GraphDistatisRv function

Plot maps of the factor scores (from the Rv matrix) of the distance matrices for a DISTATIS analysis

Plot maps of the factor scores of the observations for a distatis

analysis. The factor scores are obtained from the eigen-decomposition of the between distance matrices cosine matrix (often a matrix of Rv coefficients). Note that the factor scores for the first dimension are always positive. There are used to derive the $alpha$ weights for DISTATIS .

GraphDistatisRv(
  RvFS,
  axis1 = 1,
  axis2 = 2,
  ZeTitle = "Distatis-Rv Map",
  participant.colors = NULL,
  nude = FALSE,
  RvCtr = NULL
)

Arguments

• RvFS: The factor scores of the distance matrices ($res4Cmat$G

  from distatis).

• axis1: The dimension for the horizontal axis of the plots.

• axis2: The dimension for the vertical axis of the plots.

• ZeTitle: General title for the plots.

• participant.colors: A $I*1$ matrix (with $I$ = # participants) of color names for the observations. If NULL (default), prettyGraphs chooses.

• nude: When nude is TRUE the labels for the observations are not plotted (useful when editing the graphs for publication).

• RvCtr: Contributions of each participant. If codeNULL (default), these are computed from RvFS.

Returns

• constraints: A set of plot constraints that are returned.

• participant.colors: A set of colors for the participants are returned.

Details

Note that, in the current version, the graphs are plotted as R-plots and are not passed back by the routine. So the graphs need to be saved "by hand" from the R graphic windows. We plan to improve this in a future version.

Examples

# 1. Load the DistAlgo data set (available from the DistatisR package)
data(DistAlgo)
# DistAlgo is a 6*6*4 Array (faces*faces*Algorithms)
#-----------------------------------------------------------------------------
# 2. Call the DISTATIS routine with the array of distance (DistAlgo) as parameter
DistatisAlgo <- distatis(DistAlgo)
# 3. Plot the compromise map with the labels for the first 2 dimensions
# DistatisAlgo$res4Cmat$G are the factors scores
#  for the 4 distance matrices (i.e., algorithms)
 GraphDistatisRv(DistatisAlgo$res4Cmat$G,ZeTitle='Rv Mat')
# Et voila!

References

The plots are similar to the graphs described in:

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., 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.

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., & 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.

The $R_V$ coefficient is described in

Abdi, H. (2007). RV coefficient and congruence coefficient. In N.J. Salkind (Ed.): Encyclopedia of Measurement and Statistics. Thousand Oaks (CA): Sage. pp. 849--853.

Abdi, H. (2010). Congruence: Congruence coefficient, RV coefficient, and Mantel Coefficient. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): Encyclopedia of Research Design. Thousand Oaks (CA): Sage. pp. 222--229.

These papers are available from https://personal.utdallas.edu/~herve/

See Also

GraphDistatisAll

GraphDistatisCompromise GraphDistatisPartial

GraphDistatisBoot GraphDistatisRv

distatis

Author(s)

Derek Beaton and Herve Abdi