| DistatisR-package {DistatisR} | R Documentation |
implements three way metric multidimensional scaling: DISTATIS and COVSTATIS.
Description
DistatisR: package implements three way
metric multidimensional scaling:
DISTATIS and COVSTATIS.
Details
Analyzes sets of distance (or covariance) matrices collected on the same set of observations and find common and specific metric spaces.
| Package: | DistatisR |
| Type: | Package |
| Version: | 1.1.0 |
| Date: | 2022-09-28 |
| License: | GPL-2 |
| Depends: | prettyGraphs (>= 2.0.0), car |
The example shown here comes from Abdi
et al. (2007), distatis
paper on the sorting task.
Author(s)
Derek Beaton [aut, com, ctb], & Herve Abdi [aut, cre]
Maintainer: Herve Abdi <herve@utdallas.edu>
References
https://personal.utdallas.edu/~herve/
Abdi, H., Valentin, D., O'Toole, A.J., & Edelman, B. (2005). DISTATIS: The analysis of multiple distance matrices. Proceedings of the IEEE Computer Society: International Conference on Computer Vision and Pattern Recognition. (San Diego, CA, USA). pp. 42-47.
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., Dunlop, J.P., & Williams, L.J. (2009). How to compute reliability estimates and display confidence and tolerance intervals for pattern classiffers using the Bootstrap and 3-way multidimensional scaling (DISTATIS). NeuroImage, 45, 89–95.
Abdi, H., Williams, L.J., Valentin, D., & Bennani-Dosse, M. (2012). STATIS and DISTATIS: Optimum multi-table principal component analysis and three way metric multidimensional scaling. Wiley Interdisciplinary Reviews: Computational Statistics, 4, 124–167.
Chollet, S., Valentin, D., & Abdi, H. (2014). The free sorting task. In. P.V. Tomasco & G. Ares (Eds), Novel Techniques in Sensory Characterization and Consumer Profiling. Boca Raton: Taylor and Francis.
Valentin, D., Chollet, S., Nestrud, M., & Abdi, H. (2018). Sorting and similarity methodologies. In. S. Kemp, S., J. Hort, & T. Hollowood (Eds.), Descriptive Analysis in Sensory Evaluation. London: Wiley-Blackwell.
See Also
distatis BootFactorScores
BootFromCompromise DistanceFromSort
distatis GraphDistatisAll
GraphDistatisBoot GraphDistatisCompromise
GraphDistatisPartial GraphDistatisRv
mmds
prettyGraphs
Examples
# Here we use the sorting task from Abdi et al.' (2007) paper.
# where 10 Assessors sorted 8 beers.
#-----------------------------------------------------------------------------
# 1. Get the data from the 2007 sorting example
# this is the way 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))
# (alternatively we could have read a csv file)
# 1.5 Example of how to read a csv filw
# Sort <- read.table("BeeerSortingTask.csv", header=TRUE,
# sep=",", na.strings="NA", dec=".", row.names=1, strip.white=TRUE)
#------------------------------------------------------------------
# 2. Create the set of distance matrices
# (one distance matrix per assessor)
# (uses the function DistanceFromSort)
DistanceCube <- DistanceFromSort(Sort)
#------------------------------------------------------------------
# 3. Call the DISTATIS routine with the cube of distance as parameter
testDistatis <- distatis(DistanceCube)
# The factor scores for the beers are in
# testDistatis$res4Splus$F
# the factor scores for the assessors are in (RV matrice)
# testDistatis$res4Cmat$G
#------------------------------------------------------------------
# 4. Inferences on the beers obtained via bootstrap
# here we use two different bootstraps:
# 1. Bootstrap on factors (very fast but could be too liberal
# when the number of assessors is very large)
# 2. Complete bootstrap obtained by computing sets of compromises
# and projecting them (could be significantly longer because a lot
# of computations is required)
#
# 4.1 Get the bootstrap factor scores (with default 1000 iterations)
BootF <- BootFactorScores(testDistatis$res4Splus$PartialF)
#
# 4.2 Get the boostrap from full bootstrap (default niter = 1000)
F_fullBoot <- BootFromCompromise(DistanceCube,niter=1000)
#------------------------------------------------------------------
# 5. Create the Graphics
# 5.1 an Rv map
rv.graph.out <- GraphDistatisRv(testDistatis$res4Cmat$G)
# 5.2 a compromise plot
compromise.graph.out <- GraphDistatisCompromise(testDistatis$res4Splus$F)
# 5.3 a partial factor score plot
partial.scores.graph.out <-
GraphDistatisPartial(testDistatis$res4Splus$F,testDistatis$res4Splus$PartialF)
# 5.4 a bootstrap confidence interval plot
#5.4.1 with ellipses
boot.graph.out.ell <- GraphDistatisBoot(testDistatis$res4Splus$F,BootF)
#or
# boot.graph.out <- GraphDistatisBoot(testDistatis$res4Splus$F,F_fullBoot)
#5.4.2 with hulls
boot.graph.out.hull <- GraphDistatisBoot(testDistatis$res4Splus$F,BootF,ellipses=FALSE)
#or
# boot.graph.out <- GraphDistatisBoot(testDistatis$res4Splus$F,F_fullBoot,ellipses=FALSE)
#5.5 all the plots at once
all.plots.out <-
GraphDistatisAll(testDistatis$res4Splus$F,testDistatis$res4Splus$PartialF,
BootF,testDistatis$res4Cmat$G)