Reading qcaERT result objects

qcaERT functions are designed as siblings. The details differ by test, but the main returned-object structure is intentionally shared.

The code fragments in this vignette use object names such as incl_out, calib_out, and cluster_out. The getting-started vignette shows how to create those objects in a normal QCA workflow.

print(x) gives a concise summary. as.data.frame(x) returns the clean table. x$diagnostics is where to look when you need to understand a specific run, case, draw, or stopping reason.

Boundary tests

incl.test(), ncut.test(), and calib.test() are boundary tests. They move a quantity away from the baseline until either the monitored solution changes, an error occurs, or the search reaches a limit.

as.data.frame(incl_out)
incl_out$diagnostics
incl_out$bounds

as.data.frame(ncut_out)
ncut_out$diagnostics
ncut_out$bounds

as.data.frame(calib_out)
calib_out$diagnostics
calib_out$bounds

The clean table answers the practical question: how far did the value move before the result stopped being stable?

The diagnostics table preserves the path-level detail.

Case and sample tests

loo.test() gives one row per removed case. subsample.test() gives one row per subsample run.

as.data.frame(loo_out)
loo_out$diagnostics
loo_out$by_case

as.data.frame(subsample_out)
subsample_out$diagnostics
subsample_out$by_run
subsample_out$summary

Use the clean table to find cases or runs that changed the solution. Use the supporting list components when you need the full run object for a specific case or replication.

Alternative-set tests

altset.test() gives one row per random draw. The result object also stores the draw-level run details and aggregate scores.

as.data.frame(altset_out)
altset_out$diagnostics
altset_out$by_draw
altset_out$summary

The clean table is for scanning. In it, n_fit_deltas = 0 means that fit was compared and no difference exceeded fit_tol; n_fit_deltas = NA means that no like-for-like fit comparison was available. The printed summary separates attempted draws, formula-comparable draws, and fit-comparable draws so each preservation rate retains its denominator. When calibration is part of the sampled draw, changed_sets names the perturbed calibrated sets and changed_roles records whether those sets were conditions, the outcome, or both.

Cluster tests

cluster.test() and theory.test() are the deliberate structured-result exceptions. For cluster.test(), results is a list:

overview
clusters
units

cluster_out$results$overview
cluster_out$results$clusters
cluster_out$results$units

as.data.frame(cluster_out)
cluster_out$diagnostics

For cluster.test(), as.data.frame(cluster_out) returns cluster_out$results$overview.

Theory tests

theory.test() is also structured because comparative theory testing has three natural clean tables:

models
solutions
pairwise

as.data.frame(theory_out)
theory_out$results$models
theory_out$results$solutions
theory_out$results$pairwise
theory_out$diagnostics
theory_out$by_theory

For theory.test(), as.data.frame(theory_out) returns theory_out$results$models. Inspect results$solutions when the question is which terms were selected, and inspect results$pairwise when the question is how far selected solution memberships differ across theories.

Solution tables

sol.df() returns a regular data frame directly.

tab <- sol.df(intermediate = sol, solution = "intermediate")
tab
is.data.frame(tab)

Use sol.df() when you want to report or inspect QCA minimization output in a compact table.

What to inspect first

In normal use, inspect objects in this order:

Print the object.
Call as.data.frame(x) for the clean table.
Look at x$summary if the function has one.
Open x$diagnostics only when you need the full detail.
For cluster.test(), inspect x$results$clusters and x$results$units after checking the overview.
For theory.test(), inspect x$results$solutions and x$results$pairwise after checking the model table.

The same conventions are described formally in ?qcaERT_conventions.