Chi2DistanceFromSort {DistatisR} | R Documentation |
Chi2DistanceFromSort
:
Creates a 3-dimensional
distance array from the results
of a sorting task.
Description
Chi2DistanceFromSort
:
Takes the results from a (plain) sorting task where
assessors sort
observations into (mutually exclusive) groups
(i.e., one object is
in one an only one group).
Chi2DistanceFromSort
creates an
array of distance in which
each of the
"slices"
stores the (sorting) distance matrix of the
th assessor.
In one of
these distance matrices,
the distance between rows is the
distance between rows when the results
of the task are coded as 0/1 group
coding (i.e., the "complete disjunctive coding"
as used in multiple
correspondence analysis,
see Abdi & Valentin, 2007, for more)
Usage
Chi2DistanceFromSort(X)
Arguments
X |
gives the results of a sorting task (see example below) as a objects (row) by assessors (columns) matrix. |
Details
The ouput ot the function Chi2DistanceFromSort
is used as input for the
function distatis
.
The input should have assessors as columns and observations as rows (see example below)
Value
Chi2DistanceFromSort
returns an
array of
distances matrices
(between the
observations)
Author(s)
Herve Abdi
References
See examples in
Abdi, H., Valentin, D., Chollet, S., & Chrea, C. (2007). Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications. Food Quality and Preference, 18, 627–640.
Abdi, H., & Valentin, D., (2007). Some new and easy ways to describe, compare, and evaluate products and assessors. In D., Valentin, D.Z. Nguyen, L. Pelletier (Eds) New trends in sensory evaluation of food and non-food products. Ho Chi Minh (Vietnam): Vietnam National University-Ho chi Minh City Publishing House. pp. 5–18.
Abdi, H., & Valentin, D. (2007). Multiple correspondence analysis. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 651-657.
These papers are available from https://personal.utdallas.edu/~herve/
See Also
Examples
# 1. Get the data from the 2007 sorting example
# this is the eay they look from Table 1 of
# Abdi et al. (2007).
# Assessors
# 1 2 3 4 5 6 7 8 9 10
# Beer Sex f m f f m m m m f m
# -----------------------------
#Affligen 1 4 3 4 1 1 2 2 1 3
#Budweiser 4 5 2 5 2 3 1 1 4 3
#Buckler_Blonde 3 1 2 3 2 4 3 1 1 2
#Killian 4 2 3 3 1 1 1 2 1 4
#St. Landelin 1 5 3 5 2 1 1 2 1 3
#Buckler_Highland 2 3 1 1 3 5 4 4 3 1
#Fruit Defendu 1 4 3 4 1 1 2 2 2 4
#EKU28 5 2 4 2 4 2 5 3 4 5
#
# 1.1. Create the
# Name of the Beers
BeerName <- c('Affligen', 'Budweiser','Buckler Blonde',
'Killian','St.Landelin','Buckler Highland',
'Fruit Defendu','EKU28')
# 1.2. Create the name of the Assessors
# (F are females, M are males)
Juges <- c('F1','M2', 'F3', 'F4', 'M5', 'M6', 'M7', 'M8', 'F9', 'M10')
# 1.3. Get the sorting data
SortData <- c(1, 4, 3, 4, 1, 1, 2, 2, 1, 3,
4, 5, 2, 5, 2, 3, 1, 1, 4, 3,
3, 1, 2, 3, 2, 4, 3, 1, 1, 2,
4, 2, 3, 3, 1, 1, 1, 2, 1, 4,
1, 5, 3, 5, 2, 1, 1, 2, 1, 3,
2, 3, 1, 1, 3, 5, 4, 4, 3, 1,
1, 4, 3, 4, 1, 1, 2, 2, 2, 4,
5, 2, 4, 2, 4, 2, 5, 3, 4, 5)
# 1.4 Create a data frame
Sort <- matrix(SortData,ncol = 10, byrow= TRUE, dimnames = list(BeerName, Juges))
#
#-----------------------------------------------------------------------------
# 2. Create the set of distance matrices (one distance matrix per assessor)
# (use the function DistanceFromSort)
DistanceCube <- Chi2DistanceFromSort(Sort)
#-----------------------------------------------------------------------------
# 3. Call the DISTATIS routine with the cube of distance
# obtained from DistanceFromSort as a parameter for the distatis function
testDistatis <- distatis(DistanceCube)