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 -66 -65 -65 -64
#>         2 -66 -65 -64 -66
#>         3 -65 -66 -65 -66
#>         4 -65 -65 -66 -65
#>         5 -67 -66 -66 -67
#> 
#> , , variable = alpha
#> 
#>          chain
#> iteration    1    2    3     4
#>         1 0.37 0.21 0.66 0.314
#>         2 0.34 0.35 0.28 0.748
#>         3 0.58 0.25 0.41 0.023
#>         4 0.20 0.37 0.45 0.297
#>         5 0.24 0.37 0.72 0.328
#> 
#> , , variable = beta[1]
#> 
#>          chain
#> iteration     1     2     3     4
#>         1 -0.54 -0.69 -0.71 -0.55
#>         2 -0.98 -0.91 -0.67 -0.70
#>         3 -0.77 -0.30 -0.82 -0.63
#>         4 -0.41 -0.66 -1.07 -0.75
#>         5 -0.26 -0.78 -0.72 -0.45
#> 
#> , , variable = beta[2]
#> 
#>          chain
#> iteration      1      2      3      4
#>         1 -0.738 -0.427 -0.183 -0.256
#>         2 -0.545 -0.076 -0.220 -0.433
#>         3 -0.168 -0.494 -0.400 -0.201
#>         4 -0.281 -0.029 -0.388  0.072
#>         5  0.054 -0.460 -0.027  0.122
#> 
#> # ... 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 ± 1.4 
#> 
#> $alpha: rvar<1000,4>[1] mean ± sd:
#> [1] 0.37 ± 0.22 
#> 
#> $beta: rvar<1000,4>[3] mean ± sd:
#> [1] -0.67 ± 0.25  -0.27 ± 0.22   0.67 ± 0.27 
#> 
#> $log_lik: rvar<1000,4>[100] mean ± sd:
#>   [1] -0.517 ± 0.101  -0.404 ± 0.151  -0.497 ± 0.215  -0.451 ± 0.152 
#>   [5] -1.177 ± 0.282  -0.595 ± 0.194  -0.639 ± 0.124  -0.281 ± 0.134 
#>   [9] -0.696 ± 0.170  -0.736 ± 0.229  -0.284 ± 0.127  -0.503 ± 0.249 
#>  [13] -0.654 ± 0.209  -0.361 ± 0.173  -0.281 ± 0.106  -0.278 ± 0.087 
#>  [17] -1.585 ± 0.286  -0.482 ± 0.109  -0.234 ± 0.076  -0.114 ± 0.078 
#>  [21] -0.214 ± 0.088  -0.572 ± 0.150  -0.333 ± 0.139  -0.137 ± 0.067 
#>  [25] -0.455 ± 0.121  -1.516 ± 0.346  -0.310 ± 0.123  -0.447 ± 0.085 
#>  [29] -0.723 ± 0.222  -0.697 ± 0.195  -0.492 ± 0.165  -0.426 ± 0.108 
#>  [33] -0.410 ± 0.128  -0.064 ± 0.050  -0.584 ± 0.182  -0.327 ± 0.132 
#>  [37] -0.697 ± 0.220  -0.313 ± 0.145  -0.180 ± 0.109  -0.682 ± 0.131 
#>  [41] -1.124 ± 0.254  -0.931 ± 0.201  -0.413 ± 0.269  -1.172 ± 0.186 
#>  [45] -0.360 ± 0.119  -0.582 ± 0.132  -0.307 ± 0.130  -0.326 ± 0.084 
#>  [49] -0.321 ± 0.080  -1.289 ± 0.340  -0.290 ± 0.095  -0.834 ± 0.146 
#>  [53] -0.405 ± 0.132  -0.371 ± 0.144  -0.387 ± 0.137  -0.322 ± 0.189 
#>  [57] -0.657 ± 0.118  -0.947 ± 0.345  -1.358 ± 0.345  -0.977 ± 0.161 
#>  [61] -0.542 ± 0.101  -0.881 ± 0.324  -0.118 ± 0.073  -0.899 ± 0.249 
#>  [65] -1.999 ± 0.597  -0.509 ± 0.135  -0.278 ± 0.082  -1.056 ± 0.235 
#>  [69] -0.437 ± 0.085  -0.641 ± 0.239  -0.608 ± 0.208  -0.462 ± 0.171 
#>  [73] -1.479 ± 0.361  -0.947 ± 0.192  -1.146 ± 0.401  -0.373 ± 0.139 
#>  [77] -0.875 ± 0.138  -0.489 ± 0.178  -0.762 ± 0.186  -0.544 ± 0.202 
#>  [81] -0.164 ± 0.099  -0.225 ± 0.139  -0.345 ± 0.082  -0.277 ± 0.091 
#>  [85] -0.131 ± 0.075  -1.126 ± 0.328  -0.822 ± 0.130  -0.773 ± 0.243 
#>  [89] -1.276 ± 0.323  -0.261 ± 0.132  -0.386 ± 0.129  -1.490 ± 0.344 
#>  [93] -0.740 ± 0.216  -0.319 ± 0.088  -0.391 ± 0.113  -1.566 ± 0.280 
#>  [97] -0.433 ± 0.103  -1.050 ± 0.364  -0.693 ± 0.143  -0.392 ± 0.099 
#> 
posterior::as_draws_list(fit)
#> # A draws_list: 1000 iterations, 4 chains, and 105 variables
#> 
#> [chain = 1]
#> $lp__
#>  [1] -66 -66 -65 -65 -67 -65 -64 -64 -66 -64
#> 
#> $alpha
#>  [1] 0.37 0.34 0.58 0.20 0.24 0.48 0.36 0.35 0.21 0.35
#> 
#> $`beta[1]`
#>  [1] -0.54 -0.98 -0.77 -0.41 -0.26 -0.80 -0.62 -0.74 -0.55 -0.51
#> 
#> $`beta[2]`
#>  [1] -0.738 -0.545 -0.168 -0.281  0.054 -0.315 -0.204 -0.103  0.067 -0.446
#> 
#> 
#> [chain = 2]
#> $lp__
#>  [1] -65 -65 -66 -65 -66 -68 -66 -68 -65 -65
#> 
#> $alpha
#>  [1]  0.211  0.348  0.245  0.371  0.366  0.050  0.135 -0.055  0.417  0.247
#> 
#> $`beta[1]`
#>  [1] -0.69 -0.91 -0.30 -0.66 -0.78 -0.47 -0.86 -0.85 -0.32 -0.72
#> 
#> $`beta[2]`
#>  [1] -0.427 -0.076 -0.494 -0.029 -0.460  0.199 -0.272  0.096 -0.201 -0.249
#> 
#> # ... with 990 more iterations, and 2 more chains, and 101 more variables
# }