DistatisR: GraphDistatisAll – R documentation

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GraphDistatisAll

This function combines the functionality of GraphDistatisCompromise, GraphDistatisPartial, GraphDistatisBoot, and GraphDistatisRv.

Description

This function produces 4 plots: (1) a compromise plot, (2) a partial factor scores plot, (3) a bootstrap confidence intervals plot, and (4) a Rv map.

Usage

GraphDistatisAll(FS, PartialFS, FBoot, RvFS, axis1 = 1, axis2 = 2, constraints = NULL, 
	item.colors = NULL, participant.colors = NULL, ZeTitleBase = NULL, nude = FALSE, 
	Ctr = NULL, RvCtr=NULL, color.by.observations = TRUE, lines = TRUE, 
	lwd = 3.5, ellipses = TRUE, fill = TRUE, fill.alpha = 0.27, percentage = 0.95)

Arguments

`FS`	The factor scores of the observations (`$res4Splus$F`from `distatis`)
`PartialFS`	The partial factor scores of the observations (`$res4Splus$PartialF` from `distatis`)
`FBoot`	is the bootstrapped factor scores array (`FBoot` obtained from `BootFactorScores` or `BootFromCompromise`)
`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.
`constraints`	constraints for the axes
`item.colors`	A I1* matrix (with I = # observations) of color names for the observations. If NULL (default), `prettyGraphs` chooses.
`participant.colors`	A I1* matrix (with I = # participants) of color names for the observations. If NULL (default), `prettyGraphs` chooses.
`ZeTitleBase`	General title for the plots.
`nude`	When `nude` is `TRUE` the labels for the observations are not plotted (useful when editing the graphs for publication).
`Ctr`	Contributions of each observation. If NULL (default), these are computed from FS
`RvCtr`	Contributions of each participant. If NULL (default), these are computed from RvFS
`color.by.observations`	if `TRUE` (default), the partial factor scores are colored by `item.colors`. When `FALSE`, `participant.colors` are used.
`lines`	If `TRUE` (default) then lines are drawn between the partial factor score of an observation and the compromise factor score of the observation.
`lwd`	Thickness of the line plotting the ellipse or hull.
`ellipses`	a boolean. When `TRUE` will plot ellipses (from `car` package). When `FALSE` will plot peeled hulls (from `prettyGraphs` package).
`fill`	when `TRUE`, fill in the ellipse with color. Related to ellipses only.
`fill.alpha`	transparency index when filling in the ellipses. Related to ellipses only.
`percentage`	A value to determine the percent coverage of the bootstrap partial factor scores to provide ellipse or hull confidence intervals.

Value

`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.

Author(s)

Derek Beaton and Herve Abdi

Examples

# 1. Load the Sort data set from the SortingBeer example  (available from the DistatisR package)
data(SortingBeer)
# Provide an 8 beers by 10 assessors results of a sorting task
#-----------------------------------------------------------------------------
# 2. Create the set of distance matrices (one distance matrix per assessor)
#    (ues 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 partial factor score for the beers for the assessors are in 
#  testDistatis$res4Splus$PartialF
# 
# 4. Get the bootstraped factor scores (with default 1000 iterations)
BootF <- BootFactorScores(testDistatis$res4Splus$PartialF)
#-----------------------------------------------------------------------------
# 5. Create the Graphics with GraphDistatisAll
#
GraphDistatisAll(testDistatis$res4Splus$F,testDistatis$res4Splus$PartialF,
	BootF,testDistatis$res4Cmat$G)

DistatisR

DiSTATIS Three Way Metric Multidimensional Scaling

v1.0.1

GPL-2

Authors

Derek Beaton [aut, com, ctb], Cherise Chin Fatt [ctb], Herve Abdi [aut, cre]

Initial release

2013-07-10