Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/check.regular.R

`check.regular`

is used to check whether the data satisfy
regular minimality/maximality.

1 2 | ```
check.regular(X, type = c("probability.different", "percent.same",
"reg.minimal", "reg.maximal"))
``` |

`X` |
a required square matrix or data frame of numeric
data. No |

`type` |
an optional character string giving the type of check to
be performed. This must be one of |

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).

If the data do satisfy regular minimality/maximality,
`check.regular`

returns a named list consisting of the
following four components:

`canonical.representation` |
a matrix giving the
representation of |

`canonical.transformation` |
a data frame giving the
permutation of the columns of |

`check` |
a character string giving the check that was
performed. This is either |

`in.canonical.form` |
logical. If |

Thomas Kiefer, Ali Uenlue. Based on original MATLAB source by Ehtibar N. Dzhafarov.

Dzhafarov, E. N. and Colonius, H. (2006) Reconstructing
distances among objects from their discriminability.
*Psychometrika*, **71**, 365–386.

Dzhafarov, E. N. and Colonius, H. (2007) Dissimilarity
cumulation theory and subjective metrics. *Journal of
Mathematical Psychology*, **51**, 290–304.

Uenlue, A. and Kiefer, T. and Dzhafarov, E. N.
(2009) Fechnerian scaling in **R**: The package fechner.
*Journal of Statistical Software*, **31**(6), 1–24.
URL http://www.jstatsoft.org/v31/i06/.

`check.data`

for checking data format;
`fechner`

, the main function for Fechnerian scaling.
See also `fechner-package`

for general information
about this package.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
## dataset \link{wish} satisfies regular minimality in canonical form
check.regular(wish)
## dataset \link{regMin} satisfies regular minimality in non-canonical
## form and so is canonically transformed and relabeled
regMin
check.regular(regMin)
## dataset \link{noRegMin} does satisfy neither regular minimality nor
## regular maximality
check.regular(noRegMin, type = "probability.different")
check.regular(noRegMin, type = "reg.maximal")
## dataset \link{morse} satisfies regular maximality in canonical form
check.regular(morse, type = "percent.same")
## part of \link{morse} data satisfies regular maximality
check.regular(morse[c(2, 27:36), c(2, 27:36)], type = "reg.maximal")
``` |

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