Extract posterior or prior draws using posterior, tidybayes, or coda S3 generics.
Usage
# S3 method for class 'mcpfit'
as_draws(x, prior = FALSE, ...)
as_draws(x, ...)
as_draws_df(x, ...)
as_draws_array(x, ...)
as_draws_matrix(x, ...)
as_draws_rvars(x, ...)Arguments
- x
An
mcpfitobject.- prior
Logical. Extract prior draws (
TRUE) instead of posterior draws (FALSE)? Errors if the requested draws are unavailable.- ...
Passed to posterior or tidybayes format conversion functions.
Examples
# Default posterior draws, with one row per iteration and chain
draws = as_draws(demo_fit) # Return a posterior::draws object
head(as_draws_df(demo_fit)) # Convert draws to a data frame
#> # A draws_df: 6 iterations, 1 chains, and 7 variables
#> Intercept_1 Intercept_3 cp_1 cp_2 sigma_1 time_2 time_3
#> 1 10.2 16 31 72 4.5 0.51 0.077
#> 2 9.9 16 31 70 3.6 0.51 -0.047
#> 3 9.4 17 31 71 3.6 0.51 -0.078
#> 4 10.8 17 32 71 3.4 0.54 -0.075
#> 5 9.3 19 33 73 3.8 0.59 -0.178
#> 6 9.3 17 30 72 4.0 0.54 -0.113
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}
# Other posterior formats are useful in different downstream packages
as_draws_matrix(demo_fit)[1:3, 1:3] # Matrix of draws by parameter
#> # A draws_matrix: 3 iterations, 1 chains, and 3 variables
#> variable
#> draw Intercept_1 Intercept_3 cp_1
#> 1 10.2 16 31
#> 2 9.9 16 31
#> 3 9.4 17 31
as_draws_array(demo_fit)[1:2, , 1:2] # Iteration-by-chain-by-parameter array
#> # A draws_array: 2 iterations, 2 chains, and 2 variables
#> , , variable = Intercept_1
#>
#> chain
#> iteration 1 2
#> 1 10.2 9.6
#> 2 9.9 10.1
#>
#> , , variable = Intercept_3
#>
#> chain
#> iteration 1 2
#> 1 16 19
#> 2 16 16
#>
as_draws_rvars(demo_fit)[c("cp_1", "cp_2")] # Random-variable representation
#> # A draws_rvars: 500 iterations, 2 chains, and 2 variables
#> $cp_1: rvar<500,2>[1] mean ± sd:
#> [1] 31 ± 1.7
#>
#> $cp_2: rvar<500,2>[1] mean ± sd:
#> [1] 71 ± 1
#>
# mcp also supports the coda and tidybayes conventions
head(coda::as.mcmc(demo_fit)[[1]]) # First chain as a coda mcmc object
#> Markov Chain Monte Carlo (MCMC) output:
#> Start = 1
#> End = 7
#> Thinning interval = 1
#> Intercept_1 Intercept_3 cp_1 cp_2 sigma_1 time_2 time_3
#> [1,] 10.191209 15.69228 30.84290 72.12381 4.459326 0.5126160 0.07732289
#> [2,] 9.865942 16.35426 30.53939 69.99791 3.571110 0.5118716 -0.04739203
#> [3,] 9.380313 16.53452 30.63572 71.06167 3.615166 0.5141171 -0.07802441
#> [4,] 10.789719 16.97402 31.93417 70.89540 3.437456 0.5390244 -0.07489507
#> [5,] 9.323356 18.69243 33.12817 72.57656 3.843492 0.5946020 -0.17761014
#> [6,] 9.305478 16.87526 30.01061 72.06966 3.968703 0.5380811 -0.11323448
#> [7,] 10.021265 15.98717 32.82439 72.41413 4.029903 0.5880111 -0.04399810
head(tidybayes::tidy_draws(demo_fit)) # Tidybayes-compatible draw data
#> # A draws_df: 6 iterations, 1 chains, and 7 variables
#> Intercept_1 Intercept_3 cp_1 cp_2 sigma_1 time_2 time_3
#> 1 10.2 16 31 72 4.5 0.51 0.077
#> 2 9.9 16 31 70 3.6 0.51 -0.047
#> 3 9.4 17 31 71 3.6 0.51 -0.078
#> 4 10.8 17 32 71 3.4 0.54 -0.075
#> 5 9.3 19 33 73 3.8 0.59 -0.178
#> 6 9.3 17 30 72 4.0 0.54 -0.113
#> # ... hidden reserved variables {'.chain', '.iteration', '.draw'}