The type argument specifies whether regular minimality or
regular maximality is to be checked. "probability.different"
and "percent.same" are for datasets in the
probability-different and percent-same formats, and imply regular
minimality and regular maximality checks, respectively.
"reg.minimal" and "reg.maximal" can be specified to
force checking for regular minimality and regular maximality,
respectively, independent of the used dataset. In particular,
"reg.minimal" and"reg.maximal" are to be used for
datasets that are properly in the general format. check.regular calls check.data. In
particular, the rows and columns of the canonical
representation matrix (see ‘Value’) are canonically
relabeled based on the labeling provided by
check.data. That is, using the
check.data labeling, the pairs of points of subjective
equality (PSEs) are assigned identical labels, leaving intact the
labeling of the rows and relabeling the columns with their
corresponding PSEs. If the data X do not satisfy regular
minimality/maximality, check.regular produces respective
messages. The latter give information about parts of X
violating that condition.
Regular minimality/maximality is a fundamental property of
discrimination and means that
- every row contains a single minimal/maximal entry;
- every column contains a single minimal/maximal entry;
- an entry $p\_ij$ of
X which is
minimal/maximal in the $i$th row is also minimal/maximal
in the $j$th column, and vice versa.
If $p\_ij$ is the entry which is minimal/maximal in
the $i$th row and in the $j$th column, the
$i$th row object (in one, the first, observation area) and
the $j$th column object (in the other, the second,
observation area) are called each other's PSEs. In psychophysical
applications, for instance, observation area refers to the two fixed
and perceptually distinct areas in which the stimuli are presented
pairwise; for example, spatial arrangement (left versus right) or
temporal order (first versus second).