Description Usage Format Note References See Also Examples

Artificial data of fictitious ‘discrimination probabilities’
among *10* fictitious stimuli.

1 |

The `regMin`

data frame consists of *10* rows and *10*
columns, representing the fictitious stimuli presented in the first
and second observation area, respectively. Each number, a numeric,
in the data frame is assumed to give the relative frequency of
perceivers scoring ‘different’ to the row stimulus
‘followed’ by the column stimulus.

This dataset is artificial and included for illustrating regular
minimality in the non-canonical form. It differs from the
artificial data `noRegMin`

only in the entry in row
\#*9* and column \#*10*.

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/.

`noRegMin`

for the other artificial data violating
regular minimality; `check.data`

for checking data
format; `check.regular`

for checking regular
minimality/maximality; `fechner`

, the main function for
Fechnerian scaling. See also `morse`

for Rothkopf's
Morse code data, `wish`

for Wish's Morse-code-like data,
and `fechner-package`

for general information about this
package.

1 2 3 | ```
## dataset regMin satisfies regular minimality in non-canonical form
regMin
check.regular(regMin, type = "reg.minimal")
``` |

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