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()
#> Processing csv files: /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-1-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-2-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-3-7b6c9f.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpFBtN6X/logistic-202307251435-4-7b6c9f.csv
#>
#> 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.
#>
#> Effective sample size satisfactory.
#>
#> Split R-hat values satisfactory all parameters.
#>
#> Processing complete, no problems detected.
fit$cmdstan_summary()
#> Inference for Stan model: logistic_model
#> 4 chains: each with iter=(1000,1000,1000,1000); warmup=(0,0,0,0); thin=(1,1,1,1); 4000 iterations saved.
#>
#> Warmup took (0.022, 0.022, 0.022, 0.021) seconds, 0.087 seconds total
#> Sampling took (0.069, 0.072, 0.068, 0.066) seconds, 0.28 seconds total
#>
#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
#>
#> lp__ -6.6e+01 3.3e-02 1.4 -69 -6.6e+01 -6.4e+01 1875 6818 1.0
#> accept_stat__ 0.91 1.3e-03 0.099 0.71 0.95 1.0 5487 19952 1.0e+00
#> stepsize__ 0.75 2.8e-02 0.040 0.69 0.76 0.80 2.0 7.3 1.2e+13
#> treedepth__ 2.4 9.4e-03 0.53 2.0 2.0 3.0 3168 11520 1.0e+00
#> n_leapfrog__ 5.3 3.3e-02 2.0 3.0 7.0 7.0 3698 13446 1.0e+00
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
#> energy__ 68 5.2e-02 2.0 65 68 72 1501 5457 1.0e+00
#>
#> alpha 3.8e-01 3.3e-03 0.22 0.031 3.8e-01 7.3e-01 4281 15567 1.00
#> beta[1] -6.6e-01 3.9e-03 0.25 -1.1 -6.6e-01 -2.5e-01 4206 15294 1.00
#> beta[2] -2.8e-01 3.7e-03 0.23 -0.65 -2.8e-01 1.0e-01 3812 13860 1.0
#> beta[3] 6.8e-01 4.4e-03 0.27 0.25 6.8e-01 1.1e+00 3764 13688 1.00
#> log_lik[1] -5.2e-01 1.5e-03 0.097 -0.69 -5.1e-01 -3.7e-01 4137 15045 1.0
#> log_lik[2] -4.0e-01 2.4e-03 0.15 -0.69 -3.8e-01 -2.0e-01 4070 14798 1.00
#> log_lik[3] -5.0e-01 3.5e-03 0.22 -0.90 -4.7e-01 -2.1e-01 3974 14450 1.0
#> log_lik[4] -4.5e-01 2.4e-03 0.15 -0.72 -4.3e-01 -2.4e-01 3841 13967 1.00
#> log_lik[5] -1.2e+00 4.3e-03 0.28 -1.7 -1.2e+00 -7.6e-01 4262 15497 1.00
#> log_lik[6] -5.9e-01 2.8e-03 0.19 -0.94 -5.7e-01 -3.1e-01 4639 16869 1.00
#> log_lik[7] -6.4e-01 1.8e-03 0.13 -0.86 -6.3e-01 -4.5e-01 4605 16746 1.00
#> log_lik[8] -2.8e-01 2.3e-03 0.14 -0.54 -2.5e-01 -1.1e-01 3527 12826 1.0
#> log_lik[9] -6.9e-01 2.6e-03 0.17 -0.99 -6.8e-01 -4.3e-01 4522 16442 1.00
#> log_lik[10] -7.4e-01 3.7e-03 0.23 -1.2 -7.2e-01 -4.1e-01 3966 14423 1.0
#> log_lik[11] -2.8e-01 2.1e-03 0.13 -0.52 -2.6e-01 -1.1e-01 3682 13390 1.0
#> log_lik[12] -5.0e-01 3.9e-03 0.24 -0.93 -4.5e-01 -1.9e-01 3842 13973 1.00
#> log_lik[13] -6.6e-01 3.1e-03 0.21 -1.0 -6.4e-01 -3.6e-01 4585 16673 1.00
#> log_lik[14] -3.7e-01 2.8e-03 0.17 -0.70 -3.4e-01 -1.4e-01 3858 14029 1.0
#> log_lik[15] -2.8e-01 1.7e-03 0.11 -0.48 -2.7e-01 -1.4e-01 3977 14463 1.0
#> log_lik[16] -2.8e-01 1.5e-03 0.087 -0.44 -2.6e-01 -1.5e-01 3495 12708 1.0
#> log_lik[17] -1.6e+00 5.1e-03 0.29 -2.1 -1.6e+00 -1.1e+00 3184 11579 1.0
#> log_lik[18] -4.8e-01 1.7e-03 0.11 -0.68 -4.7e-01 -3.1e-01 4019 14615 1.0
#> log_lik[19] -2.3e-01 1.3e-03 0.075 -0.37 -2.2e-01 -1.3e-01 3432 12479 1.0
#> log_lik[20] -1.1e-01 1.3e-03 0.080 -0.27 -9.4e-02 -2.9e-02 3626 13186 1.0
#> log_lik[21] -2.1e-01 1.5e-03 0.088 -0.38 -2.0e-01 -9.4e-02 3506 12749 1.0
#> log_lik[22] -5.7e-01 2.3e-03 0.15 -0.84 -5.5e-01 -3.4e-01 4293 15611 1.0
#> log_lik[23] -3.3e-01 2.4e-03 0.14 -0.60 -3.0e-01 -1.4e-01 3628 13194 1.0
#> log_lik[24] -1.4e-01 1.1e-03 0.066 -0.26 -1.2e-01 -5.2e-02 3504 12742 1.0
#> log_lik[25] -4.5e-01 1.8e-03 0.12 -0.67 -4.4e-01 -2.8e-01 4234 15396 1.00
#> log_lik[26] -1.5e+00 5.5e-03 0.34 -2.1 -1.5e+00 -1.0e+00 3760 13672 1.0
#> log_lik[27] -3.1e-01 2.0e-03 0.12 -0.54 -2.9e-01 -1.4e-01 3688 13412 1.0
#> log_lik[28] -4.5e-01 1.3e-03 0.083 -0.59 -4.4e-01 -3.2e-01 4007 14572 1.0
#> log_lik[29] -7.2e-01 3.4e-03 0.22 -1.1 -7.0e-01 -4.0e-01 4367 15880 1.00
#> log_lik[30] -6.9e-01 3.1e-03 0.20 -1.0 -6.7e-01 -4.1e-01 4146 15077 1.0
#> log_lik[31] -4.9e-01 2.5e-03 0.17 -0.80 -4.6e-01 -2.5e-01 4260 15491 1.0
#> log_lik[32] -4.2e-01 1.6e-03 0.11 -0.61 -4.2e-01 -2.7e-01 4253 15465 1.00
#> log_lik[33] -4.1e-01 2.0e-03 0.13 -0.65 -4.0e-01 -2.3e-01 4207 15297 1.00
#> log_lik[34] -6.3e-02 8.6e-04 0.051 -0.16 -4.9e-02 -1.3e-02 3530 12835 1.00
#> log_lik[35] -5.9e-01 2.6e-03 0.18 -0.91 -5.7e-01 -3.2e-01 4846 17624 1.00
#> log_lik[36] -3.2e-01 2.2e-03 0.13 -0.57 -3.0e-01 -1.5e-01 3788 13775 1.0
#> log_lik[37] -7.0e-01 3.4e-03 0.22 -1.1 -6.8e-01 -3.8e-01 4249 15451 1.0
#> log_lik[38] -3.1e-01 2.4e-03 0.15 -0.60 -2.8e-01 -1.2e-01 3822 13898 1.00
#> log_lik[39] -1.8e-01 1.8e-03 0.11 -0.40 -1.6e-01 -5.3e-02 3734 13579 1.0
#> log_lik[40] -6.8e-01 2.0e-03 0.13 -0.91 -6.7e-01 -4.8e-01 4407 16026 1.00
#> log_lik[41] -1.1e+00 4.2e-03 0.26 -1.6 -1.1e+00 -7.4e-01 3931 14294 1.0
#> log_lik[42] -9.3e-01 3.1e-03 0.20 -1.3 -9.1e-01 -6.2e-01 4217 15335 1.00
#> log_lik[43] -4.1e-01 4.0e-03 0.27 -0.92 -3.5e-01 -1.0e-01 4495 16345 1.00
#> log_lik[44] -1.2e+00 2.9e-03 0.18 -1.5 -1.2e+00 -8.9e-01 3801 13820 1.0
#> log_lik[45] -3.6e-01 1.9e-03 0.12 -0.58 -3.5e-01 -1.9e-01 3946 14348 1.0
#> log_lik[46] -5.8e-01 2.0e-03 0.13 -0.81 -5.7e-01 -3.8e-01 4290 15599 1.00
#> log_lik[47] -3.0e-01 2.1e-03 0.13 -0.55 -2.8e-01 -1.3e-01 3827 13916 1.00
#> log_lik[48] -3.2e-01 1.3e-03 0.082 -0.47 -3.2e-01 -2.0e-01 3733 13576 1.0
#> log_lik[49] -3.2e-01 1.3e-03 0.079 -0.46 -3.1e-01 -2.0e-01 3527 12825 1.0
#> log_lik[50] -1.3e+00 5.1e-03 0.33 -1.9 -1.3e+00 -8.0e-01 4204 15288 1.00
#> log_lik[51] -2.9e-01 1.5e-03 0.094 -0.46 -2.8e-01 -1.6e-01 4007 14571 1.00
#> log_lik[52] -8.3e-01 2.2e-03 0.14 -1.1 -8.2e-01 -6.2e-01 4159 15122 1.00
#> log_lik[53] -4.0e-01 2.1e-03 0.13 -0.65 -3.8e-01 -2.1e-01 4104 14922 1.0
#> log_lik[54] -3.7e-01 2.2e-03 0.14 -0.63 -3.6e-01 -1.8e-01 4025 14635 1.0
#> log_lik[55] -3.9e-01 2.2e-03 0.14 -0.64 -3.7e-01 -2.0e-01 4074 14813 1.00
#> log_lik[56] -3.2e-01 3.2e-03 0.19 -0.70 -2.7e-01 -9.4e-02 3722 13536 1.0
#> log_lik[57] -6.6e-01 1.7e-03 0.12 -0.86 -6.5e-01 -4.8e-01 4340 15782 1.00
#> log_lik[58] -9.5e-01 5.2e-03 0.35 -1.6 -9.1e-01 -4.6e-01 4377 15915 1.00
#> log_lik[59] -1.4e+00 5.6e-03 0.35 -2.0 -1.3e+00 -8.4e-01 3916 14239 1.00
#> log_lik[60] -9.8e-01 2.4e-03 0.16 -1.2 -9.7e-01 -7.3e-01 4048 14720 1.00
#> log_lik[61] -5.4e-01 1.5e-03 0.097 -0.71 -5.3e-01 -3.9e-01 4267 15517 1.00
#> log_lik[62] -8.7e-01 4.9e-03 0.32 -1.5 -8.3e-01 -4.3e-01 4186 15223 1.00
#> log_lik[63] -1.2e-01 1.3e-03 0.074 -0.26 -9.7e-02 -3.2e-02 3420 12438 1.0
#> log_lik[64] -9.1e-01 4.0e-03 0.26 -1.4 -8.8e-01 -5.3e-01 4216 15333 1.0
#> log_lik[65] -2.0e+00 1.0e-02 0.60 -3.1 -2.0e+00 -1.1e+00 3486 12678 1.00
#> log_lik[66] -5.1e-01 2.0e-03 0.13 -0.75 -5.0e-01 -3.1e-01 4559 16578 1.00
#> log_lik[67] -2.8e-01 1.4e-03 0.081 -0.42 -2.7e-01 -1.6e-01 3503 12740 1.0
#> log_lik[68] -1.1e+00 3.9e-03 0.24 -1.5 -1.1e+00 -7.0e-01 3902 14189 1.0
#> log_lik[69] -4.4e-01 1.3e-03 0.082 -0.58 -4.3e-01 -3.1e-01 4099 14906 1.0
#> log_lik[70] -6.4e-01 3.8e-03 0.24 -1.1 -6.0e-01 -3.0e-01 4023 14629 1.0
#> log_lik[71] -6.1e-01 3.0e-03 0.21 -0.99 -5.9e-01 -3.2e-01 4800 17455 1.00
#> log_lik[72] -4.7e-01 2.7e-03 0.18 -0.80 -4.4e-01 -2.2e-01 4327 15734 1.00
#> log_lik[73] -1.5e+00 6.0e-03 0.37 -2.1 -1.5e+00 -9.4e-01 3679 13379 1.00
#> log_lik[74] -9.5e-01 3.0e-03 0.19 -1.3 -9.4e-01 -6.6e-01 4126 15004 1.0
#> log_lik[75] -1.1e+00 6.0e-03 0.39 -1.8 -1.1e+00 -5.8e-01 4255 15474 1.00
#> log_lik[76] -3.8e-01 2.2e-03 0.14 -0.63 -3.6e-01 -1.8e-01 3886 14131 1.0
#> log_lik[77] -8.8e-01 2.1e-03 0.14 -1.1 -8.7e-01 -6.6e-01 4420 16075 1.00
#> log_lik[78] -4.9e-01 2.6e-03 0.17 -0.80 -4.7e-01 -2.5e-01 4490 16326 1.00
#> log_lik[79] -7.7e-01 2.9e-03 0.19 -1.1 -7.5e-01 -4.9e-01 4123 14992 1.00
#> log_lik[80] -5.4e-01 3.0e-03 0.20 -0.92 -5.2e-01 -2.6e-01 4287 15591 1.00
#> log_lik[81] -1.6e-01 1.6e-03 0.099 -0.36 -1.4e-01 -4.9e-02 3918 14248 1.00
#> log_lik[82] -2.3e-01 2.3e-03 0.14 -0.51 -1.9e-01 -6.4e-02 3867 14061 1.00
#> log_lik[83] -3.4e-01 1.3e-03 0.080 -0.49 -3.4e-01 -2.3e-01 3809 13853 1.0
#> log_lik[84] -2.8e-01 1.5e-03 0.090 -0.45 -2.7e-01 -1.5e-01 3815 13872 1.0
#> log_lik[85] -1.3e-01 1.3e-03 0.075 -0.28 -1.2e-01 -4.4e-02 3631 13202 1.0
#> log_lik[86] -1.1e+00 5.2e-03 0.33 -1.7 -1.1e+00 -6.4e-01 4016 14603 1.00
#> log_lik[87] -8.2e-01 1.9e-03 0.13 -1.0 -8.2e-01 -6.3e-01 4341 15786 1.00
#> log_lik[88] -7.8e-01 3.7e-03 0.25 -1.2 -7.5e-01 -4.3e-01 4546 16530 1.00
#> log_lik[89] -1.3e+00 5.2e-03 0.32 -1.8 -1.3e+00 -8.0e-01 3869 14070 1.00
#> log_lik[90] -2.6e-01 2.1e-03 0.13 -0.51 -2.3e-01 -9.4e-02 4086 14857 1.00
#> log_lik[91] -3.9e-01 2.0e-03 0.13 -0.63 -3.8e-01 -2.0e-01 4332 15753 1.00
#> log_lik[92] -1.5e+00 5.5e-03 0.34 -2.1 -1.5e+00 -9.7e-01 3795 13801 1.0
#> log_lik[93] -7.3e-01 3.5e-03 0.22 -1.1 -7.1e-01 -4.2e-01 3875 14091 1.00
#> log_lik[94] -3.2e-01 1.4e-03 0.086 -0.48 -3.1e-01 -1.9e-01 3797 13806 1.0
#> log_lik[95] -3.9e-01 1.9e-03 0.11 -0.60 -3.7e-01 -2.2e-01 3692 13426 1.0
#> log_lik[96] -1.6e+00 4.9e-03 0.28 -2.1 -1.6e+00 -1.1e+00 3247 11808 1.0
#> log_lik[97] -4.3e-01 1.6e-03 0.10 -0.61 -4.2e-01 -2.8e-01 4122 14989 1.0
#> log_lik[98] -1.1e+00 5.5e-03 0.37 -1.7 -1.0e+00 -5.3e-01 4388 15957 1.00
#> log_lik[99] -7.0e-01 2.2e-03 0.15 -0.95 -6.9e-01 -4.8e-01 4345 15799 1.00
#> log_lik[100] -3.9e-01 1.5e-03 0.096 -0.56 -3.8e-01 -2.5e-01 4119 14977 1.0
#>
#> Samples were drawn using hmc with nuts.
#> For each parameter, N_Eff is a crude measure of effective sample size,
#> and R_hat is the potential scale reduction factor on split chains (at
#> convergence, R_hat=1).
# }