     254.
#>  4 theta[2]  5.25   5.13  4.63  4.25  -1.97  12.5   1.02     564.     372.
#>  5 theta[3]  3.04   3.99  6.80  4.94 -10.3   11.9   1.01     312.     205.
#>  6 theta[4]  4.86   4.99  4.92  4.51  -3.57  12.2   1.02     695.     252.
#>  7 theta[5]  3.22   3.72  5.08  4.38  -5.93  10.8   1.01     523.     306.
#>  8 theta[6]  3.99   4.14  5.16  4.81  -4.32  11.5   1.02     548.     205.
#>  9 theta[7]  6.50   5.90  5.26  4.54  -1.19  15.4   1.00     434.     308.
#> 10 theta[8]  4.57   4.64  5.25  4.89  -3.79  12.2   1.02     355.     146.

Basically, we get a data frame with one row per variable and one column per summary statistic or convergence diagnostic. The summaries rhat, ess_bulk, and ess_tail are described in Vehtari et al. (2020). We can choose which summaries to compute by passing additional arguments, either functions or names of functions. For instance, if we only wanted the mean and its corresponding Monte Carlo Standard Error (MCSE) we would use:

summarise_draws(eight_schools_df, "mean", "mcse_mean")
#> # A tibble: 10 × 3
#>    variable  mean mcse_mean
#>    <chr>    <dbl>     <dbl>
#>  1 mu        4.18     0.150
#>  2 tau       4.16     0.213
#>  3 theta[1]  6.75     0.319
#>  4 theta[2]  5.25     0.202
#>  5 theta[3]  3.04     0.447
#>  6 theta[4]  4.86     0.189
#>  7 theta[5]  3.22     0.232
#>  8 theta[6]  3.99     0.222
#>  9 theta[7]  6.50     0.250
#> 10 theta[8]  4.57     0.273

For a function to work with summarise_draws, it needs to take a vector or matrix of numeric values and returns a single numeric value or a named vector of numeric values.

Subsetting draws

Another common task when working with posterior (or prior) draws, is subsetting according to various aspects of the draws (iterations, chains, or variables). posterior provides a convenient interface for this purpose via the subset_draws() method. For example, here is the code to extract the first five iterations of the first two chains of the variable mu:

subset_draws(eight_schools_df, variable = "mu", chain = 1:2, iteration = 1:5)
#> # A draws_df: 5 iterations, 2 chains, and 1 variables
#>      mu
#> 1   2.0
#> 2   1.5
#> 3   5.8
#> 4   6.8
#> 5   1.8
#> 6   3.0
#> 7   8.2
#> 8  -1.2
#> 9  10.9
#> 10  9.8
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

The same call to subset_draws() can be used regardless of whether the object is a draws_df, draws_array, draws_list, etc.

Mutating and renaming draws

The magic of having obtained draws from the joint posterior (or prior) distribution of a set of variables is that these draws can also be used to obtain draws from any other variable that is a function of the original variables. That is, if are interested in the posterior distribution of, say, phi = (mu + tau)^2 all we have to do is to perform the transformation for each of the individual draws to obtain draws from the posterior distribution of the transformed variable. This procedure is automated in the mutate_variables method:

x <- mutate_variables(eight_schools_df, phi = (mu + tau)^2)
x <- subset_draws(x, c("mu", "tau", "phi"))
print(x)
#> # A draws_df: 100 iterations, 4 chains, and 3 variables
#>      mu tau   phi
#> 1  2.01 2.8  22.8
#> 2  1.46 7.0  71.2
#> 3  5.81 9.7 240.0
#> 4  6.85 4.8 135.4
#> 5  1.81 2.8  21.7
#> 6  3.84 4.1  62.8
#> 7  5.47 4.0  88.8
#> 8  1.20 1.5   7.1
#> 9  0.15 3.9  16.6
#> 10 7.17 1.8  79.9
#> # ... with 390 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

When we do the math ourselves, we see that indeed for each draw, phi is equal to (mu + tau)^2 (up to rounding two 2 digits for the purpose of printing).

We may also easily rename variables, or even entire vectors of variables via rename_variables, for example:

x <- rename_variables(eight_schools_df, mean = mu, alpha = theta)
variables(x)
#>  [1] "mean"     "tau"      "alpha[1]" "alpha[2]" "alpha[3]" "alpha[4]" "alpha[5]"
#>  [8] "alpha[6]" "alpha[7]" "alpha[8]"

As with all posterior methods, mutate_variables and rename_variables can be used with all draws formats.

Binding draws together

Suppose we have multiple draws objects that we want to bind together:

x1 <- draws_matrix(alpha = rnorm(5), beta = 1)
x2 <- draws_matrix(alpha = rnorm(5), beta = 2)
x3 <- draws_matrix(theta = rexp(5))

Then, we can use the bind_draws method to bind them along different dimensions. For example, we can bind x1 and x3 together along the 'variable' dimension:

x4 <- bind_draws(x1, x3, along = "variable")
print(x4)
#> # A draws_matrix: 5 iterations, 1 chains, and 3 variables
#>     variable
#> draw alpha beta theta
#>    1 -1.10    1 0.102
#>    2 -0.64    1 1.386
#>    3 -0.91    1 0.012
#>    4  1.36    1 0.722
#>    5  0.24    1 2.198

Or, we can bind x1 and x2 together along the 'draw' dimension:

x5 <- bind_draws(x1, x2, along = "draw")
print(x5)
#> # A draws_matrix: 10 iterations, 1 chains, and 2 variables
#>     variable
#> draw  alpha beta
#>   1  -1.102    1
#>   2  -0.643    1
#>   3  -0.912    1
#>   4   1.356    1
#>   5   0.245    1
#>   6   0.429    2
#>   7   0.929    2
#>   8   0.042    2
#>   9   0.118    2
#>   10 -0.168    2

As with all posterior methods, bind_draws can be used with all draws formats.

Converting from regular R objects to draws formats

The eight_schools example already comes in a format natively supported by posterior but we could of course also import the draws from other sources, for example, from common base R objects:

x <- matrix(rnorm(50), nrow = 10, ncol = 5)
colnames(x) <- paste0("V", 1:5)
x <- as_draws_matrix(x)
print(x)
#> # A draws_matrix: 10 iterations, 1 chains, and 5 variables
#>     variable
#> draw     V1    V2     V3     V4     V5
#>   1   0.819 -1.46  0.092 -0.751 -0.549
#>   2   1.675 -0.03  0.962  1.616 -0.624
#>   3   0.084  0.58 -1.509  0.223 -0.180
#>   4   1.798  0.63  0.230 -0.143 -0.764
#>   5  -1.140 -0.34 -1.283 -0.298 -0.769
#>   6   0.099  0.42  0.778 -0.318 -1.344
#>   7   0.576  0.31 -0.950 -0.702  0.067
#>   8   0.649 -3.09 -0.084  0.019  0.674
#>   9  -0.683  0.64 -1.470 -0.609 -0.452
#>   10  0.019 -0.70  1.326  0.222 -0.199

summarise_draws(x, "mean", "sd", "median", "mad")
#> # A tibble: 5 × 5
#>   variable    mean    sd   median   mad
#>   <chr>      <dbl> <dbl>    <dbl> <dbl>
#> 1 V1        0.390  0.927  0.338   0.593
#> 2 V2       -0.305  1.19   0.140   0.719
#> 3 V3       -0.191  1.05   0.00404 1.42 
#> 4 V4       -0.0741 0.691 -0.220   0.615
#> 5 V5       -0.414  0.547 -0.500   0.423

Instead of as_draws_matrix() we also could have just used as_draws(), which attempts to find the closest available format to the input object. In this case this would result in a draws_matrix object either way.

The above matrix example contained only one chain. Multi-chain draws could be stored in base R 3-D array object, which can also be converted to a draws object:

x <- array(data=rnorm(200), dim=c(10, 2, 5))
x <- as_draws_matrix(x)
variables(x) <-  paste0("V", 1:5)
print(x)
#> # A draws_matrix: 10 iterations, 2 chains, and 5 variables
#>     variable
#> draw    V1    V2     V3    V4     V5
#>   1   1.53 -0.18  0.022 -0.15  0.170
#>   2   1.27 -0.48  0.111 -0.61 -0.452
#>   3   1.23 -0.80  0.348  1.03  0.749
#>   4   0.33 -0.67  1.214  1.57 -0.245
#>   5  -1.07  0.76  0.482  0.73 -0.382
#>   6   0.25  0.30  0.815 -3.47 -2.536
#>   7  -0.74 -0.66  1.139  0.36 -0.017
#>   8   0.25  0.78 -1.884 -1.25  1.450
#>   9  -0.24  0.88  0.056 -0.86  0.913
#>   10 -0.48  1.11  0.382  0.23  0.228
#> # ... with 10 more draws

Converting from mcmc objects to draws formats

The coda and rjags packages use mcmc and mcmc.list objects which can also be converted to draws objects:

data(line, package = "coda")
line <- as_draws_df(line)
print(line)
#> # A draws_df: 200 iterations, 2 chains, and 3 variables
#>    alpha  beta sigma
#> 1    7.2 -1.57 11.23
#> 2    3.0  1.50  4.89
#> 3    3.7  0.63  1.40
#> 4    3.3  1.18  0.66
#> 5    3.7  0.49  1.36
#> 6    3.6  0.21  1.04
#> 7    2.7  0.88  1.29
#> 8    3.0  1.09  0.46
#> 9    3.5  1.07  0.63
#> 10   2.1  1.48  0.91
#> # ... with 390 more draws
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}

Contributing to posterior

We welcome contributions! The posterior package is under active development. If you find bugs or have ideas for new features (for us or yourself to implement) please open an issue on GitHub (https://github.com/stan-dev/posterior/issues).

Citing posterior

Developing and maintaining open source software is an important yet often underappreciated contribution to scientific progress. Thus, whenever you are using open source software (or software in general), please make sure to cite it appropriately so that developers get credit for their work.

When using posterior, please cite it as follows:

Bürkner P. C., Gabry J., Kay M., & Vehtari A. (2020). “posterior: Tools for Working with Posterior Distributions.” R package version XXX, <URL: https://mc-stan.org/posterior/>.

When using the MCMC convergence diagnostics rhat, ess_bulk, ess_tail, ess_median, ess_quantile, mcse_median, or mcse_quantile please also cite

Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion). Bayesian Analysis. 16(2), 667–718. doi.org/10.1214/20-BA1221

When using the MCMC convergence diagnostic rhat_nested please also cite

Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L., Vehtari, A., and Gelman, A. (2024). Nested $\widehat{R}$: Assessing the convergence of Markov chain Monte Carlo when running many short chains. Bayesian Analysis, doi:10.1214/24-BA1453.

When using the MCMC convergence diagnostic rstar please also cite

Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers. Bayesian Analysis, 17(2):353-379. doi:10.1214/20-BA1252

When using the Pareto-k diagnostics pareto_khat, pareto_min_ss, pareto_convergence_rate, khat_threshold or pareto_diags, or Pareto smoothing pareto_smooth please also cite

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58.

The same information can be obtained by running citation("posterior").

References

Gelman A., Carlin J. B., Stern H. S., David B. Dunson D. B., Aki Vehtari A., & Rubin D. B. (2013). Bayesian Data Analysis, Third Edition. Chapman and Hall/CRC.

Lambert, B. and Vehtari, A. (2022). $R^*$: A robust MCMC convergence diagnostic with uncertainty using decision tree classifiers. Bayesian Analysis, 17(2):353-379. doi:10.1214/20-BA1252

Margossian, C. C., Hoffman, M. D., Sountsov, P., Riou-Durand, L., Vehtari, A., and Gelman, A. (2024). Nested $\widehat{R}$: Assessing the convergence of Markov chain Monte Carlo when running many short chains. Bayesian Analysis, doi:10.1214/24-BA1453.

Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C. (2021). Rank-normalization, folding, and localization: An improved Rhat for assessing convergence of MCMC (with discussion). Bayesian Analysis. 16(2), 667–718. doi.org/10.1214/20-BA1221

Vehtari, A., Simpson, D., Gelman, A., Yao, Y., and Gabry, J. (2024). Pareto smoothed importance sampling. Journal of Machine Learning Research, 25(72):1-58.

Licensing

The posterior package is licensed under the following licenses:

Code: BSD 3-clause (https://opensource.org/license/bsd-3-clause)
Documentation: CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/)

posterior

Installation

Examples

Draws formats

Summarizing draws

Subsetting draws

Mutating and renaming draws

Binding draws together

Converting from regular R objects to draws formats

Converting from mcmc objects to draws formats

Contributing to posterior

Citing posterior

References

Licensing

Copy Link

Version

Install

Monthly Downloads

Version

License

Homepage

Maintainer

Last Published

Functions in posterior (1.6.1)