GraphDistatisRv {DistatisR}

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

Description

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.

Usage

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\times 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.

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.

Value

constraints

A set of plot constraints that are returned.

participant.colors

A set of colors for the participants are returned.

Author(s)

Derek Beaton and Herve Abdi

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

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!