GraphDistatisPartial function

Plot maps of the factor scores and partial factor scores of the observations for a DISTATIS analysis.

GraphDistatisPartial plots maps of the factor scores of the observations from a distatis analysis. GraphDistatisPartial gives a map of the factors scores of the observations plus partial factor scores, as "seen" by each of the matrices.

GraphDistatisPartial(
  FS,
  PartialFS,
  axis1 = 1,
  axis2 = 2,
  constraints = NULL,
  item.colors = NULL,
  participant.colors = NULL,
  ZeTitle = "Distatis-Partial",
  Ctr = NULL,
  color.by.observations = TRUE,
  nude = FALSE,
  lines = TRUE
)

Arguments

• FS: The factor scores of the observations ($res4Splus$F from the output of distatis).
• PartialFS: The partial factor scores of the observations ($res4Splus$PartialF from distatis)
• axis1: The dimension for the horizontal axis of the plots.
• axis2: The dimension for the vertical axis of the plots.
• constraints: constraints for the axes
• item.colors: A $I*1$ matrix (with $I$ = # observations) of color names for the observations. If NULL (default), prettyGraphs chooses.
• participant.colors: A $I*1$ matrix (with $I$ = # participants) of color names for the observations. If NULL (default), prettyGraphs chooses (with function prettyGraphs::).
• ZeTitle: General title for the plots.
• Ctr: Contributions of each observation. If NULL (default), these are computed from FS
• color.by.observations: if TRUE (default), the partial factor scores are colored by item.colors. When FALSE, participant.colors are used.
• nude: When nude is TRUE the labels for the observations are not plotted (useful when editing the graphs for publication).
• lines: If TRUE (default) then lines are drawn between the partial factor score of an observation and the compromise factor score of the observation.

Returns

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

• item.colors: A set of colors for the observations 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 (face*face*Algorithm)
#-----------------------------------------------------------------------------
# 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$res4Splus$F are the factors scores for the 6 observations (i.e., faces)
# DistatisAlgo$res4Splus$PartialF are the partial factors scores
        ##(i.e., one set of factor scores per algorithm)
 GraphDistatisPartial(DistatisAlgo$res4Splus$F,DistatisAlgo$res4Splus$PartialF)

References

The plots are similar to the graphs from

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.

Paper available from https://personal.utdallas.edu/~herve/

See Also

GraphDistatisAll

GraphDistatisCompromise GraphDistatisPartial

GraphDistatisBoot GraphDistatisRv

distatis

Author(s)

Derek Beaton and Herve Abdi