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stansummary: do not report MCSE, ESS, and R-hat for the sampler diagnostics #1363

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stansummary: do not report MCSE, ESS, and R-hat for the sampler diagnostics #1363

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e1c15de
Do not compute MCSE, ESS, and R-hat for sampler diagnostics in stansu…
avehtari Oct 24, 2026
0264af8
Update stansummary_test MCSE values for split-chain MCSE in Stan
avehtari Oct 24, 2026
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3 changes: 2 additions & 1 deletion src/cmdstan/stansummary.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -201,8 +201,9 @@ Example: stansummary model_chain_1.csv model_chain_2.csv
Eigen::MatrixXd model_params(num_model_params, header.size());

get_stats(chains, sampling_times, probs, {0}, lp_param);
// MCSE, ESS, and R_hat are not meaningful for the sampler diagnostics
get_stats(chains, sampling_times, probs, sampler_params_idxes,
sampler_params);
sampler_params, false);
get_stats(chains, sampling_times, probs, model_param_idxes, model_params);

// Console output formatting
Expand Down
13 changes: 11 additions & 2 deletions src/cmdstan/stansummary_helper.hpp
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Expand Up @@ -8,6 +8,7 @@
#include <ios>
#include <cmath>
#include <iostream>
#include <limits>
#include <stdexcept>
#include <string>
#include <vector>
Expand Down Expand Up @@ -393,31 +394,39 @@ std::vector<std::string> get_header(
* @param in vector of model param column incides in chains object
* @param in span length
* @param in out matrix of model param statistics
* @param in convergence_diagnostics if false, MCSE, ESS, and R_hat are NaN
*/
void get_stats(const stan::mcmc::chainset &chains,
const Eigen::VectorXd &sampling_times,
const Eigen::VectorXd &probs, std::vector<int> cols,
Eigen::MatrixXd &params) {
Eigen::MatrixXd &params, bool convergence_diagnostics = true) {
params.setZero();

if (params.rows() != cols.size()) {
throw std::domain_error("get_stats: size mismatch");
}

double total_sampling_time = sampling_times.sum();
constexpr double nan = std::numeric_limits<double>::quiet_NaN();

// Model parameters
int i = 0;
for (int i_chains : cols) {
params(i, 0) = chains.mean(i_chains);
params(i, 1) = chains.mcse_mean(i_chains);
params(i, 1) = convergence_diagnostics ? chains.mcse_mean(i_chains) : nan;
params(i, 2) = chains.sd(i_chains);
params(i, 3) = chains.med_abs_deviation(i_chains);
Eigen::VectorXd quantiles = chains.quantiles(i_chains, probs);
for (int j = 0; j < quantiles.size(); j++)
params(i, 4 + j) = quantiles(j);

auto offset = 4 + quantiles.size();
if (!convergence_diagnostics) {
for (int j = 0; j < 4; ++j)
params(i, offset++) = nan;
i++;
continue;
}
auto [ess_bulk, ess_tail] = chains.split_rank_normalized_ess(i_chains);

params(i, offset++) = ess_bulk;
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30 changes: 15 additions & 15 deletions src/test/interface/stansummary_test.cpp
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Expand Up @@ -433,11 +433,11 @@ TEST(CommandStansummary, check_console_output) {
= "theta 0.26 6.1e-03 0.12 0.12 0.080 0.25 0.47 "
" 362 396 15718 1.0";
std::string accept_stat
= "accept_stat__ 0.90 4.6e-03 1.5e-01 0.064 0.57 0.96 1.0 "
"1284 941 55805 1.00";
= "accept_stat__ 0.90 nan 1.5e-01 0.064 0.57 0.96 1.0 "
" nan nan nan nan";
std::string energy
= "energy__ 7.8 5.1e-02 1.0e+00 0.75 6.8 7.5 9.9 "
" 490 486 21299 1.0";
= "energy__ 7.8 nan 1.0e+00 0.75 6.8 7.5 9.9 "
" nan nan nan nan";

std::string path_separator;
path_separator.push_back(get_path_separator());
Expand Down Expand Up @@ -485,13 +485,13 @@ TEST(CommandStansummary, check_csv_output) {
= "name,Mean,MCSE,StdDev,MAD,5%,50%,95%,ESS_bulk,ESS_tail,ESS_bulk/"
"s,R_hat";
std::string lp
= "\"lp__\",-7.2719,0.0365168,0.768874,0.303688,-8.98426,-6.97009,-6."
= "\"lp__\",-7.2719,0.0365073,0.768874,0.303688,-8.98426,-6.97009,-6."
"75007,519.29,503.309,22577.8,1.00141";
std::string energy
= "\"energy__\",7.78428,0.0508815,1.0314,0.745859,6.80565,7.46758,9.8864,"
"489.874,486.438,21298.9,1.00495";
= "\"energy__\",7.78428,nan,1.0314,0.745859,6.80565,7.46758,9.8864,nan,"
"nan,nan,nan";
std::string theta
= "\"theta\",0.256552,0.00610844,0.119654,0.120965,0.0802982,0.24996,0."
= "\"theta\",0.256552,0.00609368,0.119654,0.120965,0.0802982,0.24996,0."
"47034,361.506,395.736,15717.6,1.00186";

std::string path_separator;
Expand Down Expand Up @@ -538,7 +538,7 @@ TEST(CommandStansummary, check_csv_output_no_percentiles) {
std::string csv_header
= "name,Mean,MCSE,StdDev,MAD,ESS_bulk,ESS_tail,ESS_bulk/s,R_hat";
std::string lp
= "\"lp__\",-7.2719,0.0365168,0.768874,0.303688,519.29,503.309,22577.8,1."
= "\"lp__\",-7.2719,0.0365073,0.768874,0.303688,519.29,503.309,22577.8,1."
"00141";

std::string path_separator;
Expand Down Expand Up @@ -581,7 +581,7 @@ TEST(CommandStansummary, check_csv_output_sig_figs) {
std::string lp
= "\"lp__\",-7.3,0.037,0.77,0.3,-9,-7,-6.8,5.2e+02,5e+02,2.3e+04,1";
std::string energy
= "\"energy__\",7.8,0.051,1,0.75,6.8,7.5,9.9,4.9e+02,4.9e+02,2.1e+04,1";
= "\"energy__\",7.8,nan,1,0.75,6.8,7.5,9.9,nan,nan,nan,nan";
std::string theta
= "\"theta\",0.26,0.0061,0.12,0.12,0.08,0.25,0.47,3.6e+02,4e+02,1.6e+04,"
"1";
Expand Down Expand Up @@ -632,17 +632,17 @@ TEST(CommandStansummary, check_csv_output_include_param) {
= "name,Mean,MCSE,StdDev,MAD,5%,50%,95%,ESS_bulk,ESS_tail,ESS_bulk/"
"s,R_hat";
std::string lp
= "\"lp__\",-15.5617,0.97319,6.05585,6.3817,-25.3182,-15.7598,-5.47732,"
= "\"lp__\",-15.5617,0.952577,6.05585,6.3817,-25.3182,-15.7598,-5.47732,"
"41.1897,113.537,396.283,1.00153";
std::string energy
= "\"energy__\",20.5888,1.01449,6.43127,6.6161,10.2809,20.8278,30.9921,"
"42.5605,140.171,409.472,1.00069";
= "\"energy__\",20.5888,nan,6.43127,6.6161,10.2809,20.8278,30.9921,nan,"
"nan,nan,nan";
// note: skipping theta 1-5
std::string theta6
= "\"theta[6]\",5.001,0.365016,5.76072,5.37947,-4.95375,5.22746,14.1688,"
= "\"theta[6]\",5.001,0.368663,5.76072,5.37947,-4.95375,5.22746,14.1688,"
"230.645,464.978,2219.02,1.00054";
std::string theta7
= "\"theta[7]\",8.54125,0.650098,6.22195,5.35785,-0.814388,8.09342,19."
= "\"theta[7]\",8.54125,0.641448,6.22195,5.35785,-0.814388,8.09342,19."
"2622,92.3075,241.177,888.084,1.00244";
// note: skipping theta 8
std::string message = "# Inference for Stan model: eight_schools_cp_model";
Expand Down