rvars
datatype
draws_rvars
datatypervar
srvar
s
rvar
sdraws
and rvar
srvar
s in data frames and in ggplot2This vignette describes the rvar()
datatype, a
multidimensional, samplebased representation of random variables
designed to act as much like base R arrays as possible (e.g., by
supporting many math operators and functions). This format is also the
basis of the draws_rvars()
format.
The rvar()
datatype is inspired by the rv package and Kerman and Gelman
(2007), though with a slightly different backing format
(multidimensional arrays). It is also designed to interoperate with
vectorized distributions in the distributional
package, to be able to be used inside data.frame()
s and
tibble()
s, and to be used with distribution visualizations
in the ggdist
package.
rvars
datatypeThe rvar()
datatype is a wrapper around a
multidimensional array where the first dimension is the number of draws
in the random variable. The most direct way to create a random variable
is to pass such an array to the rvar()
function.
For example, to create a “scalar” rvar
, one would pass a
onedimensional array or a vector whose length (here 4000
)
is the desired number of draws:
## rvar<4000>[1] mean ± sd:
## [1] 1 ± 1
The default display of an rvar
shows the mean and
standard deviation of each element of the array.
We can create random vectors by adding an additional dimension beyond just the draws dimension to the input array:
n < 4 # length of output vector
x < rvar(array(rnorm(4000*n, mean = 1, sd = 1), dim = c(4000, n)))
x
## rvar<4000>[4] mean ± sd:
## [1] 1.01 ± 0.99 1.02 ± 0.99 0.98 ± 1.00 0.99 ± 1.02
Or we can create a random matrix:
rows < 4
cols < 3
x < rvar(array(rnorm(4000 * rows * cols, mean = 1, sd = 1), dim = c(4000, rows, cols)))
x
## rvar<4000>[4,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 1.00 ± 0.98 1.00 ± 1.00 0.97 ± 1.00
## [2,] 1.00 ± 1.01 1.01 ± 1.02 0.99 ± 0.99
## [3,] 1.02 ± 1.01 0.99 ± 1.00 1.00 ± 0.99
## [4,] 1.01 ± 1.01 1.02 ± 1.00 1.00 ± 1.01
Or any array up to an arbitrary number of dimensions. The array
backing an rvar
can be accessed (and modified, with
caution) via draws_of()
:
## num [1:4000, 1:4, 1:3] 0.6879 0.0448 0.3519 1.261 0.2197 ...
##  attr(*, "dimnames")=List of 3
## ..$ : chr [1:4000] "1" "2" "3" "4" ...
## ..$ : NULL
## ..$ : NULL
While the above examples assume all draws come from a single chain,
rvar
s can also contain samples from multiple chains. For
example, if your array of draws has iterations as the first dimension
and chains as the second dimension, you can use
with_chains = TRUE
to create an rvar
that
includes chain information:
iterations < 1000
chains < 4
rows < 4
cols < 3
x_array < array(
rnorm(iterations * chains * rows * cols, mean = 1, sd = 1),
dim = c(iterations, chains, rows, cols)
)
x < rvar(x_array, with_chains = TRUE)
x
## rvar<1000,4>[4,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 0.97 ± 1.00 1.00 ± 0.99 1.02 ± 0.99
## [2,] 1.02 ± 1.00 0.99 ± 1.01 1.01 ± 0.99
## [3,] 1.00 ± 1.00 1.00 ± 1.00 1.01 ± 1.00
## [4,] 1.03 ± 0.99 1.05 ± 1.00 0.98 ± 1.00
Manual construction and modification of rvar
s in this
way is not always recommended unless you need it for performance
reasons: several other higherlevel interfaces to constructing and
manipulating rvar
s are described below.
rvar_factor
and rvar_ordered
subtypesYou can also use rvar
s to represent discrete
distributions, using the rvar_factor()
and
rvar_ordered()
subtypes. If you attempt to create an
rvar
using character values or a factor
, it
will automatically be treated as an rvar_factor
:
## rvar_factor<4000>[1] mode <entropy>:
## [1] a <0.74>
## 3 levels: a b c
Numeric arrays with a "levels"
attribute can also be
passed to rvar_factor()
. This (along with conversion of
character values) means output from
rstanarm::posterior_predict()
and
brms::posterior_predict()
on categorical models can be
passed directly to rvar_factor()
.
The default display shows the mode (as returned by
modal_category()
) and normalized entropy
(entropy()
), which is Shannon entropy scaled by the maximum
possible entropy for a distribution with the same number of levels: thus
0 means all probability is concentrated in one category, and 1 means the
distribution is uniform.
You can construct an ordered factor using rvar_ordered()
(or by passing an ordered()
vector to
rvar()
):
## rvar_ordered<4000>[1] mode <dissent>:
## [1] a <0.55>
## 3 levels: a < b < c
For rvar_ordered()
, the default display is mode and
dissention (dissent()
), which is 0 when all probability is
concentrated in one category, and 1 when the distribution is bimodal at
opposite ends of the scale.
rvar_factor
s attempt to mimic factor()
and
rvar_ordered
s attempt to mimic ordered()
s, by
implementing factorspecific functions like levels()
.
Comparison operations are also implemented where valid. For example, in
x
as defined above, approximately 90% of draws should be
less than "b"
(which means the "a"
and
"b"
levels):
## rvar<4000>[1] mean ± sd:
## [1] 0.9 ± 0.29
rvar
s also supply an implementation of
match()
and %in%
, which can be especially
useful with rvar_factor
s:
## rvar<4000>[1] mean ± sd:
## [1] 0.8 ± 0.4
draws_rvars
datatypeThe draws_rvars()
datatype, like all draws
datatypes in posterior, contains multiple variables in a joint sample
from some distribution (e.g. a posterior or prior distribution).
You can construct draws_rvars()
objects directly using
the draws_rvars()
function. The input rvar
s
must have the same number of chains and iterations, but can otherwise
have different shapes:
## # A draws_rvars: 4000 iterations, 1 chains, and 2 variables
## $x: rvar_ordered<4000>[1] mode <dissent>:
## [1] a <0.55>
## 3 levels: a < b < c
##
## $y: rvar<4000>[1] mean ± sd:
## [1] 0.012 ± 0.98
Existing objects can also be converted to the
draws_rvars()
format using as_draws_rvars()
.
Below is the example_draws("multi_normal")
dataset
converted into the draws_rvars()
format. This dataset has
100 iterations from 4 chains from the posterior of a a 3dimensional
multivariate normal model. The mu
variable is a mean vector
of length 3 and the Sigma
variable is a \(3 \times 3\) covariance matrix:
## # A draws_rvars: 100 iterations, 4 chains, and 2 variables
## $mu: rvar<100,4>[3] mean ± sd:
## [1] 0.051 ± 0.11 0.111 ± 0.20 0.186 ± 0.31
##
## $Sigma: rvar<100,4>[3,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 1.28 ± 0.17 0.53 ± 0.20 0.40 ± 0.28
## [2,] 0.53 ± 0.20 3.67 ± 0.45 2.10 ± 0.48
## [3,] 0.40 ± 0.28 2.10 ± 0.48 8.12 ± 0.95
The draws_rvars()
datatype works much the same way that
other draws
formats do; see the main package vignette at
vignette("posterior")
for an introduction to
draws
objects. One difference is that
draws_rvars
counts variables differently, because it allows
variables to be multidimensional. For example, the post
object above contains two variables, mu
and
Sigma
:
## [1] "mu" "Sigma"
But converted to a draws_list()
, it contains one
variable for each combination of the dimensions of its variables:
## [1] "mu[1]" "mu[2]" "mu[3]" "Sigma[1,1]" "Sigma[2,1]"
## [6] "Sigma[3,1]" "Sigma[1,2]" "Sigma[2,2]" "Sigma[3,2]" "Sigma[1,3]"
## [11] "Sigma[2,3]" "Sigma[3,3]"
rvar
sThe rvar()
datatype implements most math operations,
including basic arithmetic, functions in the Math and
Summary groups, like log()
and exp()
(see help("groupGeneric")
for a list), and more. Binary
operators can be performed between multiple rvar
s or
between rvar
s and numeric
s. A simple
example:
## rvar<100,4>[3] mean ± sd:
## [1] 1.1 ± 0.11 1.1 ± 0.20 1.2 ± 0.31
Matrix multiplication is also implemented (using a tensor product
under the hood). Because the normal matrix multiplication operator in R
(%*%
) cannot be properly implemented for S3 datatypes,
rvar
uses %**%
instead. A trivial example:
## rvar<100,4>[3,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 1.28 ± 0.17 1.05 ± 0.40 1.21 ± 0.85
## [2,] 0.53 ± 0.20 7.33 ± 0.89 6.30 ± 1.44
## [3,] 0.40 ± 0.28 4.20 ± 0.96 24.35 ± 2.84
The set of mathematical functions and operators supported by
rvar
s includes:
Group  Functions and operators 

Arithmetic operators  + ,  , * ,
/ , ^ , %% , %/% 
Logical operators  & ,  , ! 
Comparison operators  == , != , < ,
<= , >= , > 
Value matching  match() , %in% 
Matrix multiplication  %**% 
Basic functions  abs() ,
sign() sqrt() floor() ,
ceiling() , trunc() , round() ,
signif() 
Logarithms and exponentials  exp() ,
expm1() log() , log10() ,
log2() , log1p() 
Trigonometric functions  cos() , sin() ,
tan() cospi() , sinpi() ,
tanpi() acos() , asin() ,
atan() 
Hyperbolic functions  cosh() , sinh() ,
tanh() acosh() , asinh() ,
atanh() 
Special functions  lgamma() , gamma() ,
digamma() , trigamma() 
Cumulative functions  cumsum() , cumprod() ,
cummax() , cummin() 
Array transposition  t() , aperm() 
Matrix decomposition  chol() 
Matrix diagonals  diag() 
The E()
function is an alias of mean()
,
producing means within each cell of an rvar
. For example,
given mu
:
## rvar<100,4>[3] mean ± sd:
## [1] 0.051 ± 0.11 0.111 ± 0.20 0.186 ± 0.31
We can get the expectation of each cell of mu
:
## [1] 0.05139284 0.11132363 0.18581977
Expectations of logical expressions are probabilities, and can be
computed either with E()
/ mean()
or with
Pr()
. Pr()
is provided as notational sugar,
but also checks that the input is a logical variable before taking the
mean:
## [1] 0.6600 0.6900 0.7025
More generally, the rvar
data type provides two types of
summary functions:
Summary functions that mimic baseR vector summary functions,
except applied to rvar
vectors. These apply their summaries
over elements of the input vectors
within each draw, generally returning an
rvar
of length 1. These functions are prefixed with
rvar_
as a reminder that they return rvar
s.
Here is an example of rvar_mean()
:
## rvar<100,4>[1] mean ± sd:
## [1] 0.12 ± 0.11
Summary functions that summarise within elements
of input vectors and over draws. These summary
functions generally return base arrays (numeric
or
logical
) of the same shape as the input rvar
,
and are especially useful for diagnostic summaries. These summary
functions are not prefixed with rvar_
as they do not return
rvar
s. Here is an example of mean()
:
## [1] 0.05139284 0.11132363 0.18581977
You should expect the same values from these functions (though in a
different shape) when you use them with summarise_draws()
,
for example:
## # A tibble: 3 × 2
## variable mean
## <chr> <dbl>
## 1 mu[1] 0.0514
## 2 mu[2] 0.111
## 3 mu[3] 0.186
Here is a table of both types of summary functions:
1. Summarise within draws, over elements 
2. Summarise over draws, within elements 


Output format of res = f(x) 
rvar of length 1 
array of same shape as input
rvar 
Help page  help("rvarsummarieswithindraws") 
help("rvarsummariesoverdraws") 
Numeric summaries  rvar_median() rvar_sum() ,
rvar_prod() rvar_min() ,
rvar_max() 
median() sum() ,
prod() min() , max() 
Mean  rvar_mean() N/A 
mean() ,
E() Pr() : enforces that input is
logical 
Spread  rvar_sd() rvar_var() rvar_mad() 
sd() var() ,
variance() mad() 
Range  rvar_range() Note: length(res) == 2 
range() Note: dim(res) == c(2, dim(x)) 
Quantiles  rvar_quantile() Note: length(res) == length(probs) 
quantile() Note: dim(res) == c(length(probs), dim(x)) 
Logical summaries  rvar_all() , rvar_any() 
all() , any() 
Special value predicates  rvar_is_finite() rvar_is_infinite() rvar_is_nan() rvar_is_na() Note: dim(res) == dim(x) . These functions act within draws but do
not summarise over elements. 
is.finite() is.infinite() is.nan() is.na() Note: res[i] == TRUE if x[i] has any draws matching
predicate (except for is.finite() , where all draws in
x[i] must match) 
Diagnostics  N/A  ess_basic() , ess_bulk() ,
ess_quantile() , ess_sd() ,
ess_tail() ,mcse_mean() ,
mcse_quantile() ,
mcse_sd() rhat() ,
rhat_basic() 
Constant rvar
s can be constructed by converting numeric
vectors or arrays into rvar
s using as_rvar()
,
which will return an rvar
with one draw and the same
dimensions as its input:
## rvar<1>[3] mean ± sd:
## [1] 1 ± NA 2 ± NA 3 ± NA
While normally rvar
s must have the same number of draws
to be used in the same expression, rvar
s with one draw are
treated like constants, and can be combined with other
rvar
s:
## rvar<100,4>[3] mean ± sd:
## [1] 1.1 ± 0.11 2.1 ± 0.20 3.2 ± 0.31
rvar
sWhile rvar
s attempt to emulate as much of the
functionality of base R arrays as possible, there are situations in
which an existing R function may not work directly with an
rvar
. There are several approaches to solving this
problem.
For example, say you wish to generate samples from the following expression for \(\mu\), \(\sigma\), and \(x\):
\[ \begin{align} \left[\begin{matrix}\mu_1 \\ \vdots \\ \mu_4 \end{matrix}\right] &\sim \textrm{Normal}\left(\left[\begin{matrix}1 \\ \vdots \\ 4 \end{matrix}\right],1\right)\\ \sigma &\sim \textrm{Gamma}(1,1)\\ \left[\begin{matrix}x_1 \\ \vdots \\ x_4 \end{matrix}\right] &\sim \textrm{Normal}\left(\left[\begin{matrix}\mu_1 \\ \vdots \\ \mu_4 \end{matrix}\right], \sigma\right) \end{align} \]
There are three different approaches you might take to doing this:
converting existing R functions with rfun()
, executing
expressions of random variables with rdo()
, or evaluating
random number generator functions using rvar_rng()
.
rfun()
The rfun()
wrapper converts an existing R function into
a new function that rvar
s can be passed to it as arguments,
and which will return rvar
s. We can use rfun()
to convert the base rnorm()
and rgamma()
random number generating functions into functions that accept and return
rvar
s:
Then we can translate the above example into code using those functions:
mu < rvar_norm(4, mean = 1:4, sd = 1)
sigma < rvar_gamma(1, shape = 1, rate = 1)
x < rvar_norm(4, mu, sigma)
x
## rvar<4000>[4] mean ± sd:
## [1] 1 ± 1.7 2 ± 1.7 3 ± 1.7 4 ± 1.8
While rfun()
converted functions work well for
prototyping, they will generally speaking be slower than functions
designed specifically for rvar
s. Thus, you may find you
need to adopt other strategies (like rvar_rng()
, described
below; or rewriting functions to support rvar
directly
using math operators and/or the draws_of()
function).
rdo()
An alternative to rfun()
is to use rdo()
,
which can be passed nearlyarbitrary R expressions. The expression will
be executed multiple times to construct an rvar
. E.g., we
can write an expression for mu
like in the above
example:
## rvar<4000>[4] mean ± sd:
## [1] 0.98 ± 0.98 2.02 ± 1.02 2.99 ± 1.01 3.99 ± 1.01
We can also control the number of draws using the ndraws
argument:
## rvar<1000>[4] mean ± sd:
## [1] 0.96 ± 1.05 1.99 ± 1.03 2.98 ± 0.98 4.01 ± 0.99
rdo()
expressions can also contain other
rvar
s, so long as all rvar
s in the expression
have the same number of draws. Thus, we can rewrite the example above
that used rfun()
as follows:
mu < rdo(rnorm(4, mean = 1:4, sd = 1))
sigma < rdo(rgamma(1, shape = 1, rate = 1))
x < rdo(rnorm(4, mu, sigma))
x
## rvar<4000>[4] mean ± sd:
## [1] 0.97 ± 1.7 2.05 ± 1.7 3.05 ± 1.7 4.02 ± 1.7
Like rfun()
, rdo()
is not necessarily fast,
so you may find it more useful for prototyping than production code.
rvar_rng()
rvar_rng()
is an alternative to
rfun()
/rdo()
designed specifically to work
with random number generating functions that follow the typical API of
such functions in base R. Such functions, like rnorm()
,
rgamma()
, rbinom()
, etc all following this
interface:
n
, giving the number of
draws to take from the distribution.mean
,
sd
, shape
, rate
, etc.) are
vectorized.n
, representing
n
draws from the distribution.You can use any function with this interface with
rvar_rng()
, and it will adapt it to be able to take
rvar
arguments and return an rvar
, as
follows:
mu < rvar_rng(rnorm, 4, mean = 1:4, sd = 1)
sigma < rvar_rng(rgamma, 1, shape = 1, rate = 1)
x < rvar_rng(rnorm, 4, mu, sigma)
x
## rvar<4000>[4] mean ± sd:
## [1] 1 ± 1.8 2 ± 1.8 3 ± 1.8 4 ± 1.8
In contrast to the rfun()
and rdo()
examples above, rvar_rng()
takes advantage of the existing
vectorization of the underlying random number generating function to
execute quickly.
Broadcasting for rvar
s does not follow R’s vector
recycling rules. Instead, when two variables with different dimensions
are being used with basic arithmetic functions, dimensions are added
until both variables have the same number of dimensions. If two
variables \(x\) and \(y\) differ on the length of dimension \(d\), they can be broadcast to the same size
so long as one of the variables has dimension \(d\) of size 1. Then that variable will be
broadcast up to the same size as the other variable along that
dimension. If two variables disagree on the size of a dimension and
neither has size 1, it is an error.
For example, consider this random matrix:
## rvar<4000>[4,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 0.99 ± 1.00 5.00 ± 1.00 9.02 ± 0.99
## [2,] 1.98 ± 1.02 5.99 ± 1.01 9.96 ± 1.00
## [3,] 3.03 ± 0.98 6.99 ± 1.00 11.03 ± 0.99
## [4,] 3.99 ± 1.01 7.99 ± 0.99 11.98 ± 1.02
And this vector of length 3:
## rvar<4000>[3] mean ± sd:
## [1] 3.00 ± 1.00 2.02 ± 0.99 0.96 ± 0.99
If we attempt to add X
and y
, it will
produce an error as vectors are by default treated as column vectors,
and y
has length 3 while columns of X
have
length 4:
## Error: Cannot broadcast array of shape [4000,3,1] to array of shape [4000,4,3]:
## All dimensions must be 1 or equal.
By contrast, R arrays of the same shape will simply recycle
y
until it is the same length as X
(regardless
of the dimensions). Thus will produce a result, though likely not the
intended result:
## [,1] [,2] [,3]
## [1,] 3.990551 7.019104 9.979782
## [2,] 3.995785 6.951943 12.961398
## [3,] 3.997008 9.996424 13.053459
## [4,] 6.988434 10.011049 12.945383
On the other hand, if y were a row vector…
## rvar<4000>[1,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 3.00 ± 1.00 2.02 ± 0.99 0.96 ± 0.99
…it would have the same number of columns as X
and
contain only one row, so it can be broadcast along rows of
X
:
## rvar<4000>[4,3] mean ± sd:
## [,1] [,2] [,3]
## [1,] 4 ± 1.4 7 ± 1.4 10 ± 1.4
## [2,] 5 ± 1.4 8 ± 1.4 11 ± 1.4
## [3,] 6 ± 1.4 9 ± 1.4 12 ± 1.4
## [4,] 7 ± 1.4 10 ± 1.4 13 ± 1.4
The [[
and [
operators implement all of the
base array slicing operations, including numeric, character, and logical
indices, as well as slicing arrays using a matrix of indices with
[
. The main difference between rvar
slicing
and base array slicing is that rvar
s default to
drop = FALSE
; i.e. they retain all dimensions of the
original rvar
array. For a complete list of
rvar
slicing types, see
help("rvarslice")
.
In addition to the base slicing operations, rvar
also
implements three slicing/conditioning methods that allow you to use
other rvar
s to define a slice.
To demonstrate these operations, consider an rvar
vector
of two components:
## rvar<4000>[2] mean ± sd:
## [1] 1 ± 0.98 5 ± 0.99
Perhaps we want to create a mixture of these two components,
mixture
, with a mixing proportion of 0.75. We could create
an index, i
, that is a random variable indicating which
component (1 or 2) determines the value of mixture
on each
draw:
## rvar<4000>[1] mean ± sd:
## [1] 1.7 ± 0.44
We can use several different approaches to create the mixture distribution
rvar
s by draw:
x[<logical rvar>]
A slice x[i]
where i
is a scalar logical
rvar
returns a new rvar with the same shape as
x
, but containing only those draws where i
is
TRUE
. Thus, we can use i == 2
to select draws
from the second component and overwrite them in the first component,
creating the mixture distribution:
## rvar<4000>[1] mean ± sd:
## [1] 4 ± 2
The resulting mixture looks like this:
See vignette("slabinterval", package = "ggdist")
for
more examples of visualizing distributiontype objects, including
rvar
s.
rvar_ifelse()
You could create the same mixture using
rvar_ifelse(test, yes, no)
, which broadcasts
test
, yes
, and no
to the same
shape, then returns a new rvar
containing draws from
yes
where test == TRUE
and draws from
no
where test == FALSE
.
Thus, we can create the mixture as follows:
## rvar<4000>[1] mean ± sd:
## [1] 4 ± 2
x[[<numeric rvar>]]
The slice x[[i]]
, where i
is a scalar
numeric rvar, generalizes indexing when i
is a scalar
numeric. Within each draw of x
, it selects the element of
x
corresponding to the value of i
within that
same draw.
Thus, since i
in our example is a scalar numeric
rvar
whose values are either 1
or
2
within each draw, you can use it as an index directly on
component
to create the mixture:
## rvar<4000>[1] mean ± sd:
## [1] 4 ± 2
This approach is also nice because it generalizes easily to more than two components.
rvar
sThe rvar
data type supplies an implementation of
as.list()
, which should give compatibility with the base R
family of functions for applying functions over arrays:
apply()
, lapply()
, vapply()
,
sapply()
, etc. You can also manually use
as.list()
to convert an rvar
into a list along
its first dimension, which may be necessary for compatibility with some
functions (like purrr:map()
).
For example, given this multidimensional rvar
…
## rvar<4000>[2,3,4] mean ± sd:
## , , 1
##
## [,1] [,2] [,3]
## [1,] 1 ± 1.00 3 ± 0.98 5 ± 1.00
## [2,] 2 ± 1.00 4 ± 1.00 6 ± 1.01
##
## , , 2
##
## [,1] [,2] [,3]
## [1,] 7 ± 1.00 9 ± 1.00 11 ± 1.03
## [2,] 8 ± 0.98 10 ± 0.99 12 ± 1.00
##
## , , 3
##
## [,1] [,2] [,3]
## [1,] 13 ± 1.00 15 ± 1.00 17 ± 0.99
## [2,] 14 ± 1.01 16 ± 1.00 18 ± 1.00
##
## , , 4
##
## [,1] [,2] [,3]
## [1,] 19 ± 0.99 21 ± 0.99 23 ± 0.99
## [2,] 20 ± 1.00 22 ± 1.00 24 ± 1.00
… you can apply functions along the margins using
apply()
(here, a silly example):
## [,1] [,2] [,3]
## [1,] 4 4 4
## [2,] 4 4 4
One exception is that while apply()
will work with an
rvar
input if your function returns base data types (like
numerics), it will not give you simplified rvar
arrays if
your function returns an rvar
. Thus, we supply the
rvar_apply()
function, which takes in either base arrays or
rvar
arrays and returns rvar
arrays, and which
also uses the rvar
broadcasting rules to combine the
results of the applied function.
For example, you can use rvar_apply()
with
rvar_mean()
to compute the distributions of means along one
margin of an array:
## rvar<4000>[2] mean ± sd:
## [1] 12 ± 0.29 13 ± 0.29
Or along multiple dimensions:
## rvar<4000>[3,4] mean ± sd:
## [,1] [,2] [,3] [,4]
## [1,] 1.5 ± 0.70 7.5 ± 0.69 13.5 ± 0.71 19.5 ± 0.70
## [2,] 3.5 ± 0.70 9.5 ± 0.71 15.5 ± 0.72 21.5 ± 0.70
## [3,] 5.5 ± 0.71 11.5 ± 0.72 17.5 ± 0.71 23.5 ± 0.70
draws
and rvar
sThe rvar
datatype is also used in
for_each_draw()
, which allows you to loop over draws in a
draws
object or an rvar
.
for_each_draw(x, expr)
converts x
into a
draws_rvars()
object, then loops over each draw of
x
, executing the provided expression, expr
.
The expression can use the variables in x
as if they were
regular R arrays.
One application of for_each_draw()
is in constructing
baseR plots of individual draws (for ggplot2
based
plotting of rvar
s, see the next section and the ggdist package). For
example, it can be used to construct a parallel coordinates plot:
eight_schools < as_draws_rvars(example_draws())
plot(1, type = "n",
xlim = c(1, length(eight_schools$theta)),
ylim = range(range(eight_schools$theta)),
xlab = "school", ylab = "theta"
)
# use for_each_draw() to make a parallel coordinates plot of all draws
# of eight_schools$theta
for_each_draw(eight_schools, {
lines(seq_along(theta), theta, col = rgb(1, 0, 0, 0.05))
})
# add means and 90% intervals
lines(seq_along(eight_schools$theta), mean(eight_schools$theta))
with(summarise_draws(eight_schools$theta),
segments(seq_along(eight_schools$theta), y0 = q5, y1 = q95)
)
As for_each_draw()
will be slower than most other ways
of manipulating draws, this function should generally not be used unless
needed.
rvar
s in data frames and in ggplot2rvar
s can be used as columns in
data.frame()
or tibble()
objects:
## group mu
## 1 a 1 ± 1.02
## 2 b 2 ± 1.02
## 3 c 3 ± 0.99
## 4 d 4 ± 1.01
This makes them convenient for adding predictions to a data frame
alongside the data used to generate the predictions. rvar
s
can then be visualized with ggplot2 by passing them to the
xdist
and ydist
aesthetics of the
stat_...
family of geometries in the ggdist package, such as
stat_halfeye()
, stat_lineribbon()
, and
stat_dotsinterval()
. For example:
See vignette("slabinterval", package = "ggdist")
or
vignette("tidyposterior", package = "tidybayes")
for more
examples.