implements three way metric multidimensional scaling: DISTATIS and COVSTATIS.
DistatisR: package implements three way metric multidimensional scaling: DISTATIS and COVSTATIS.
package
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.
# 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)
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.
distatis BootFactorScores
BootFromCompromise DistanceFromSort
distatis GraphDistatisAll
GraphDistatisBoot GraphDistatisCompromise
GraphDistatisPartial GraphDistatisRv
mmds
prettyGraphs
Derek Beaton [aut, com, ctb], & Herve Abdi [aut, cre]
Maintainer: Herve Abdi herve@utdallas.edu
Useful links