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R: Plot maps of the factor scores (from the Rv matrix) of the...

GraphDistatisRv

R Documentation

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 I1* 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 NULL (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 www.utdallas.edu/~herve

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!

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(DistatisR)
Loading required package: prettyGraphs
Loading required package: car
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/DistatisR/GraphDistatisRv.Rd_%03d_medium.png", width=480, height=480)
> ### Name: GraphDistatisRv
> ### Title: Plot maps of the factor scores (from the Rv matrix) of the
> ###   distance matrices for a DISTATIS analysis
> ### Aliases: GraphDistatisRv
> ### Keywords: DistatisR
> 
> ### ** 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')
dev.new(): using pdf(file="Rplots137.pdf")
$constraints
$constraints$minx
    dim 1 
-0.103478 

$constraints$maxx
  dim 1 
1.03478 

$constraints$miny
     dim 2 
-0.6338271 

$constraints$maxy
    dim 2 
0.7913781 


$participant.colors
     [,1]     
[1,] "#305ABF"
[2,] "#84BF30"
[3,] "#BF30AD"
[4,] "#30BFA7"

> # Et voila!
> 
> 
> 
> 
> 
> dev.off()
png 
  2 
>