Run CmdStan's stansummary and diagnose utilities. These are
documented in the CmdStan Guide:
https://mc-stan.org/docs/cmdstan-guide/stansummary.html
https://mc-stan.org/docs/cmdstan-guide/diagnose.html
Although these methods can be used for models fit using the
$variational() method, much of the output is
currently only relevant for models fit using the
$sample() method.
See the $summary() for computing similar summaries in R rather than calling CmdStan's utilites.
cmdstan_summary(flags = NULL)
cmdstan_diagnose()An optional character vector of flags (e.g.
flags = c("--sig_figs=1")).
# \dontrun{
fit <- cmdstanr_example("logistic")
fit$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 (0.023, 0.023, 0.022, 0.021) seconds, 0.089 seconds total
#> Sampling took (0.078, 0.073, 0.074, 0.072) seconds, 0.30 seconds total
#>
#> Mean MCSE StdDev MAD 5% 50% 95% ESS_bulk ESS_tail R_hat
#>
#> lp__ -6.6e+01 3.1e-02 1.4 1.2 -69 -6.6e+01 -6.4e+01 2077 2863 1.0
#> accept_stat__ 0.91 1.4e-03 0.10 0.072 0.71 0.95 1.0 5781 4282 1.0
#> stepsize__ 0.72 nan 0.040 0.039 0.66 0.73 0.77 nan nan nan
#> treedepth__ 2.4 1.5e-02 0.53 0.00 2.0 2.0 3.0 1630 1360 1.0
#> n_leapfrog__ 5.3 9.5e-02 2.0 0.00 3.0 7.0 7.0 3307 884 1.0
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 0.00 nan nan nan
#> energy__ 68 4.7e-02 2.0 1.8 65 68 72 1774 2711 1.0
#>
#> alpha 3.7e-01 3.4e-03 0.22 0.22 0.028 3.7e-01 7.3e-01 4093 2683 1.0
#> beta[1] -6.6e-01 3.9e-03 0.25 0.25 -1.1 -6.6e-01 -2.6e-01 4257 3329 1.0
#> beta[2] -2.7e-01 3.6e-03 0.22 0.23 -0.64 -2.6e-01 9.8e-02 3945 3224 1.0
#> beta[3] 6.7e-01 4.3e-03 0.27 0.27 0.25 6.6e-01 1.1e+00 3898 2767 1.00
#> log_lik[1] -5.2e-01 1.5e-03 0.098 0.098 -0.69 -5.1e-01 -3.7e-01 4210 2902 1.0
#> log_lik[2] -4.0e-01 2.2e-03 0.14 0.14 -0.67 -3.9e-01 -2.0e-01 4291 2935 1.0
#> log_lik[3] -5.0e-01 3.3e-03 0.21 0.20 -0.90 -4.6e-01 -2.1e-01 4240 2834 1.0
#> log_lik[4] -4.5e-01 2.5e-03 0.16 0.15 -0.73 -4.3e-01 -2.4e-01 3795 3034 1.0
#> log_lik[5] -1.2e+00 4.4e-03 0.28 0.28 -1.7 -1.2e+00 -7.5e-01 4168 2944 1.0
#> log_lik[6] -5.9e-01 3.0e-03 0.19 0.19 -0.93 -5.8e-01 -3.3e-01 3850 2768 1.00
#> log_lik[7] -6.4e-01 1.9e-03 0.13 0.12 -0.87 -6.3e-01 -4.5e-01 4232 3044 1.0
#> log_lik[8] -2.8e-01 2.2e-03 0.13 0.12 -0.53 -2.6e-01 -1.1e-01 3612 3023 1.0
#> log_lik[9] -6.9e-01 2.6e-03 0.16 0.16 -0.98 -6.8e-01 -4.5e-01 4078 3164 1.0
#> log_lik[10] -7.4e-01 3.7e-03 0.23 0.23 -1.2 -7.1e-01 -4.0e-01 4026 2967 1.0
#> log_lik[11] -2.8e-01 2.1e-03 0.13 0.12 -0.52 -2.6e-01 -1.2e-01 3419 2597 1.0
#> log_lik[12] -5.0e-01 3.6e-03 0.24 0.22 -0.94 -4.7e-01 -1.9e-01 4359 3260 1.0
#> log_lik[13] -6.5e-01 3.3e-03 0.21 0.21 -1.0 -6.3e-01 -3.6e-01 4071 2663 1.0
#> log_lik[14] -3.6e-01 2.7e-03 0.17 0.16 -0.68 -3.3e-01 -1.4e-01 4196 2966 1.0
#> log_lik[15] -2.8e-01 1.7e-03 0.11 0.10 -0.47 -2.6e-01 -1.4e-01 4062 2457 1.0
#> log_lik[16] -2.8e-01 1.5e-03 0.087 0.085 -0.44 -2.7e-01 -1.5e-01 3304 2815 1.0
#> log_lik[17] -1.6e+00 4.8e-03 0.29 0.29 -2.1 -1.6e+00 -1.1e+00 3624 2964 1.0
#> log_lik[18] -4.8e-01 1.7e-03 0.10 0.10 -0.66 -4.8e-01 -3.2e-01 3932 2824 1.0
#> log_lik[19] -2.4e-01 1.3e-03 0.075 0.074 -0.37 -2.3e-01 -1.3e-01 3554 3085 1.0
#> log_lik[20] -1.1e-01 1.3e-03 0.079 0.061 -0.27 -9.5e-02 -3.0e-02 4170 3008 1.0
#> log_lik[21] -2.2e-01 1.5e-03 0.089 0.084 -0.38 -2.0e-01 -9.7e-02 3302 2740 1.0
#> log_lik[22] -5.7e-01 2.4e-03 0.14 0.14 -0.83 -5.6e-01 -3.6e-01 3779 3160 1.0
#> log_lik[23] -3.3e-01 2.2e-03 0.14 0.13 -0.58 -3.1e-01 -1.5e-01 3934 3316 1.0
#> log_lik[24] -1.4e-01 1.1e-03 0.067 0.061 -0.27 -1.3e-01 -5.4e-02 3656 3128 1.0
#> log_lik[25] -4.6e-01 1.9e-03 0.12 0.12 -0.68 -4.4e-01 -2.8e-01 4029 2992 1.0
#> log_lik[26] -1.5e+00 5.2e-03 0.34 0.33 -2.1 -1.5e+00 -9.9e-01 4325 3387 1.0
#> log_lik[27] -3.1e-01 2.1e-03 0.12 0.12 -0.54 -2.9e-01 -1.5e-01 3375 2563 1.0
#> log_lik[28] -4.5e-01 1.3e-03 0.082 0.082 -0.59 -4.4e-01 -3.2e-01 3776 2975 1.0
#> log_lik[29] -7.3e-01 3.3e-03 0.23 0.23 -1.1 -7.0e-01 -3.9e-01 4688 3192 1.0
#> log_lik[30] -7.0e-01 2.9e-03 0.18 0.18 -1.0 -6.8e-01 -4.2e-01 4197 3162 1.0
#> log_lik[31] -4.9e-01 2.7e-03 0.16 0.16 -0.79 -4.7e-01 -2.6e-01 3604 2908 1.0
#> log_lik[32] -4.3e-01 1.7e-03 0.11 0.11 -0.62 -4.2e-01 -2.7e-01 3793 2685 1.0
#> log_lik[33] -4.1e-01 2.0e-03 0.13 0.12 -0.65 -3.9e-01 -2.3e-01 4149 2968 1.0
#> log_lik[34] -6.6e-02 8.7e-04 0.052 0.038 -0.16 -5.2e-02 -1.3e-02 3571 2878 1.0
#> log_lik[35] -5.9e-01 2.8e-03 0.19 0.19 -0.92 -5.6e-01 -3.2e-01 4485 3033 1.0
#> log_lik[36] -3.3e-01 2.0e-03 0.13 0.12 -0.56 -3.1e-01 -1.5e-01 4361 3167 1.00
#> log_lik[37] -7.0e-01 3.4e-03 0.23 0.22 -1.1 -6.7e-01 -3.7e-01 4430 3268 1.0
#> log_lik[38] -3.2e-01 2.5e-03 0.15 0.14 -0.61 -2.9e-01 -1.2e-01 3889 3062 1.0
#> log_lik[39] -1.8e-01 1.7e-03 0.11 0.089 -0.38 -1.6e-01 -5.5e-02 4256 2773 1.0
#> log_lik[40] -6.8e-01 2.0e-03 0.12 0.12 -0.90 -6.7e-01 -4.9e-01 4119 3108 1.0
#> log_lik[41] -1.1e+00 4.3e-03 0.25 0.25 -1.6 -1.1e+00 -7.5e-01 3475 2646 1.0
#> log_lik[42] -9.3e-01 3.0e-03 0.19 0.19 -1.3 -9.2e-01 -6.3e-01 4203 3085 1.0
#> log_lik[43] -4.1e-01 3.9e-03 0.26 0.22 -0.91 -3.5e-01 -1.1e-01 4971 3223 1.00
#> log_lik[44] -1.2e+00 2.9e-03 0.18 0.18 -1.5 -1.2e+00 -8.9e-01 3992 2815 1.0
#> log_lik[45] -3.6e-01 1.8e-03 0.12 0.11 -0.57 -3.4e-01 -1.9e-01 4297 3077 1.00
#> log_lik[46] -5.8e-01 1.9e-03 0.13 0.12 -0.81 -5.7e-01 -3.9e-01 4228 3113 1.0
#> log_lik[47] -3.1e-01 2.1e-03 0.13 0.12 -0.55 -2.9e-01 -1.4e-01 3831 2919 1.0
#> log_lik[48] -3.3e-01 1.3e-03 0.082 0.081 -0.47 -3.2e-01 -2.1e-01 3891 3283 1.0
#> log_lik[49] -3.2e-01 1.3e-03 0.079 0.078 -0.46 -3.2e-01 -2.0e-01 3475 2682 1.0
#> log_lik[50] -1.3e+00 4.9e-03 0.32 0.32 -1.8 -1.3e+00 -7.9e-01 4610 3388 1.0
#> log_lik[51] -2.9e-01 1.4e-03 0.093 0.090 -0.46 -2.8e-01 -1.6e-01 4207 3215 1.0
#> log_lik[52] -8.3e-01 2.2e-03 0.14 0.14 -1.1 -8.3e-01 -6.1e-01 4205 3039 1.0
#> log_lik[53] -4.1e-01 2.2e-03 0.13 0.12 -0.64 -3.9e-01 -2.2e-01 3463 2821 1.0
#> log_lik[54] -3.7e-01 2.2e-03 0.14 0.13 -0.63 -3.5e-01 -1.8e-01 4357 3109 1.00
#> log_lik[55] -3.9e-01 2.1e-03 0.13 0.13 -0.63 -3.7e-01 -2.0e-01 4184 2870 1.0
#> log_lik[56] -3.2e-01 2.8e-03 0.19 0.17 -0.67 -2.8e-01 -9.4e-02 4725 2892 1.0
#> log_lik[57] -6.6e-01 1.8e-03 0.12 0.12 -0.86 -6.5e-01 -4.8e-01 4204 3006 1.0
#> log_lik[58] -9.5e-01 5.4e-03 0.36 0.35 -1.6 -9.0e-01 -4.5e-01 4724 2916 1.0
#> log_lik[59] -1.4e+00 5.4e-03 0.34 0.33 -2.0 -1.3e+00 -8.4e-01 4126 2944 1.0
#> log_lik[60] -9.8e-01 2.4e-03 0.16 0.16 -1.3 -9.7e-01 -7.3e-01 4237 3028 1.0
#> log_lik[61] -5.4e-01 1.5e-03 0.097 0.096 -0.71 -5.4e-01 -3.9e-01 4260 3064 1.0
#> log_lik[62] -8.8e-01 4.8e-03 0.31 0.31 -1.4 -8.5e-01 -4.4e-01 4420 3420 1.0
#> log_lik[63] -1.2e-01 1.3e-03 0.075 0.063 -0.26 -1.0e-01 -3.4e-02 3288 2867 1.0
#> log_lik[64] -9.0e-01 3.6e-03 0.24 0.23 -1.3 -8.7e-01 -5.5e-01 4286 3272 1.0
#> log_lik[65] -2.0e+00 1.0e-02 0.59 0.61 -3.0 -2.0e+00 -1.1e+00 3390 2645 1.0
#> log_lik[66] -5.1e-01 2.1e-03 0.14 0.13 -0.75 -5.0e-01 -3.1e-01 4289 2934 1.0
#> log_lik[67] -2.8e-01 1.3e-03 0.081 0.080 -0.42 -2.7e-01 -1.6e-01 3650 3029 1.0
#> log_lik[68] -1.1e+00 3.7e-03 0.23 0.22 -1.5 -1.0e+00 -7.1e-01 3844 3244 1.0
#> log_lik[69] -4.4e-01 1.4e-03 0.083 0.084 -0.58 -4.3e-01 -3.1e-01 3763 2707 1.0
#> log_lik[70] -6.4e-01 3.4e-03 0.23 0.21 -1.1 -6.1e-01 -3.2e-01 4692 3113 1.0
#> log_lik[71] -6.1e-01 3.3e-03 0.21 0.21 -0.99 -5.8e-01 -3.1e-01 4276 3089 1.0
#> log_lik[72] -4.6e-01 2.6e-03 0.17 0.16 -0.78 -4.4e-01 -2.3e-01 4326 3218 1.0
#> log_lik[73] -1.5e+00 6.0e-03 0.37 0.37 -2.1 -1.4e+00 -9.1e-01 3893 3087 1.0
#> log_lik[74] -9.5e-01 2.9e-03 0.19 0.20 -1.3 -9.3e-01 -6.5e-01 4399 2879 1.0
#> log_lik[75] -1.1e+00 5.8e-03 0.38 0.38 -1.8 -1.1e+00 -5.8e-01 4471 3388 1.0
#> log_lik[76] -3.7e-01 2.1e-03 0.14 0.13 -0.62 -3.5e-01 -1.8e-01 4223 2860 1.00
#> log_lik[77] -8.8e-01 2.2e-03 0.14 0.14 -1.1 -8.7e-01 -6.7e-01 3901 2655 1.0
#> log_lik[78] -4.9e-01 2.6e-03 0.17 0.16 -0.80 -4.6e-01 -2.5e-01 4623 3544 1.00
#> log_lik[79] -7.6e-01 3.0e-03 0.19 0.19 -1.1 -7.5e-01 -4.8e-01 4156 3125 1.0
#> log_lik[80] -5.4e-01 2.8e-03 0.19 0.18 -0.88 -5.2e-01 -2.7e-01 4708 3185 1.0
#> log_lik[81] -1.7e-01 1.7e-03 0.10 0.084 -0.37 -1.4e-01 -4.9e-02 4002 3125 1.0
#> log_lik[82] -2.2e-01 2.1e-03 0.14 0.11 -0.48 -1.9e-01 -6.6e-02 4234 2703 1.0
#> log_lik[83] -3.5e-01 1.3e-03 0.080 0.079 -0.49 -3.4e-01 -2.2e-01 3872 2864 1.00
#> log_lik[84] -2.8e-01 1.5e-03 0.091 0.087 -0.44 -2.7e-01 -1.5e-01 4033 2778 1.0
#> log_lik[85] -1.3e-01 1.2e-03 0.075 0.063 -0.28 -1.2e-01 -4.3e-02 4008 3071 1.0
#> log_lik[86] -1.1e+00 4.8e-03 0.31 0.30 -1.7 -1.1e+00 -6.7e-01 4424 2829 1.0
#> log_lik[87] -8.2e-01 1.9e-03 0.13 0.12 -1.0 -8.1e-01 -6.3e-01 4199 3091 1.0
#> log_lik[88] -7.7e-01 3.9e-03 0.24 0.24 -1.2 -7.4e-01 -4.3e-01 3979 2725 1.00
#> log_lik[89] -1.3e+00 5.0e-03 0.32 0.31 -1.8 -1.2e+00 -8.0e-01 4105 2643 1.0
#> log_lik[90] -2.7e-01 2.2e-03 0.14 0.12 -0.54 -2.4e-01 -9.4e-02 4059 3128 1.0
#> log_lik[91] -3.9e-01 2.0e-03 0.13 0.12 -0.63 -3.7e-01 -2.0e-01 4231 3276 1.0
#> log_lik[92] -1.5e+00 5.5e-03 0.34 0.34 -2.1 -1.5e+00 -9.7e-01 3905 2575 1.0
#> log_lik[93] -7.4e-01 3.5e-03 0.22 0.22 -1.1 -7.2e-01 -4.2e-01 3995 2990 1.0
#> log_lik[94] -3.2e-01 1.4e-03 0.087 0.084 -0.48 -3.1e-01 -1.9e-01 4054 2800 1.00
#> log_lik[95] -3.9e-01 1.9e-03 0.11 0.11 -0.59 -3.8e-01 -2.3e-01 3454 2901 1.0
#> log_lik[96] -1.6e+00 4.6e-03 0.28 0.28 -2.1 -1.5e+00 -1.1e+00 3721 3096 1.0
#> log_lik[97] -4.3e-01 1.5e-03 0.098 0.095 -0.60 -4.2e-01 -2.8e-01 4401 3224 1.0
#> log_lik[98] -1.0e+00 5.7e-03 0.38 0.37 -1.7 -1.0e+00 -5.2e-01 4630 2912 1.0
#> log_lik[99] -6.9e-01 2.1e-03 0.14 0.13 -0.94 -6.8e-01 -4.8e-01 4146 3139 1.0
#> log_lik[100] -3.9e-01 1.5e-03 0.096 0.096 -0.56 -3.8e-01 -2.5e-01 4330 2963 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.
# }