The as_draws_df() methods convert
objects to the draws_df format.
The draws_df() function creates an object of the
draws_df format based on a set of numeric vectors.
See Details.
as_draws_df(x, ...)
# S3 method for default
as_draws_df(x, ...)
# S3 method for data.frame
as_draws_df(x, ...)
# S3 method for draws_df
as_draws_df(x, ...)
# S3 method for draws_matrix
as_draws_df(x, ...)
# S3 method for draws_array
as_draws_df(x, ...)
# S3 method for draws_list
as_draws_df(x, ...)
# S3 method for draws_rvars
as_draws_df(x, ...)
# S3 method for mcmc
as_draws_df(x, ...)
# S3 method for mcmc.list
as_draws_df(x, ...)
draws_df(..., .nchains = 1)
is_draws_df(x)An object to convert to a draws_df object.
For as_draws_df():
Arguments passed to individual methods (if applicable).
For draws_df(): Named
arguments containing numeric vectors each defining a separate variable.
(positive integer) The number of chains. The default is 1.
A draws_df object, which has classes
c("draws_df", "draws", class(tibble::tibble())).
Objects of class "draws_df" are tibble data
frames. They have one column per variable as well as additional metadata
columns ".iteration", ".chain", and ".draw". The difference between
the ".iteration" and ".draw" columns is that the former is relative to
the MCMC chain while the latter ignores the chain information and has all
unique values. See Examples.
If a data.frame-like object is supplied to as_draws_df that contains
columns named ".iteration" or ".chain", they will be treated as
iteration and chain indices, respectively. See Examples.
Other formats:
draws_array(),
draws_list(),
draws_matrix(),
draws_rvars(),
draws
x1 <- as_draws_df(example_draws())
class(x1)
#> [1] "draws_df" "draws" "tbl_df" "tbl" "data.frame"
print(x1)
#> # 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'}
str(x1)
#> 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 ...
x2 <- draws_df(a = rnorm(10), b = rnorm(10), c = 1)
class(x2)
#> [1] "draws_df" "draws" "tbl_df" "tbl" "data.frame"
print(x2)
#> # A draws_df: 10 iterations, 1 chains, and 3 variables
#> a b c
#> 1 -1.11 0.86 1
#> 2 0.24 -0.57 1
#> 3 1.31 -1.09 1
#> 4 0.71 0.26 1
#> 5 0.64 1.03 1
#> 6 1.50 -0.21 1
#> 7 -0.34 -0.88 1
#> 8 -0.15 -0.25 1
#> 9 -0.40 0.99 1
#> 10 0.62 -0.26 1
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
str(x2)
#> draws_df [10 × 6] (S3: draws_df/draws/tbl_df/tbl/data.frame)
#> $ a : num [1:10] -1.108 0.239 1.307 0.71 0.638 ...
#> $ b : num [1:10] 0.857 -0.566 -1.093 0.259 1.034 ...
#> $ c : num [1:10] 1 1 1 1 1 1 1 1 1 1
#> $ .chain : int [1:10] 1 1 1 1 1 1 1 1 1 1
#> $ .iteration: int [1:10] 1 2 3 4 5 6 7 8 9 10
#> $ .draw : int [1:10] 1 2 3 4 5 6 7 8 9 10
# the difference between iteration and draw is clearer when contrasting
# the head and tail of the data frame
print(head(x1), reserved = TRUE, max_variables = 2)
#> # A draws_df: 6 iterations, 1 chains, and 10 variables
#> mu tau .chain .iteration .draw
#> 1 2.0 2.8 1 1 1
#> 2 1.5 7.0 1 2 2
#> 3 5.8 9.7 1 3 3
#> 4 6.8 4.8 1 4 4
#> 5 1.8 2.8 1 5 5
#> 6 3.8 4.1 1 6 6
#> # ... with 8 more variables
print(tail(x1), reserved = TRUE, max_variables = 2)
#> # A draws_df: 6 iterations, 1 chains, and 10 variables
#> mu tau .chain .iteration .draw
#> 1 5.69 2.2 4 95 395
#> 2 3.28 3.3 4 96 396
#> 3 5.04 3.6 4 97 397
#> 4 2.73 6.8 4 98 398
#> 5 0.48 1.8 4 99 399
#> 6 7.05 4.8 4 100 400
#> # ... with 8 more variables
# manually supply chain information
xnew <- data.frame(mu = rnorm(10), .chain = rep(1:2, each = 5))
xnew <- as_draws_df(xnew)
print(xnew)
#> # A draws_df: 5 iterations, 2 chains, and 1 variables
#> mu
#> 1 -0.28
#> 2 -0.45
#> 3 0.72
#> 4 2.15
#> 5 -0.37
#> 6 -0.15
#> 7 -0.88
#> 8 -2.91
#> 9 -0.28
#> 10 1.73
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}