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.

You can install the latest official release version via

`install.packages("posterior")`

or the latest development version from GitHub via

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

`library("posterior")`

`## This is posterior version 1.4.1`

```
##
## 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, throughout the
rest of this vignette we will use example posterior draws obtained from
the eight schools hierarchical meta-analysis model described in Gelman
et al. (2013). The variables are an estimate per school
(`theta[1]`

through `theta[8]`

) as well as an
overall mean (`mu`

) and standard deviation across schools
(`tau`

).

```
<- example_draws("eight_schools")
eight_schools_array 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 structure of this object is explained in the next section.

Because different formats are preferable in different situations, posterior supports multiple formats and easy conversion between them. The currently supported formats are:

`draws_array`

: An iterations by chains by variables array.`draws_matrix`

: A draws (iterations x chains) by variables array.`draws_df`

: A draws by variables data frame with addition meta columns`.chain`

,`.iteration`

,`.draw`

.`draws_list`

: A list with one sublist per chain. Each sublist is a named list with one vector of iterations per variable.`draws_rvars`

: A list of random variable`rvar`

objects, one per variable. See`vignette("rvar")`

for an introduction to this new data type.

These 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 (e.g., no dropping of
dimensions with one level when indexing) and additional methods. The
exception to this is the `draws_rvars`

format, which contains
`rvar`

objects that behave somewhat like arrays but are
really a unique data type. See the separate vignette on the
`rvar`

and `draws_rvars`

data types for
details.

The draws for our example come as a `draws_array`

object
with 100 iterations, 4 chains, and 10 variables:

`str(eight_schools_array)`

```
## 'draws_array' num [1:100, 1:4, 1:10] 2.01 1.46 5.81 6.85 1.81 ...
## - attr(*, "dimnames")=List of 3
## ..$ iteration: chr [1:100] "1" "2" "3" "4" ...
## ..$ chain : chr [1:4] "1" "2" "3" "4"
## ..$ variable : chr [1:10] "mu" "tau" "theta[1]" "theta[2]" ...
```

Each of the formats has a method `as_draws_<format>`

(e.g., `as_draws_list()`

) for creating an object of the class
from any of the other formats. As a demonstration we can convert the
example `draws_array`

to a `draws_df`

, a data
frame with additional meta information. To convert to a
`draws_df`

we use `as_draws_df()`

.

```
<- as_draws_df(eight_schools_array)
eight_schools_df str(eight_schools_df)
```

```
## draws_df [400 × 13] (S3: draws_df/draws/tbl_df/tbl/data.frame)
## $ mu : num [1:400] 2.01 1.46 5.81 6.85 1.81 ...
## $ tau : num [1:400] 2.77 6.98 9.68 4.79 2.85 ...
## $ theta[1] : num [1:400] 3.962 0.124 21.251 14.7 5.96 ...
## $ theta[2] : num [1:400] 0.271 -0.069 14.931 8.586 1.156 ...
## $ theta[3] : num [1:400] -0.743 0.952 1.829 2.675 3.109 ...
## $ theta[4] : num [1:400] 2.1 7.28 1.38 4.39 1.99 ...
## $ theta[5] : num [1:400] 0.923 -0.062 0.531 4.758 0.769 ...
## $ theta[6] : num [1:400] 1.65 11.26 7.16 8.1 4.66 ...
## $ theta[7] : num [1:400] 3.32 9.62 14.8 9.49 1.21 ...
## $ theta[8] : num [1:400] 4.85 -8.64 -1.74 5.28 -4.54 ...
## $ .chain : int [1:400] 1 1 1 1 1 1 1 1 1 1 ...
## $ .iteration: int [1:400] 1 2 3 4 5 6 7 8 9 10 ...
## $ .draw : int [1:400] 1 2 3 4 5 6 7 8 9 10 ...
```

`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'}
```

`draws`

formatsThe example draws already come in a format natively supported by posterior, but we can of course also import the draws from other sources like common base R objects.

In addition to converting other `draws`

objects to the
`draws_matrix`

format, the `as_draws_matrix()`

function will convert a regular matrix to a
`draws_matrix`

.

```
<- matrix(rnorm(50), nrow = 10, ncol = 5)
x colnames(x) <- paste0("V", 1:5)
<- as_draws_matrix(x)
x print(x)
```

```
## # A draws_matrix: 10 iterations, 1 chains, and 5 variables
## variable
## draw V1 V2 V3 V4 V5
## 1 2.09 0.267 -1.518 0.70 -0.043
## 2 -0.32 1.211 -0.824 -0.51 0.437
## 3 0.30 0.187 1.274 -0.20 0.146
## 4 -0.67 0.271 -1.046 -1.35 1.173
## 5 -0.73 0.687 0.817 0.77 0.836
## 6 0.44 1.800 -0.075 0.57 -0.191
## 7 -1.53 -0.533 -0.589 1.30 1.194
## 8 0.53 1.219 -0.128 2.17 -0.688
## 9 0.21 0.083 0.121 0.37 0.314
## 10 1.17 0.428 -0.995 1.47 -0.526
```

Because the matrix was converted to a `draws_matrix`

, all
of the methods for working with `draws`

objects described in
subsequent sections of this vignette will now be available.

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 the result would be a
`draws_matrix`

object either way.

In addition to the `as_draws_matrix()`

converter function
there is also a `draws_matrix()`

constructor function that
can be used to create draws matrix from multiple vectors.

```
<- draws_matrix(alpha = rnorm(50), beta = rnorm(50))
x print(x)
```

```
## # A draws_matrix: 50 iterations, 1 chains, and 2 variables
## variable
## draw alpha beta
## 1 -0.084 0.82
## 2 -0.183 0.50
## 3 -1.126 0.03
## 4 -0.268 0.32
## 5 -0.361 -0.88
## 6 -0.434 1.17
## 7 -0.324 0.37
## 8 0.712 -1.80
## 9 0.112 -2.26
## 10 1.736 0.28
## # ... with 40 more draws
```

Analogous functions exist for the other draws formats and are used similarly.

`draws`

objectsThe posterior package provides many methods for manipulating draws objects in useful ways. In this section we demonstrate several of the most commonly used methods. These methods, like the other methods in posterior, are available for every supported draws format.

Subsetting `draws`

objects can be done according to
various aspects of the draws (iterations, chains, or variables). The
posterior package provides a convenient interface for this purpose via
`subset_draws()`

. 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)
sub_df str(sub_df)
```

```
## draws_df [10 × 4] (S3: draws_df/draws/tbl_df/tbl/data.frame)
## $ mu : num [1:10] 2.01 1.46 5.81 6.85 1.81 ...
## $ .chain : int [1:10] 1 1 1 1 1 2 2 2 2 2
## $ .iteration: int [1:10] 1 2 3 4 5 1 2 3 4 5
## $ .draw : int [1:10] 1 2 3 4 5 6 7 8 9 10
```

The same call to `subset_draws()`

can be used regardless
of the draws format. For example, here is the same code except replacing
the `draws_df`

object with the `draws_array`

object.

```
<- subset_draws(eight_schools_array, variable = "mu", chain = 1:2, iteration = 1:5)
sub_arr str(sub_arr)
```

```
## 'draws_array' num [1:5, 1:2, 1] 2.01 1.46 5.81 6.85 1.81 ...
## - attr(*, "dimnames")=List of 3
## ..$ iteration: chr [1:5] "1" "2" "3" "4" ...
## ..$ chain : chr [1:2] "1" "2"
## ..$ variable : chr "mu"
```

We can check that these two calls to `subset_draws()`

(the
first with the data frame, the second with the array) produce the same
result.

```
identical(sub_df, as_draws_df(sub_arr))
identical(as_draws_array(sub_df), sub_arr)
```

```
## [1] TRUE
## [1] TRUE
```

It is also possible to use standard R subsetting syntax with
`draws`

objects. The following is equivalent to the use of
`subset_draws()`

with the array above.

`1:5, 1:2, "mu"] eight_schools_array[`

```
## # A draws_array: 5 iterations, 2 chains, and 1 variables
## , , variable = mu
##
## chain
## iteration 1 2
## 1 2.0 3.0
## 2 1.5 8.2
## 3 5.8 -1.2
## 4 6.8 10.9
## 5 1.8 9.8
```

The major difference between how posterior behaves when indexing and
how base R behaves is that posterior will *not* drop dimensions
with only one level. That is, even though there is only one variable
left after subsetting, the result of the subsetting above is still a
`draws_array`

and not a `draws_matrix`

.

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 we 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 handled by
`mutate_variables()`

.

```
<- mutate_variables(eight_schools_df, phi = (mu + tau)^2)
x <- subset_draws(x, c("mu", "tau", "phi"))
x 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'}
```

To rename variables use `rename_variables()`

. Here we
rename the scalar `mu`

to `mean`

and the vector
`theta`

to `alpha`

.

```
# mu is a scalar, theta is a vector
<- rename_variables(eight_schools_df, mean = mu, alpha = theta)
x variables(x)
```

```
## [1] "mean" "tau" "alpha[1]" "alpha[2]" "alpha[3]" "alpha[4]"
## [7] "alpha[5]" "alpha[6]" "alpha[7]" "alpha[8]"
```

In the call to `rename_variables()`

above, `mu`

and `theta`

can be quoted or unquoted.

It is also possible to rename individual elements of non-scalar
parameters, for example we can rename just the first element of
`alpha`

:

```
<- rename_variables(x, a1 = `alpha[1]`)
x variables(x)
```

```
## [1] "mean" "tau" "a1" "alpha[2]" "alpha[3]" "alpha[4]"
## [7] "alpha[5]" "alpha[6]" "alpha[7]" "alpha[8]"
```

The `bind_draws()`

method can be used to combine
`draws`

objects along different dimensions. As an example,
suppose we have several different `draws_matrix`

objects:

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

We can bind `x1`

and `x3`

together along the
`'variable'`

dimension to get a single
`draws_matrix`

with the variables from both `x1`

and `x3`

:

```
<- bind_draws(x1, x3, along = "variable")
x4 print(x4)
```

```
## # A draws_matrix: 5 iterations, 1 chains, and 3 variables
## variable
## draw alpha beta theta
## 1 -0.63 -0.19 0.0089
## 2 -0.33 0.53 0.4641
## 3 -2.33 -0.37 0.3919
## 4 -0.97 -1.06 0.0647
## 5 1.89 -0.43 2.3296
```

Because `x1`

and `x2`

have the same variables,
we can bind them along the `'draw'`

dimension to create a
single `draws_matrix`

with more draws:

```
<- bind_draws(x1, x2, along = "draw")
x5 print(x5)
```

```
## # A draws_matrix: 10 iterations, 1 chains, and 2 variables
## variable
## draw alpha beta
## 1 -0.63 -0.189
## 2 -0.33 0.534
## 3 -2.33 -0.367
## 4 -0.97 -1.058
## 5 1.89 -0.433
## 6 1.14 -1.229
## 7 1.57 0.912
## 8 -0.21 0.018
## 9 0.09 0.028
## 10 1.10 1.345
```

As with all posterior methods, `bind_draws()`

can be used
with all draws formats and depending on the format different dimensions
are available to bind on. For example, we can bind
`draws_array`

objects together by `iteration`

,
`chain`

, or `variable`

, but a 2-D
`draws_matrix`

with the chains combined can only by bound by
`draw`

and `variable`

.

Computing summaries of posterior or prior draws and convergence
diagnostics for posterior draws are some 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()`

(or
`summarize_draws()`

), which can be passed any of the formats
supported by the package.

```
# 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> <num> <num> <num> <num> <num> <num> <num> <num> <num>
## 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.
```

The result is 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 could use either of
these options:

```
# the function mcse_mean is provided by the posterior package
<- summarise_draws(eight_schools_df, "mean", "mcse_mean")
s1 <- summarise_draws(eight_schools_df, mean, mcse_mean)
s2 identical(s1, s2)
```

`## [1] TRUE`

`print(s1)`

```
## # A tibble: 10 × 3
## variable mean mcse_mean
## <chr> <num> <num>
## 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
```

The column names in the output can be changed by providing the
functions as name-value pairs, where the name is the name to use in the
output and the value is a function name or definition. For example, here
we change the names `mean`

and `sd`

to
`posterior_mean`

and `posterior_sd`

.

`summarise_draws(eight_schools_df, posterior_mean = mean, posterior_sd = sd)`

```
## # A tibble: 10 × 3
## variable posterior_mean posterior_sd
## <chr> <num> <num>
## 1 mu 4.18 3.40
## 2 tau 4.16 3.58
## 3 theta[1] 6.75 6.30
## 4 theta[2] 5.25 4.63
## 5 theta[3] 3.04 6.80
## 6 theta[4] 4.86 4.92
## 7 theta[5] 3.22 5.08
## 8 theta[6] 3.99 5.16
## 9 theta[7] 6.50 5.26
## 10 theta[8] 4.57 5.25
```

For a function to work with `summarise_draws()`

, it needs
to take a vector or matrix of numeric values and return a single numeric
value or a named vector of numeric values. Additional arguments to the
function can be specified in a list passed to the `.args`

argument.

```
<- function(x, wts) {
weighted_mean sum(x * wts)/sum(wts)
}summarise_draws(
eight_schools_df,
weighted_mean, .args = list(wts = rexp(ndraws(eight_schools_df)))
)
```

```
## # A tibble: 10 × 2
## variable weighted_mean
## <chr> <num>
## 1 mu 4.37
## 2 tau 4.06
## 3 theta[1] 6.70
## 4 theta[2] 5.44
## 5 theta[3] 3.03
## 6 theta[4] 5.20
## 7 theta[5] 3.38
## 8 theta[6] 4.16
## 9 theta[7] 6.66
## 10 theta[8] 4.83
```

It is also possible to specify a summary function using a one-sided
formula that follows the conventions supported by
`rlang::as_function()`

. For example, the function

`function(x) quantile(x, probs = c(0.4, 0.6))`

can be simplified to

```
# for multiple arguments `.x` and `.y` can be used, see ?rlang::as_function
~quantile(., probs = c(0.4, 0.6))
```

Both can be used with `summarise_draws()`

and produce the
same output:

`summarise_draws(eight_schools_df, function(x) quantile(x, probs = c(0.4, 0.6)))`

```
## # A tibble: 10 × 3
## variable `40%` `60%`
## <chr> <num> <num>
## 1 mu 3.41 5.35
## 2 tau 2.47 3.96
## 3 theta[1] 4.95 7.01
## 4 theta[2] 4.32 6.13
## 5 theta[3] 2.54 5.33
## 6 theta[4] 3.78 6.11
## 7 theta[5] 2.69 4.69
## 8 theta[6] 2.92 5.47
## 9 theta[7] 4.81 7.33
## 10 theta[8] 3.50 5.92
```

`summarise_draws(eight_schools_df, ~quantile(.x, probs = c(0.4, 0.6)))`

```
## # A tibble: 10 × 3
## variable `40%` `60%`
## <chr> <num> <num>
## 1 mu 3.41 5.35
## 2 tau 2.47 3.96
## 3 theta[1] 4.95 7.01
## 4 theta[2] 4.32 6.13
## 5 theta[3] 2.54 5.33
## 6 theta[4] 3.78 6.11
## 7 theta[5] 2.69 4.69
## 8 theta[6] 2.92 5.47
## 9 theta[7] 4.81 7.33
## 10 theta[8] 3.50 5.92
```

See `help("as_function", "rlang")`

for details on
specifying these functions.

In addition to the default diagnostic functions used by
`summarise_draws()`

(`rhat()`

,
`ess_bulk()`

, `ess_tail()`

), posterior also
provides additional diagnostics like effective sample sizes and Monte
Carlo standard errors for quantiles and standard deviations, an
experimental new diagnostic called R*, and others. For a list of
available diagnostics and links to their individual help pages see
`help("diagnostics", "posterior")`

.

If you have suggestions for additional diagnostics that should be implemented in posterior, please open an issue at https://github.com/stan-dev/posterior/issues.

`draws`

objectsIn addition to the methods demonstrated in this vignette, posterior
has various other methods available for working with `draws`

objects. The following is a (potentially incomplete) list.

Method |
Description |
---|---|

`order_draws()` |
Order `draws` objects according to iteration
and chain number |

`repair_draws()` |
Repair indices of `draws` objects so that
iterations chains, and draws are continuously and consistently
numbered |

`resample_draws()` |
Resample `draws` objects according to
provided weights |

`thin_draws()` |
Thin `draws` objects to reduce size and
autocorrelation |

`weight_draws()` |
Add weights to draws objects, with one weight per draw, for use in subsequent weighting operations |

`extract_variable()` |
Extract a vector of draws of a single variable |

`extract_variable_matrix()` |
Extract an iterations x chains matrix of draws of a single variable |

`merge_chains()` |
Merge chains of `draws` objects into a
single chain. |

`split_chains()` |
Split chains of `draws` objects by halving
the number of iterations per chain and doubling the number of
chains. |

If you have suggestions for additional methods that would be useful
for working with `draws`

objects, please open an issue at https://github.com/stan-dev/posterior/issues.

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.

Vehtari A., Gelman A., Simpson D., Carpenter B., & Bürkner P. C.
(2020). Rank-normalization, folding, and localization: An improved Rhat
for assessing convergence of MCMC. *Bayesian Analysis*.