The draws_df format

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, .iteration = NULL, .chain = NULL, ...)

# 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 mcmc
as_draws_df(x, ...)

# S3 method for mcmc.list
as_draws_df(x, ...)

draws_df(..., .nchains = 1)

is_draws_df(x)

Arguments

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.
.iteration	Optional name of a column in the supplied data.frame that contain iteration indices. If NULL (the default), the .iteration column is used if it exists. Otherwise, the input is treated as being index continuously from the first to the last row.
.chain	Optional name of a column in the supplied data.frame that contain chain indices. If NULL (the default), the .chain column is used if it exists. Otherwise, the input is treated as belonging to a single chain.
.nchains	Number of chains (default is 1).

Value

A draws_df object, which has classes c("draws_df", "draws", class(tibble::tibble())).

Details

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.

See also

Other formats: draws_array(), draws_list(), draws_matrix(), draws

Examples

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)
#> tibble [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   0.862  1.07 1
#> 2  -0.243 -0.67 1
#> 3  -0.206  1.11 1
#> 4   0.019 -0.25 1
#> 5   0.030 -1.18 1
#> 6   0.550 -0.98 1
#> 7  -2.274  1.07 1
#> 8   2.683  0.13 1
#> 9  -0.361  0.49 1
#> 10  0.213 -1.70 1
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
str(x2)
#> tibble [10 × 6] (S3: draws_df/draws/tbl_df/tbl/data.frame)
#>  $ a         : num [1:10] 0.8621 -0.2432 -0.2061 0.0192 0.0296 ...
#>  $ b         : num [1:10] 1.074 -0.665 1.114 -0.246 -1.178 ...
#>  $ 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 NA  NA     NA          1     1
#> 2 NA  NA     NA          2     2
#> 3 NA  NA     NA          3     3
#> 4 NA  NA     NA          4     4
#> 5 NA  NA     NA          5     5
#> 6 NA  NA     NA          6     6
#> # ... with 8 more variables