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 CmdStanVB
as_draws(x, ...)

# S3 method for CmdStanGQ
as_draws(x, ...)

Arguments

x

A CmdStanR fitted model object.

...

Optional arguments passed to the $draws() method (e.g., variables, inc_warmup, etc.).

Details

To subset iterations, chains, or draws, use the posterior::subset_draws() method after creating the draws object.

Examples

# \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 -67 -65 -65 -67 #> 2 -65 -66 -65 -64 #> 3 -67 -68 -65 -67 #> 4 -65 -67 -66 -68 #> 5 -64 -67 -68 -66 #> #> , , variable = alpha #> #> chain #> iteration 1 2 3 4 #> 1 0.46 0.450 0.50 0.49 #> 2 0.21 0.098 0.30 0.34 #> 3 0.31 0.712 0.40 0.26 #> 4 0.45 0.242 0.39 0.56 #> 5 0.46 0.848 0.28 0.24 #> #> , , variable = beta[1] #> #> chain #> iteration 1 2 3 4 #> 1 -0.43 -0.55 -0.38 -1.19 #> 2 -0.64 -0.87 -0.91 -0.76 #> 3 -1.16 -0.35 -0.31 -0.16 #> 4 -0.62 -1.15 -0.89 -1.25 #> 5 -0.56 -0.37 -0.69 -1.10 #> #> , , variable = beta[2] #> #> chain #> iteration 1 2 3 4 #> 1 -0.33 -0.575 -0.167 -0.291 #> 2 -0.51 0.026 -0.341 -0.179 #> 3 -0.36 -0.126 -0.401 -0.464 #> 4 -0.49 -0.319 -0.023 -0.047 #> 5 -0.39 -0.397 0.022 -0.209 #> #> # ... 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] 0.38 ± 0.22 #> #> $beta: rvar<1000,4>[3] mean ± sd: #> [1] -0.66 ± 0.25 -0.28 ± 0.23 0.69 ± 0.27 #> #> $log_lik: rvar<1000,4>[100] mean ± sd: #> [1] -0.517 ± 0.101 -0.401 ± 0.148 -0.501 ± 0.220 -0.446 ± 0.151 #> [5] -1.187 ± 0.287 -0.586 ± 0.189 -0.640 ± 0.125 -0.276 ± 0.135 #> [9] -0.690 ± 0.166 -0.744 ± 0.235 -0.277 ± 0.125 -0.491 ± 0.234 #> [13] -0.660 ± 0.207 -0.364 ± 0.175 -0.279 ± 0.110 -0.274 ± 0.089 #> [17] -1.597 ± 0.300 -0.478 ± 0.109 -0.232 ± 0.078 -0.114 ± 0.081 #> [21] -0.211 ± 0.090 -0.565 ± 0.147 -0.328 ± 0.140 -0.136 ± 0.068 #> [25] -0.454 ± 0.122 -1.519 ± 0.347 -0.304 ± 0.122 -0.445 ± 0.086 #> [29] -0.723 ± 0.226 -0.693 ± 0.191 -0.483 ± 0.161 -0.423 ± 0.110 #> [33] -0.409 ± 0.128 -0.064 ± 0.052 -0.587 ± 0.182 -0.324 ± 0.133 #> [37] -0.701 ± 0.227 -0.310 ± 0.149 -0.180 ± 0.112 -0.680 ± 0.128 #> [41] -1.138 ± 0.260 -0.931 ± 0.198 -0.414 ± 0.265 -1.176 ± 0.192 #> [45] -0.361 ± 0.119 -0.581 ± 0.130 -0.302 ± 0.128 -0.324 ± 0.085 #> [49] -0.319 ± 0.082 -1.286 ± 0.332 -0.289 ± 0.096 -0.833 ± 0.144 #> [53] -0.399 ± 0.130 -0.373 ± 0.143 -0.384 ± 0.136 -0.319 ± 0.193 #> [57] -0.658 ± 0.119 -0.950 ± 0.352 -1.362 ± 0.345 -0.977 ± 0.162 #> [61] -0.542 ± 0.100 -0.868 ± 0.309 -0.116 ± 0.075 -0.904 ± 0.248 #> [65] -2.034 ± 0.615 -0.507 ± 0.138 -0.276 ± 0.084 -1.065 ± 0.239 #> [69] -0.435 ± 0.086 -0.638 ± 0.236 -0.613 ± 0.206 -0.464 ± 0.171 #> [73] -1.490 ± 0.370 -0.948 ± 0.197 -1.133 ± 0.386 -0.375 ± 0.141 #> [77] -0.880 ± 0.140 -0.491 ± 0.175 -0.766 ± 0.191 -0.540 ± 0.195 #> [81] -0.163 ± 0.101 -0.223 ± 0.139 -0.343 ± 0.084 -0.275 ± 0.094 #> [85] -0.131 ± 0.077 -1.130 ± 0.323 -0.823 ± 0.129 -0.782 ± 0.241 #> [89] -1.283 ± 0.323 -0.260 ± 0.135 -0.386 ± 0.130 -1.504 ± 0.354 #> [93] -0.735 ± 0.217 -0.318 ± 0.090 -0.386 ± 0.112 -1.576 ± 0.295 #> [97] -0.431 ± 0.102 -1.052 ± 0.371 -0.695 ± 0.142 -0.392 ± 0.099 #>
posterior::as_draws_list(fit)
#> # A draws_list: 1000 iterations, 4 chains, and 105 variables #> #> [chain = 1] #> $lp__ #> [1] -67 -65 -67 -65 -64 -67 -65 -66 -66 -65 #> #> $alpha #> [1] 0.46 0.21 0.31 0.45 0.46 0.27 0.34 0.56 0.28 0.52 #> #> $`beta[1]` #> [1] -0.43 -0.64 -1.16 -0.62 -0.56 -1.13 -0.66 -0.88 -0.42 -0.91 #> #> $`beta[2]` #> [1] -0.327 -0.506 -0.364 -0.489 -0.387 0.114 -0.647 -0.539 0.094 -0.208 #> #> #> [chain = 2] #> $lp__ #> [1] -65 -66 -68 -67 -67 -66 -67 -68 -67 -65 #> #> $alpha #> [1] 0.450 0.098 0.712 0.242 0.848 0.382 0.178 0.062 0.682 0.147 #> #> $`beta[1]` #> [1] -0.55 -0.87 -0.35 -1.15 -0.37 -0.23 -1.14 -0.71 -0.93 -0.65 #> #> $`beta[2]` #> [1] -0.575 0.026 -0.126 -0.319 -0.397 -0.109 -0.369 -0.415 -0.210 -0.334 #> #> # ... with 990 more iterations, and 2 more chains, and 101 more variables
# }