Create a draws object supported by the posterior package. These
methods are just wrappers around CmdStanR's $draws()
method provided for convenience.
# S3 method for CmdStanMCMC
as_draws(x, ...)
# S3 method for CmdStanMLE
as_draws(x, ...)
# S3 method for CmdStanLaplace
as_draws(x, ...)
# S3 method for CmdStanVB
as_draws(x, ...)
# S3 method for CmdStanGQ
as_draws(x, ...)
# S3 method for CmdStanPathfinder
as_draws(x, ...)A CmdStanR fitted model object.
Optional arguments passed to the $draws()
method (e.g., variables, inc_warmup, etc.).
To subset iterations, chains, or draws, use the
posterior::subset_draws() method after creating the draws object.
# \dontrun{
fit <- cmdstanr_example()
as_draws(fit)
#> # A draws_array: 1000 iterations, 4 chains, and 105 variables
#> , , variable = lp__
#>
#> chain
#> iteration 1 2 3 4
#> 1 -66 -65 -65 -64
#> 2 -67 -67 -65 -68
#> 3 -66 -65 -65 -66
#> 4 -65 -66 -64 -66
#> 5 -64 -67 -65 -66
#>
#> , , variable = alpha
#>
#> chain
#> iteration 1 2 3 4
#> 1 0.792 0.30 0.38 0.38
#> 2 0.061 0.54 0.49 0.14
#> 3 0.538 0.23 0.47 0.29
#> 4 0.446 0.10 0.37 0.41
#> 5 0.253 0.68 0.46 0.56
#>
#> , , variable = beta[1]
#>
#> chain
#> iteration 1 2 3 4
#> 1 -0.93 -0.46 -0.99 -0.75
#> 2 -0.60 -0.87 -0.93 -0.84
#> 3 -0.67 -0.51 -0.45 -0.41
#> 4 -0.90 -0.78 -0.75 -0.19
#> 5 -0.63 -0.57 -0.45 -0.62
#>
#> , , variable = beta[2]
#>
#> chain
#> iteration 1 2 3 4
#> 1 -0.26 0.031 -0.15 -0.195
#> 2 0.12 -0.226 -0.36 -0.386
#> 3 -0.02 -0.199 -0.15 -0.548
#> 4 -0.31 -0.013 -0.20 -0.084
#> 5 -0.12 -0.615 -0.09 -0.610
#>
#> # ... with 995 more iterations, and 101 more variables
# posterior's as_draws_*() methods will also work
posterior::as_draws_rvars(fit)
#> # A draws_rvars: 1000 iterations, 4 chains, and 4 variables
#> $lp__: rvar<1000,4>[1] mean ± sd:
#> [1] -66 ± 1.5
#>
#> $alpha: rvar<1000,4>[1] mean ± sd:
#> [1] 0.37 ± 0.22
#>
#> $beta: rvar<1000,4>[3] mean ± sd:
#> [1] -0.67 ± 0.25 -0.28 ± 0.23 0.69 ± 0.27
#>
#> $log_lik: rvar<1000,4>[100] mean ± sd:
#> [1] -0.518 ± 0.101 -0.398 ± 0.148 -0.500 ± 0.222 -0.445 ± 0.153
#> [5] -1.183 ± 0.289 -0.593 ± 0.195 -0.637 ± 0.127 -0.278 ± 0.133
#> [9] -0.697 ± 0.172 -0.743 ± 0.237 -0.280 ± 0.126 -0.492 ± 0.240
#> [13] -0.655 ± 0.213 -0.361 ± 0.174 -0.279 ± 0.109 -0.275 ± 0.087
#> [17] -1.594 ± 0.288 -0.481 ± 0.109 -0.232 ± 0.075 -0.112 ± 0.078
#> [21] -0.211 ± 0.087 -0.571 ± 0.151 -0.330 ± 0.138 -0.135 ± 0.065
#> [25] -0.451 ± 0.123 -1.520 ± 0.340 -0.307 ± 0.123 -0.446 ± 0.086
#> [29] -0.722 ± 0.230 -0.699 ± 0.193 -0.489 ± 0.165 -0.425 ± 0.111
#> [33] -0.406 ± 0.128 -0.062 ± 0.048 -0.583 ± 0.188 -0.325 ± 0.130
#> [37] -0.702 ± 0.230 -0.309 ± 0.149 -0.178 ± 0.110 -0.684 ± 0.132
#> [41] -1.131 ± 0.261 -0.937 ± 0.201 -0.413 ± 0.266 -1.175 ± 0.188
#> [45] -0.359 ± 0.118 -0.578 ± 0.131 -0.301 ± 0.128 -0.324 ± 0.083
#> [49] -0.319 ± 0.081 -1.288 ± 0.331 -0.288 ± 0.094 -0.832 ± 0.146
#> [53] -0.402 ± 0.132 -0.371 ± 0.142 -0.381 ± 0.136 -0.320 ± 0.188
#> [57] -0.660 ± 0.121 -0.954 ± 0.356 -1.371 ± 0.345 -0.976 ± 0.161
#> [61] -0.543 ± 0.100 -0.872 ± 0.317 -0.115 ± 0.071 -0.899 ± 0.250
#> [65] -2.024 ± 0.609 -0.509 ± 0.139 -0.276 ± 0.081 -1.059 ± 0.239
#> [69] -0.437 ± 0.086 -0.642 ± 0.235 -0.608 ± 0.213 -0.460 ± 0.173
#> [73] -1.496 ± 0.368 -0.947 ± 0.199 -1.139 ± 0.392 -0.373 ± 0.140
#> [77] -0.876 ± 0.143 -0.490 ± 0.174 -0.767 ± 0.193 -0.537 ± 0.197
#> [81] -0.160 ± 0.100 -0.220 ± 0.138 -0.344 ± 0.082 -0.275 ± 0.092
#> [85] -0.129 ± 0.074 -1.136 ± 0.323 -0.821 ± 0.130 -0.776 ± 0.248
#> [89] -1.289 ± 0.322 -0.258 ± 0.136 -0.383 ± 0.131 -1.501 ± 0.351
#> [93] -0.736 ± 0.220 -0.318 ± 0.088 -0.389 ± 0.113 -1.575 ± 0.284
#> [97] -0.432 ± 0.101 -1.058 ± 0.374 -0.690 ± 0.144 -0.392 ± 0.098
#>
posterior::as_draws_list(fit)
#> # A draws_list: 1000 iterations, 4 chains, and 105 variables
#>
#> [chain = 1]
#> $lp__
#> [1] -66 -67 -66 -65 -64 -65 -65 -66 -64 -66
#>
#> $alpha
#> [1] 0.792 0.061 0.538 0.446 0.253 0.343 0.387 0.827 0.506 0.713
#>
#> $`beta[1]`
#> [1] -0.93 -0.60 -0.67 -0.90 -0.63 -0.71 -0.64 -0.60 -0.73 -0.96
#>
#> $`beta[2]`
#> [1] -0.26 0.12 -0.02 -0.31 -0.12 -0.46 -0.44 -0.39 -0.32 -0.22
#>
#>
#> [chain = 2]
#> $lp__
#> [1] -65 -67 -65 -66 -67 -67 -67 -67 -67 -72
#>
#> $alpha
#> [1] 0.303 0.538 0.227 0.104 0.678 0.596 -0.041 0.769 0.340 0.948
#>
#> $`beta[1]`
#> [1] -0.46 -0.87 -0.51 -0.78 -0.57 -0.50 -0.57 -0.67 -0.60 -0.47
#>
#> $`beta[2]`
#> [1] 0.03060 -0.22607 -0.19885 -0.01303 -0.61503 -0.63211 0.00036 -0.41151
#> [9] -0.46209 -0.88087
#>
#> # ... with 990 more iterations, and 2 more chains, and 101 more variables
# }