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/RtmpAMFHpW/logistic-202312131001-1-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-2-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-3-2718e9.csv, /var/folders/s0/zfzm55px2nd2v__zlw5xfj2h0000gn/T/RtmpAMFHpW/logistic-202312131001-4-2718e9.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, 0.023, 0.023, 0.023) seconds, 0.091 seconds total
#> Sampling took (0.071, 0.070, 0.070, 0.070) seconds, 0.28 seconds total
#>
#> Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
#>
#> lp__ -6.6e+01 3.1e-02 1.4 -69 -6.6e+01 -6.4e+01 2120 7545 1.0
#> accept_stat__ 0.91 1.4e-03 0.10 0.69 0.95 1.0 5437 19350 1.0e+00
#> stepsize__ 0.74 2.1e-02 0.030 0.70 0.75 0.78 2.0 7.1 1.1e+13
#> treedepth__ 2.4 8.8e-03 0.53 2.0 2.0 3.0 3601 12815 1.0e+00
#> n_leapfrog__ 5.3 3.3e-02 2.0 3.0 7.0 7.0 3773 13428 1.0e+00
#> divergent__ 0.00 nan 0.00 0.00 0.00 0.00 nan nan nan
#> energy__ 68 4.9e-02 2.0 65 68 72 1750 6227 1.0e+00
#>
#> alpha 3.8e-01 3.5e-03 0.22 0.026 3.8e-01 7.5e-01 3978 14156 1.00
#> beta[1] -6.7e-01 4.1e-03 0.26 -1.1 -6.6e-01 -2.6e-01 3779 13447 1.0
#> beta[2] -2.7e-01 3.5e-03 0.23 -0.64 -2.7e-01 1.0e-01 4061 14450 1.0
#> beta[3] 6.7e-01 4.3e-03 0.27 0.24 6.7e-01 1.1e+00 3879 13804 1.00
#> log_lik[1] -5.1e-01 1.5e-03 0.097 -0.68 -5.1e-01 -3.7e-01 3951 14060 1.00
#> log_lik[2] -4.1e-01 2.3e-03 0.15 -0.69 -3.9e-01 -1.9e-01 4167 14830 1.00
#> log_lik[3] -4.9e-01 3.3e-03 0.21 -0.88 -4.6e-01 -2.1e-01 4010 14272 1.00
#> log_lik[4] -4.5e-01 2.5e-03 0.15 -0.72 -4.4e-01 -2.4e-01 3751 13350 1.0
#> log_lik[5] -1.2e+00 4.4e-03 0.28 -1.7 -1.2e+00 -7.6e-01 4068 14476 1.0
#> log_lik[6] -5.9e-01 3.0e-03 0.19 -0.96 -5.7e-01 -3.2e-01 4275 15213 1.0
#> log_lik[7] -6.4e-01 2.0e-03 0.13 -0.86 -6.4e-01 -4.5e-01 4183 14885 1.0
#> log_lik[8] -2.8e-01 2.3e-03 0.14 -0.55 -2.5e-01 -1.1e-01 3659 13021 1.0
#> log_lik[9] -6.9e-01 2.7e-03 0.17 -1.0 -6.7e-01 -4.4e-01 4128 14690 1.0
#> log_lik[10] -7.3e-01 3.6e-03 0.22 -1.1 -7.1e-01 -4.1e-01 3894 13857 1.00
#> log_lik[11] -2.8e-01 2.1e-03 0.13 -0.54 -2.6e-01 -1.1e-01 3822 13601 1.00
#> log_lik[12] -5.0e-01 3.8e-03 0.24 -0.95 -4.6e-01 -1.9e-01 3952 14065 1.00
#> log_lik[13] -6.6e-01 3.3e-03 0.21 -1.0 -6.4e-01 -3.5e-01 4181 14878 1.0
#> log_lik[14] -3.6e-01 2.6e-03 0.17 -0.67 -3.2e-01 -1.4e-01 4021 14308 1.00
#> log_lik[15] -2.8e-01 1.7e-03 0.10 -0.47 -2.6e-01 -1.3e-01 3858 13730 1.00
#> log_lik[16] -2.8e-01 1.5e-03 0.091 -0.44 -2.7e-01 -1.5e-01 3706 13189 1.00
#> log_lik[17] -1.6e+00 5.0e-03 0.30 -2.1 -1.6e+00 -1.1e+00 3612 12854 1.0
#> log_lik[18] -4.8e-01 1.8e-03 0.11 -0.68 -4.7e-01 -3.1e-01 4081 14523 1.0
#> log_lik[19] -2.3e-01 1.3e-03 0.079 -0.37 -2.2e-01 -1.2e-01 3751 13347 1.0
#> log_lik[20] -1.1e-01 1.3e-03 0.078 -0.26 -9.4e-02 -2.8e-02 3798 13516 1.00
#> log_lik[21] -2.1e-01 1.5e-03 0.091 -0.39 -2.0e-01 -9.3e-02 3626 12906 1.00
#> log_lik[22] -5.7e-01 2.4e-03 0.15 -0.86 -5.5e-01 -3.5e-01 4061 14452 1.0
#> log_lik[23] -3.3e-01 2.3e-03 0.15 -0.61 -3.1e-01 -1.4e-01 3872 13780 1.0
#> log_lik[24] -1.4e-01 1.1e-03 0.069 -0.27 -1.2e-01 -5.1e-02 3743 13321 1.00
#> log_lik[25] -4.6e-01 1.9e-03 0.12 -0.68 -4.5e-01 -2.8e-01 3975 14145 1.0
#> log_lik[26] -1.5e+00 5.8e-03 0.35 -2.1 -1.5e+00 -1.0e+00 3699 13164 1.0
#> log_lik[27] -3.1e-01 2.0e-03 0.13 -0.55 -2.9e-01 -1.4e-01 3831 13633 1.00
#> log_lik[28] -4.4e-01 1.4e-03 0.086 -0.59 -4.4e-01 -3.1e-01 3970 14130 1.0
#> log_lik[29] -7.3e-01 3.4e-03 0.22 -1.1 -7.1e-01 -4.0e-01 4258 15153 1.0
#> log_lik[30] -6.9e-01 3.1e-03 0.20 -1.1 -6.8e-01 -4.0e-01 4121 14666 1.00
#> log_lik[31] -4.9e-01 2.6e-03 0.17 -0.81 -4.7e-01 -2.5e-01 4046 14398 1.0
#> log_lik[32] -4.2e-01 1.7e-03 0.11 -0.62 -4.1e-01 -2.6e-01 3986 14185 1.0
#> log_lik[33] -4.1e-01 2.0e-03 0.13 -0.65 -4.0e-01 -2.2e-01 4122 14669 1.0
#> log_lik[34] -6.5e-02 9.1e-04 0.054 -0.17 -5.0e-02 -1.3e-02 3544 12612 1.0
#> log_lik[35] -5.9e-01 2.8e-03 0.18 -0.92 -5.7e-01 -3.2e-01 4411 15696 1.0
#> log_lik[36] -3.3e-01 2.2e-03 0.14 -0.58 -3.0e-01 -1.4e-01 4001 14239 1.0
#> log_lik[37] -6.9e-01 3.4e-03 0.22 -1.1 -6.7e-01 -3.7e-01 4045 14394 1.0
#> log_lik[38] -3.2e-01 2.4e-03 0.15 -0.60 -2.9e-01 -1.2e-01 3854 13717 1.0
#> log_lik[39] -1.8e-01 1.7e-03 0.11 -0.38 -1.5e-01 -5.2e-02 3915 13934 1.00
#> log_lik[40] -6.8e-01 2.1e-03 0.13 -0.92 -6.7e-01 -4.8e-01 4145 14752 1.0
#> log_lik[41] -1.1e+00 4.2e-03 0.26 -1.6 -1.1e+00 -7.3e-01 3929 13983 1.0
#> log_lik[42] -9.3e-01 3.2e-03 0.20 -1.3 -9.1e-01 -6.3e-01 3967 14116 1.0
#> log_lik[43] -4.1e-01 4.0e-03 0.26 -0.91 -3.4e-01 -1.0e-01 4191 14913 1.0
#> log_lik[44] -1.2e+00 3.1e-03 0.19 -1.5 -1.2e+00 -9.0e-01 3709 13201 1.00
#> log_lik[45] -3.6e-01 1.8e-03 0.11 -0.56 -3.4e-01 -1.9e-01 4009 14267 1.00
#> log_lik[46] -5.9e-01 2.0e-03 0.13 -0.82 -5.8e-01 -3.8e-01 4248 15119 1.00
#> log_lik[47] -3.1e-01 2.2e-03 0.13 -0.55 -2.9e-01 -1.3e-01 3696 13153 1.0
#> log_lik[48] -3.2e-01 1.4e-03 0.085 -0.47 -3.2e-01 -2.0e-01 3847 13691 1.0
#> log_lik[49] -3.2e-01 1.3e-03 0.082 -0.46 -3.1e-01 -2.0e-01 3716 13223 1.00
#> log_lik[50] -1.3e+00 5.4e-03 0.33 -1.9 -1.3e+00 -8.1e-01 3859 13734 1.0
#> log_lik[51] -2.9e-01 1.6e-03 0.096 -0.46 -2.8e-01 -1.5e-01 3714 13218 1.0
#> log_lik[52] -8.4e-01 2.2e-03 0.14 -1.1 -8.3e-01 -6.2e-01 3985 14182 1.00
#> log_lik[53] -4.0e-01 2.1e-03 0.13 -0.66 -3.9e-01 -2.1e-01 3970 14127 1.00
#> log_lik[54] -3.7e-01 2.2e-03 0.14 -0.62 -3.5e-01 -1.8e-01 4008 14263 1.00
#> log_lik[55] -3.9e-01 2.2e-03 0.14 -0.65 -3.7e-01 -1.9e-01 4107 14614 1.00
#> log_lik[56] -3.2e-01 3.2e-03 0.20 -0.70 -2.8e-01 -9.3e-02 3745 13326 1.0
#> log_lik[57] -6.5e-01 1.8e-03 0.12 -0.85 -6.5e-01 -4.8e-01 3918 13943 1.0
#> log_lik[58] -9.4e-01 5.3e-03 0.35 -1.6 -9.0e-01 -4.5e-01 4317 15364 1.0
#> log_lik[59] -1.4e+00 5.7e-03 0.35 -2.0 -1.3e+00 -8.4e-01 3801 13527 1.0
#> log_lik[60] -9.8e-01 2.5e-03 0.16 -1.3 -9.7e-01 -7.4e-01 3902 13886 1.00
#> log_lik[61] -5.4e-01 1.5e-03 0.097 -0.70 -5.3e-01 -3.9e-01 4078 14513 1.00
#> log_lik[62] -8.9e-01 5.0e-03 0.31 -1.5 -8.5e-01 -4.4e-01 3852 13708 1.00
#> log_lik[63] -1.2e-01 1.3e-03 0.077 -0.27 -9.9e-02 -3.2e-02 3330 11849 1.00
#> log_lik[64] -9.0e-01 3.8e-03 0.25 -1.4 -8.8e-01 -5.3e-01 4445 15817 1.00
#> log_lik[65] -2.0e+00 9.8e-03 0.61 -3.1 -2.0e+00 -1.1e+00 3783 13462 1.00
#> log_lik[66] -5.1e-01 2.1e-03 0.13 -0.74 -4.9e-01 -3.1e-01 4154 14782 1.0
#> log_lik[67] -2.8e-01 1.4e-03 0.085 -0.43 -2.7e-01 -1.6e-01 3827 13618 1.0
#> log_lik[68] -1.1e+00 3.8e-03 0.24 -1.5 -1.0e+00 -6.8e-01 4102 14597 1.00
#> log_lik[69] -4.3e-01 1.4e-03 0.085 -0.58 -4.3e-01 -3.0e-01 3842 13673 1.0
#> log_lik[70] -6.4e-01 3.6e-03 0.24 -1.1 -6.1e-01 -3.1e-01 4371 15555 1.00
#> log_lik[71] -6.1e-01 3.2e-03 0.21 -0.99 -5.9e-01 -3.1e-01 4401 15663 1.0
#> log_lik[72] -4.6e-01 2.7e-03 0.17 -0.79 -4.5e-01 -2.2e-01 4214 14996 1.0
#> log_lik[73] -1.5e+00 6.1e-03 0.37 -2.1 -1.4e+00 -9.2e-01 3671 13065 1.0
#> log_lik[74] -9.5e-01 3.0e-03 0.19 -1.3 -9.4e-01 -6.7e-01 3944 14035 1.00
#> log_lik[75] -1.2e+00 6.4e-03 0.39 -1.8 -1.1e+00 -5.9e-01 3678 13088 1.00
#> log_lik[76] -3.7e-01 2.1e-03 0.13 -0.62 -3.5e-01 -1.8e-01 3976 14151 1.00
#> log_lik[77] -8.8e-01 2.2e-03 0.14 -1.1 -8.8e-01 -6.6e-01 4034 14357 1.0
#> log_lik[78] -4.8e-01 2.7e-03 0.17 -0.79 -4.6e-01 -2.4e-01 4097 14581 1.00
#> log_lik[79] -7.6e-01 2.9e-03 0.18 -1.1 -7.4e-01 -4.8e-01 3889 13840 1.0
#> log_lik[80] -5.5e-01 3.0e-03 0.20 -0.91 -5.2e-01 -2.7e-01 4407 15683 1.00
#> log_lik[81] -1.7e-01 1.7e-03 0.10 -0.37 -1.4e-01 -4.8e-02 3780 13450 1.0
#> log_lik[82] -2.3e-01 2.3e-03 0.14 -0.51 -2.0e-01 -6.3e-02 3943 14031 1.00
#> log_lik[83] -3.4e-01 1.3e-03 0.081 -0.48 -3.4e-01 -2.2e-01 3766 13404 1.00
#> log_lik[84] -2.7e-01 1.5e-03 0.090 -0.44 -2.6e-01 -1.4e-01 3767 13405 1.00
#> log_lik[85] -1.3e-01 1.2e-03 0.075 -0.28 -1.1e-01 -4.1e-02 3780 13453 1.00
#> log_lik[86] -1.1e+00 5.0e-03 0.32 -1.7 -1.1e+00 -6.4e-01 4173 14850 1.00
#> log_lik[87] -8.3e-01 2.0e-03 0.13 -1.1 -8.2e-01 -6.2e-01 4183 14887 1.00
#> log_lik[88] -7.8e-01 3.8e-03 0.24 -1.2 -7.5e-01 -4.2e-01 4157 14794 1.0
#> log_lik[89] -1.3e+00 5.1e-03 0.32 -1.9 -1.2e+00 -7.9e-01 3879 13805 1.00
#> log_lik[90] -2.6e-01 2.2e-03 0.14 -0.52 -2.4e-01 -9.4e-02 4008 14262 1.0
#> log_lik[91] -3.9e-01 2.1e-03 0.13 -0.63 -3.8e-01 -2.1e-01 4144 14746 1.0
#> log_lik[92] -1.5e+00 5.6e-03 0.34 -2.1 -1.5e+00 -9.7e-01 3829 13625 1.00
#> log_lik[93] -7.5e-01 3.4e-03 0.21 -1.1 -7.2e-01 -4.4e-01 3888 13835 1.00
#> log_lik[94] -3.2e-01 1.4e-03 0.087 -0.47 -3.1e-01 -1.9e-01 3803 13533 1.00
#> log_lik[95] -3.9e-01 1.9e-03 0.12 -0.60 -3.8e-01 -2.2e-01 3889 13841 1.0
#> log_lik[96] -1.6e+00 4.8e-03 0.29 -2.1 -1.6e+00 -1.1e+00 3601 12815 1.00
#> log_lik[97] -4.3e-01 1.6e-03 0.10 -0.61 -4.2e-01 -2.7e-01 4067 14472 1.0
#> log_lik[98] -1.0e+00 5.6e-03 0.37 -1.7 -1.0e+00 -5.2e-01 4342 15452 1.0
#> log_lik[99] -7.0e-01 2.2e-03 0.15 -0.96 -6.9e-01 -4.7e-01 4351 15484 1.00
#> log_lik[100] -3.9e-01 1.5e-03 0.097 -0.55 -3.8e-01 -2.4e-01 4019 14301 1.00
#>
#> 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).
# }