Run CmdStan's stansummary and diagnose utilities. These are
documented in the CmdStan Guide:
These methods can be used for models fit using
$sample(),
$variational(), or
$pathfinder(). Much of the output is currently
only relevant for models fit using $sample(). These methods are not
available for optimization, Laplace approximation, or standalone generated
quantities.
See the $summary() for computing similar summaries in R rather than calling CmdStan's utilities.
Value
Both methods invisibly return a list containing the command's exit status, standard output, and standard error.
Examples
# \dontrun{
fit <- cmdstanr_example("logistic")
fit$cmdstan_diagnose()
#> Checking sampler transitions treedepth.
#> Treedepth satisfactory for all transitions.
#>
#> Checking sampler transitions for divergences.
#> No divergent transitions found.
#>
#> Checking E-BFMI - sampler transitions HMC potential energy.
#> E-BFMI satisfactory.
#>
#> Rank-normalized split effective sample size satisfactory for all parameters.
#>
#> Rank-normalized split R-hat values satisfactory for all parameters.
#>
#> Processing complete, no problems detected.
fit$cmdstan_summary()
#> Inference for Stan model: logistic_model
#> 4 chains: each with iter=1000; warmup=1000; thin=1; 1000 iterations saved.
#>
#> Warmup took (0.012, 0.013, 0.013, 0.012) seconds, 0.050 seconds total
#> Sampling took (0.039, 0.041, 0.038, 0.039) seconds, 0.16 seconds total
#>
#> Mean MCSE StdDev MAD 5% 50% 95% ESS_bulk ESS_tail ESS_bulk/s R_hat
#>
#> lp__ -6.6e+01 2.9e-02 1.4 1.2 -69 -6.6e+01 -6.4e+01 2272 3116 14469 1.0
#> accept_stat__ 0.89 1.6e-03 0.12 0.092 0.65 0.94 1.0 5932 4431 37781 1.0
#> stepsize__ 0.79 nan 0.052 0.025 0.71 0.81 0.84 nan nan nan nan
#> treedepth__ 2.3 6.0e-02 0.51 0.00 2.0 2.0 3.0 168 171 1071 1.0
#> n_leapfrog__ 4.8 1.7e-01 2.0 0.00 3.0 3.0 7.0 345 398 2199 1.0
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 0.00 nan nan nan nan
#> energy__ 68 4.6e-02 2.0 1.9 65 68 72 1859 2525 11840 1.0
#>
#> alpha 3.8e-01 3.4e-03 0.22 0.22 0.020 3.8e-01 7.3e-01 4137 3243 26352 1.0
#> beta[1] -6.6e-01 3.7e-03 0.25 0.25 -1.1 -6.6e-01 -2.6e-01 4658 3409 29670 1.0
#> beta[2] -2.7e-01 3.5e-03 0.22 0.22 -0.64 -2.7e-01 8.6e-02 4157 3267 26477 1.00
#> beta[3] 6.8e-01 4.1e-03 0.26 0.26 0.26 6.7e-01 1.1e+00 4151 3017 26438 1.0
#> log_lik[1] -5.2e-01 1.5e-03 0.099 0.095 -0.69 -5.1e-01 -3.7e-01 4207 3082 26795 1.0
#> log_lik[2] -4.0e-01 2.2e-03 0.15 0.14 -0.67 -3.8e-01 -2.0e-01 4635 3229 29523 1.00
#> log_lik[3] -5.0e-01 3.4e-03 0.22 0.20 -0.90 -4.7e-01 -2.1e-01 4355 3363 27736 1.00
#> log_lik[4] -4.5e-01 2.4e-03 0.15 0.14 -0.72 -4.3e-01 -2.4e-01 3780 3077 24075 1.0
#> log_lik[5] -1.2e+00 4.1e-03 0.28 0.28 -1.6 -1.2e+00 -7.5e-01 4620 3392 29426 1.0
#> log_lik[6] -5.9e-01 2.8e-03 0.19 0.19 -0.95 -5.7e-01 -3.2e-01 4644 3078 29582 1.0
#> log_lik[7] -6.4e-01 1.9e-03 0.12 0.13 -0.86 -6.3e-01 -4.5e-01 4358 2734 27757 1.0
#> log_lik[8] -2.8e-01 2.1e-03 0.13 0.12 -0.53 -2.6e-01 -1.1e-01 3967 3078 25264 1.0
#> log_lik[9] -6.9e-01 2.6e-03 0.17 0.17 -0.99 -6.7e-01 -4.4e-01 4475 2761 28505 1.0
#> log_lik[10] -7.4e-01 3.7e-03 0.23 0.23 -1.1 -7.2e-01 -4.1e-01 4066 3129 25899 1.0
#> log_lik[11] -2.8e-01 2.0e-03 0.12 0.11 -0.52 -2.6e-01 -1.2e-01 3893 3041 24794 1.0
#> log_lik[12] -5.0e-01 3.5e-03 0.24 0.22 -0.93 -4.6e-01 -1.9e-01 4887 2910 31130 1.00
#> log_lik[13] -6.6e-01 3.1e-03 0.21 0.21 -1.0 -6.4e-01 -3.5e-01 4823 2757 30722 1.0
#> log_lik[14] -3.6e-01 2.7e-03 0.17 0.16 -0.69 -3.3e-01 -1.4e-01 4299 3194 27385 1.00
#> log_lik[15] -2.8e-01 1.6e-03 0.11 0.100 -0.47 -2.6e-01 -1.4e-01 4175 3464 26590 1.0
#> log_lik[16] -2.8e-01 1.4e-03 0.085 0.083 -0.43 -2.7e-01 -1.5e-01 3523 3033 22440 1.00
#> log_lik[17] -1.6e+00 4.7e-03 0.29 0.28 -2.1 -1.6e+00 -1.2e+00 3746 3299 23858 1.00
#> log_lik[18] -4.8e-01 1.7e-03 0.11 0.11 -0.67 -4.7e-01 -3.2e-01 4251 2907 27074 1.0
#> log_lik[19] -2.3e-01 1.2e-03 0.075 0.072 -0.36 -2.2e-01 -1.3e-01 3657 3188 23290 1.00
#> log_lik[20] -1.1e-01 1.3e-03 0.080 0.060 -0.26 -9.4e-02 -3.1e-02 4011 3330 25546 1.00
#> log_lik[21] -2.1e-01 1.4e-03 0.085 0.081 -0.37 -2.0e-01 -9.6e-02 3527 2989 22462 1.0
#> log_lik[22] -5.7e-01 2.3e-03 0.15 0.15 -0.84 -5.5e-01 -3.5e-01 4254 3017 27094 1.0
#> log_lik[23] -3.3e-01 2.1e-03 0.14 0.13 -0.58 -3.1e-01 -1.4e-01 4305 3327 27419 1.0
#> log_lik[24] -1.4e-01 1.1e-03 0.066 0.057 -0.25 -1.2e-01 -5.3e-02 3707 3117 23615 1.00
#> log_lik[25] -4.6e-01 1.9e-03 0.12 0.12 -0.67 -4.5e-01 -2.8e-01 4031 3190 25674 1.0
#> log_lik[26] -1.5e+00 5.0e-03 0.34 0.34 -2.1 -1.5e+00 -1.0e+00 4848 3516 30880 1.00
#> log_lik[27] -3.1e-01 1.9e-03 0.12 0.11 -0.53 -2.9e-01 -1.4e-01 3797 3114 24184 1.0
#> log_lik[28] -4.5e-01 1.3e-03 0.084 0.083 -0.59 -4.4e-01 -3.2e-01 3936 3109 25067 1.0
#> log_lik[29] -7.2e-01 3.3e-03 0.22 0.22 -1.1 -7.0e-01 -4.0e-01 4658 3277 29670 1.0
#> log_lik[30] -6.9e-01 2.8e-03 0.19 0.19 -1.0 -6.8e-01 -4.1e-01 4712 3173 30012 1.0
#> log_lik[31] -4.9e-01 2.5e-03 0.16 0.16 -0.79 -4.7e-01 -2.6e-01 4167 3246 26539 1.0
#> log_lik[32] -4.2e-01 1.6e-03 0.11 0.11 -0.62 -4.2e-01 -2.7e-01 4227 3069 26926 1.0
#> log_lik[33] -4.1e-01 1.9e-03 0.13 0.12 -0.64 -4.0e-01 -2.3e-01 4328 2792 27566 1.00
#> log_lik[34] -6.3e-02 8.3e-04 0.050 0.034 -0.15 -5.0e-02 -1.4e-02 3687 3186 23487 1.0
#> log_lik[35] -5.9e-01 2.7e-03 0.18 0.17 -0.92 -5.6e-01 -3.2e-01 5078 2979 32341 1.0
#> log_lik[36] -3.2e-01 1.9e-03 0.13 0.12 -0.56 -3.1e-01 -1.5e-01 4780 3628 30448 1.0
#> log_lik[37] -7.0e-01 3.4e-03 0.22 0.22 -1.1 -6.8e-01 -3.8e-01 4370 3426 27833 1.0
#> log_lik[38] -3.1e-01 2.4e-03 0.14 0.13 -0.59 -2.8e-01 -1.3e-01 3844 3195 24486 1.0
#> log_lik[39] -1.8e-01 1.8e-03 0.11 0.090 -0.39 -1.6e-01 -5.6e-02 4160 3420 26496 1.00
#> log_lik[40] -6.8e-01 2.0e-03 0.13 0.13 -0.91 -6.7e-01 -4.9e-01 4465 2771 28440 1.0
#> log_lik[41] -1.1e+00 4.1e-03 0.25 0.25 -1.6 -1.1e+00 -7.5e-01 3884 3163 24736 1.0
#> log_lik[42] -9.3e-01 3.0e-03 0.20 0.19 -1.3 -9.1e-01 -6.4e-01 4479 2574 28528 1.0
#> log_lik[43] -4.1e-01 3.7e-03 0.27 0.22 -0.93 -3.5e-01 -1.0e-01 6013 3308 38299 1.0
#> log_lik[44] -1.2e+00 3.0e-03 0.18 0.18 -1.5 -1.2e+00 -8.9e-01 3877 3329 24693 1.00
#> log_lik[45] -3.6e-01 1.8e-03 0.12 0.11 -0.58 -3.5e-01 -1.9e-01 4254 3299 27094 1.00
#> log_lik[46] -5.8e-01 1.9e-03 0.13 0.13 -0.80 -5.7e-01 -3.8e-01 4515 3012 28759 1.0
#> log_lik[47] -3.0e-01 2.0e-03 0.12 0.12 -0.54 -2.9e-01 -1.4e-01 3927 3013 25015 1.0
#> log_lik[48] -3.2e-01 1.3e-03 0.083 0.080 -0.47 -3.2e-01 -2.0e-01 4057 3437 25839 1.0
#> log_lik[49] -3.2e-01 1.3e-03 0.079 0.075 -0.46 -3.1e-01 -2.0e-01 3577 3087 22783 1.00
#> log_lik[50] -1.3e+00 4.6e-03 0.33 0.32 -1.9 -1.3e+00 -7.9e-01 5251 3352 33443 1.0
#> log_lik[51] -2.9e-01 1.4e-03 0.095 0.090 -0.46 -2.8e-01 -1.6e-01 4569 3653 29104 1.00
#> log_lik[52] -8.3e-01 2.2e-03 0.14 0.14 -1.1 -8.3e-01 -6.1e-01 4343 2891 27659 1.0
#> log_lik[53] -4.0e-01 2.1e-03 0.13 0.12 -0.64 -3.9e-01 -2.2e-01 3923 3189 24987 1.0
#> log_lik[54] -3.7e-01 2.2e-03 0.14 0.14 -0.64 -3.5e-01 -1.8e-01 4435 3237 28249 1.0
#> log_lik[55] -3.9e-01 2.0e-03 0.13 0.13 -0.63 -3.7e-01 -2.0e-01 4441 3223 28288 1.00
#> log_lik[56] -3.2e-01 2.7e-03 0.19 0.16 -0.67 -2.8e-01 -9.3e-02 5247 3615 33420 1.0
#> log_lik[57] -6.6e-01 1.9e-03 0.12 0.11 -0.85 -6.5e-01 -4.8e-01 4003 3110 25498 1.0
#> log_lik[58] -9.5e-01 5.1e-03 0.35 0.34 -1.6 -9.1e-01 -4.6e-01 4990 3418 31786 1.0
#> log_lik[59] -1.4e+00 5.2e-03 0.34 0.33 -1.9 -1.3e+00 -8.6e-01 4295 2826 27358 1.00
#> log_lik[60] -9.8e-01 2.5e-03 0.16 0.15 -1.2 -9.7e-01 -7.3e-01 4204 3159 26774 1.0
#> log_lik[61] -5.4e-01 1.5e-03 0.099 0.096 -0.71 -5.3e-01 -3.9e-01 4382 3156 27908 1.0
#> log_lik[62] -8.8e-01 4.5e-03 0.31 0.31 -1.5 -8.4e-01 -4.3e-01 4889 3193 31137 1.00
#> log_lik[63] -1.2e-01 1.2e-03 0.070 0.058 -0.25 -1.0e-01 -3.4e-02 3447 2933 21954 1.0
#> log_lik[64] -9.0e-01 3.7e-03 0.25 0.24 -1.4 -8.8e-01 -5.4e-01 4857 3452 30936 1.0
#> log_lik[65] -2.0e+00 9.6e-03 0.58 0.59 -3.0 -2.0e+00 -1.1e+00 3771 3042 24017 1.0
#> log_lik[66] -5.1e-01 2.0e-03 0.13 0.13 -0.76 -5.0e-01 -3.1e-01 4684 3401 29832 1.0
#> log_lik[67] -2.8e-01 1.3e-03 0.081 0.078 -0.42 -2.7e-01 -1.6e-01 3844 3084 24484 1.00
#> log_lik[68] -1.1e+00 3.7e-03 0.24 0.23 -1.5 -1.0e+00 -7.1e-01 4214 3213 26843 1.0
#> log_lik[69] -4.4e-01 1.3e-03 0.084 0.083 -0.58 -4.3e-01 -3.1e-01 3853 3134 24543 1.0
#> log_lik[70] -6.4e-01 3.2e-03 0.23 0.23 -1.1 -6.1e-01 -3.1e-01 5312 3563 33833 1.0
#> log_lik[71] -6.1e-01 3.0e-03 0.21 0.20 -0.99 -5.8e-01 -3.1e-01 5136 2908 32714 1.0
#> log_lik[72] -4.6e-01 2.6e-03 0.17 0.17 -0.78 -4.4e-01 -2.2e-01 4627 2779 29469 1.0
#> log_lik[73] -1.5e+00 5.7e-03 0.35 0.35 -2.1 -1.5e+00 -9.4e-01 3962 3032 25236 1.0
#> log_lik[74] -9.5e-01 2.9e-03 0.19 0.19 -1.3 -9.3e-01 -6.6e-01 4323 3408 27537 1.0
#> log_lik[75] -1.1e+00 5.6e-03 0.39 0.38 -1.9 -1.1e+00 -5.7e-01 4924 3142 31360 1.0
#> log_lik[76] -3.7e-01 2.2e-03 0.14 0.13 -0.63 -3.6e-01 -1.8e-01 4220 3362 26879 1.00
#> log_lik[77] -8.8e-01 2.2e-03 0.14 0.14 -1.1 -8.7e-01 -6.7e-01 4221 3049 26883 1.0
#> log_lik[78] -4.9e-01 2.5e-03 0.18 0.16 -0.81 -4.6e-01 -2.4e-01 5158 3091 32852 1.0
#> log_lik[79] -7.6e-01 3.0e-03 0.18 0.18 -1.1 -7.5e-01 -4.8e-01 3954 3057 25183 1.0
#> log_lik[80] -5.4e-01 2.7e-03 0.20 0.19 -0.90 -5.2e-01 -2.7e-01 5272 3226 33578 1.0
#> log_lik[81] -1.6e-01 1.5e-03 0.097 0.081 -0.35 -1.4e-01 -5.0e-02 3965 3040 25255 1.0
#> log_lik[82] -2.2e-01 2.1e-03 0.14 0.12 -0.49 -1.9e-01 -6.7e-02 4478 3496 28525 1.00
#> log_lik[83] -3.4e-01 1.3e-03 0.081 0.078 -0.49 -3.4e-01 -2.3e-01 3778 3274 24066 1.00
#> log_lik[84] -2.8e-01 1.4e-03 0.090 0.088 -0.44 -2.6e-01 -1.5e-01 3922 2958 24979 1.00
#> log_lik[85] -1.3e-01 1.2e-03 0.075 0.062 -0.28 -1.2e-01 -4.4e-02 3910 3178 24905 1.00
#> log_lik[86] -1.1e+00 4.6e-03 0.31 0.32 -1.7 -1.1e+00 -6.5e-01 4830 3206 30767 1.0
#> log_lik[87] -8.2e-01 1.9e-03 0.13 0.13 -1.0 -8.2e-01 -6.2e-01 4475 3356 28506 1.0
#> log_lik[88] -7.8e-01 3.6e-03 0.24 0.24 -1.2 -7.5e-01 -4.2e-01 4757 2913 30300 1.0
#> log_lik[89] -1.3e+00 4.8e-03 0.31 0.31 -1.8 -1.2e+00 -8.0e-01 4340 3279 27643 1.00
#> log_lik[90] -2.6e-01 2.1e-03 0.13 0.11 -0.51 -2.3e-01 -9.7e-02 4207 3059 26794 1.0
#> log_lik[91] -3.9e-01 2.0e-03 0.13 0.12 -0.62 -3.7e-01 -2.0e-01 4388 3039 27949 1.0
#> log_lik[92] -1.5e+00 5.3e-03 0.33 0.34 -2.1 -1.5e+00 -9.8e-01 4144 3272 26393 1.0
#> log_lik[93] -7.4e-01 3.4e-03 0.22 0.21 -1.1 -7.1e-01 -4.3e-01 4110 2987 26180 1.0
#> log_lik[94] -3.2e-01 1.4e-03 0.087 0.085 -0.48 -3.1e-01 -1.9e-01 3887 3376 24759 1.00
#> log_lik[95] -3.9e-01 1.8e-03 0.11 0.11 -0.59 -3.8e-01 -2.3e-01 3787 3096 24120 1.0
#> log_lik[96] -1.6e+00 4.6e-03 0.28 0.27 -2.0 -1.6e+00 -1.1e+00 3667 3289 23358 1.00
#> log_lik[97] -4.3e-01 1.5e-03 0.10 0.099 -0.61 -4.2e-01 -2.8e-01 4682 3216 29823 1.0
#> log_lik[98] -1.1e+00 5.3e-03 0.37 0.36 -1.7 -1.0e+00 -5.3e-01 5051 3389 32171 1.0
#> log_lik[99] -7.0e-01 2.1e-03 0.14 0.14 -0.94 -6.8e-01 -4.8e-01 4548 2904 28969 1.0
#> log_lik[100] -3.9e-01 1.5e-03 0.098 0.095 -0.57 -3.8e-01 -2.5e-01 4409 3243 28084 1.0
#>
#> Samples were drawn using hmc with nuts.
#> For each parameter, ESS_bulk and ESS_tail measure the effective sample size for the entire sample (bulk) and for the .05 and .95 tails (tail),
#> and R_hat measures the potential scale reduction on split chains. At convergence R_hat will be very close to 1.00.
# }