wrGraph: addBagPlot – R documentation

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addBagPlot

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,
  returnOutL = FALSE,
  callFrom = NULL,
  silent = TRUE
)

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`)
`returnOutL`	(logical) decide if rownames of (potential) outlyer values should be returned when running the function
`callFrom`	(character) allow easier tracking of messages produced
`silent`	(logical) suppress messages

Value

plot, optional return of matrix with outlyers

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)

wrGraph

Graphics in the Context of Analyzing High-Throughput Data

v1.2.3

GPL-3

Authors

Wolfgang Raffelsberger [aut, cre]

Initial release