% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/qcaERT-tests.R
\name{qcaERT_tests}
\alias{qcaERT_tests}
\title{Choose a qcaERT robustness tool}
\description{
This page maps common QCA robustness tests to their respective functions
}
\section{Calibration}{

Use \code{\link[=calib.test]{calib.test()}} when you want to know whether a QCA solution is sensitive
to calibration thresholds. It perturbs one calibration anchor at a time and
reports how far each threshold can move before the monitored solution
changes or the search reaches a boundary.

You can also use \code{\link[=altset.test]{altset.test()}} when you want calibration perturbations to
be combined with other perturbations in sampled alternative analysis settings,
such as alternative inclusion cutoffs or frequency cutoffs.
}

\section{Inclusion or n.cut}{

Use \code{\link[=incl.test]{incl.test()}} to check the inclusion (consistency) cutoff for
sufficiency. It searches below and above the baseline \code{incl.cut} and records
the last safe cutoff and first failing cutoff in each direction.

Use \code{\link[=ncut.test]{ncut.test()}} when the concern is the minimum number of cases under which
a truth table row is declared as a remainder. It searches lower and upper
\code{n.cut} values and records when the monitored solution changes or the
feasible boundary is reached.
}

\section{Cases}{

Use \code{\link[=loo.test]{loo.test()}} when you suspect individual cases may drive the solution.
It removes selected cases one at a time and compares each reduced analysis
with the baseline.

Use \code{\link[=subsample.test]{subsample.test()}} when you want repeated partial-sample checks. It draws
subsamples, rebuilds the QCA analysis, and summarizes how often the baseline
solution is preserved across runs. This is a stringent, punishing robustness
check and is most useful when sample-composition sensitivity is substantively
important.
}

\section{Groups or clusters}{

Use \code{\link[=cluster.test]{cluster.test()}} when you expect heterogeneity across clusters, groups,
or repeated units. It compares cluster-specific consistency, coverage, and
related fit patterns against the overall truth table.
}

\section{Theory Specifications}{

Use \code{\link[=theory.test]{theory.test()}} when you want to compare several theoretically motivated
condition sets under the same outcome, truth-table cutoffs, solution type,
exclusion handling, and settings for \code{which_M} and \code{i_mode}.
}

\section{Reporting QCA Solutions}{

Use \code{\link[=sol.df]{sol.df()}} when you want QCA minimization objects turned into a compact,
data frame. Use \code{\link[=sol.chart]{sol.chart()}} when you want that table
rendered as a visual chart of prime implicants.
}

\section{How To Read The Results}{

Most qcaERT robustness functions return \code{diagnostics}, \code{results}, and
\code{settings}. \code{diagnostics} is the detailed table; \code{results} is the clean table
returned by \code{as.data.frame()}. \code{print()} shows a concise summary. The shared
output structure is described in \code{?qcaERT_conventions}.

For \code{\link[=calib.test]{calib.test()}}, \code{\link[=incl.test]{incl.test()}}, and \code{\link[=theory.test]{theory.test()}} objects, \code{plot()}
methods provide optional visual inspection helpers when \code{ggplot2} is
installed. \code{\link[=sol.chart]{sol.chart()}} provides the corresponding visual presentation for
\code{\link[=sol.df]{sol.df()}} tables. See \code{?qcaERT_plots}.
}

\examples{
\donttest{
?qcaERT_tests
?qcaERT_conventions
?qcaERT_plots
}

}
