addBagPlot {wrGraph}

Add bagplot to existing plot

Description

This function adds a bagplot on an existing (scatter-)plot allowing to highlight the central area of the data. Briefly, a bagplot is a bivariate boxplot, see Bagplot, following the basic idea of a boxplot in two dimensions. Of course, multimodal distributions - if not separated first - may likely lead to mis-interpretation, similarly as it is known for interpreting boxplots. If a group of data consists only of 2 data-points, they will be conected using a straight line. It is recommended using transparent colors to highlight the core part of a group (if only 2 points are available, they will be conected using a straight line), in addition, one could use the option to re-plot all (non-outlyer) points (arguments reCol, rePch and reCex must be used).

Usage

addBagPlot(
  x,
  lev1 = 0.5,
  outCoef = 2,
  bagCol = NULL,
  bagCont = bagCol,
  bagLwd = 1.5,
  nCore = 4,
  outlCol = 2,
  outlPch = NULL,
  outlCex = 0.6,
  reCol = NULL,
  rePch = NULL,
  reCex = NULL,
  ctrPch = NULL,
  ctrCol = NULL,
  ctrCex = NULL,
  addSubTi = TRUE,
  returnOutL = FALSE,
  silent = TRUE,
  callFrom = NULL,
  debug = FALSE
)

Arguments

x

(matrix, list or data.frame) main numeric input of data/points to plot

lev1

(numeric) min content of data for central area (default 0.5 for 50 percent)

outCoef

(numeric) parameter for defining outliers (equivalent to range in boxplot)

bagCol

(character or integer) color for filling center part of bagplot (default light transparent grey); Note: It is highly suggested to use transparency, otherwise points underneith will be covered

bagCont

(character) color for inner and outer contours of bagplot

bagLwd

(numeric) line width for outer contour, set to NULL for not diaplaying outer contour (see also par)

nCore

(integer) decide when center should be determined by median or mean: if number of points reach nCore the median will be used

outlCol

(character or integer) color for highlighting outlyers (for text and replottig outlyers points), set to NULL for not highlighting outlyers at all

outlPch

(integer) symbol replotting highlighted outlyers (for text and replottig outlyers points), set to NULL for not replotting outlyer-points (see also par)

outlCex

(numeric) cex type expansion factor for labels of highlighted outlyers, set to NULL for not printing (row)names of outlyers (see also par)

reCol

(character or integer) color for replotting (non-outlyer) points, default set to NULL for not replotting

rePch

(integer) symbol for replotting (non-outlyer) points, default set to NULL for not re-plotting (see also par)

reCex

(numeric) cex type expansion factor for lfor replotting (non-outlyer) points, default set to NULL for not replotting

ctrPch

(integer) symbol for showing group center (see also par)

ctrCol

(character or integer) color for group center symbol

ctrCex

(numeric) cex type expansion factor for size of group center (see also par)

addSubTi

(logical) decide if subtitle (stating that potential outlyers were displayed separatetly) should be added in plot

returnOutL

(logical) decide if rownames of (potential) outlyer values should be returned when running the function

silent

(logical) suppress messages

callFrom

(character) allow easier tracking of messages produced

debug

(logical) display additional messages for debugging

Details

The outlyer detection works similar to the one used in boxplot: The distance of a given point is compared to the median distance of all points to their respective group-center plus the 25 - 75 quantile-distance (of all points) times the multiplicative factor of argument outCoef.

Value

This function returns primarily a plot, optionally it may return of matrix with outlyers (if argument returnOutL=TRUE)

See Also

plotPCAw, princomp

Examples

set.seed(2020); dat1 <- matrix(round(rnorm(2000),3),ncol=2); rownames(dat1) <- 1:nrow(dat1)
dat1 <- dat1 + 5*matrix(rep(c(0,1,1,0,0,0,1,1),nrow(dat1)/4), byrow=TRUE, ncol=2)
col1 <- rgb(red=c(153,90,203,255), green=c(143,195,211,125), blue=c(204,186,78,115),
  alpha=90, maxColorValue=255)
## suppose we know the repartition into 4 subgroups which we would like to highlight them
grp1 <- rep(1:4, nrow(dat1)/4)
plot(dat1, col=grey(0.8), xlab="x", ylab="y", las=1, pch=grp1)
for(i in 1:4) addBagPlot(dat1[which(grp1==i),], bagCol=col1[i])
## slightly improved
library(wrMisc)
col2 <- convColorToTransp(col1, 255)
plot(dat1, col=grey(0.8), xlab="x", ylab="y", las=1, pch=grp1)
for(i in 1:4) addBagPlot(dat1[which(grp1==i),], bagCol=col1[i], outlPch=i,
  outlCol=col2[i], bagLwd=3)