fix n term in sbm mcse method

arviz/stats/diagnostics.py

@@ -417,6 +417,10 @@ def mcse(data, *, var_names=None, method="mean", prob=None, func=None, dask_kwar
     if isinstance(data, np.ndarray):
         data = np.atleast_2d(data)
         if len(data.shape) < 3:
+            if data.size < 1000 and method == "func":
+                warnings.warn(
+                    "Not enough samples for reliable estimate of MCSE for arbitrary functions"
+                )
             if prob is not None:
                 return mcse_func(data, prob=prob)  # pylint: disable=unexpected-keyword-arg
             else:
@@ -429,6 +433,10 @@ def mcse(data, *, var_names=None, method="mean", prob=None, func=None, dask_kwar
             raise TypeError(msg)
 
     dataset = convert_to_dataset(data, group="posterior")
+    if (dataset.dims["chain"] * dataset.dims["draw"]) < 1000 and method == "func":
+        warnings.warn(
+            "Not enough samples for reliable estimate of MCSE for arbitrary functions"
+        )
     var_names = _var_names(var_names, dataset)
 
     dataset = dataset if var_names is None else dataset[var_names]
@@ -833,11 +841,11 @@ def _mcse_mean(ary):
 def _mcse_func_sbm(ary, func):
     """Compute the Markov Chain error on an arbitrary function."""
     ary = np.asarray(ary)
-    if _not_valid(ary, shape_kwargs=dict(min_draws=4, min_chains=1)):
+    if _not_valid(ary, shape_kwargs=dict(min_draws=10, min_chains=1)):
         return np.nan
-    ess = _ess_mean(ary)
+    n = ary.size
     func_estimate_sd = _sbm(ary, func)
-    mcse_func_value = func_estimate_sd / np.sqrt(ess)
+    mcse_func_value = func_estimate_sd / np.sqrt(n)
     return mcse_func_value
 
 def _sbm(ary, func):