## Not run:
#
# # load data (CLSI EP05-A2 Within-Lab Precision Experiment)
# data(dataEP05A2_3)
#
# dataEP05A2_3$user <- sample(rep(c(1,2), 40))
# dataEP05A2_3$cls2 <- sample(rep(c(1,2), 40))
#
# # plot data as variability-chart, using automatically determined parameter
# # settings (see 'dynParmSet')
# varPlot(y~day/run, dataEP05A2_3)
#
# # display intercept (total mean)
# varPlot(y~day/run, dataEP05A2_3, MeanLine=list(var="int"))
#
# # use custom VC-names
# varPlot(y~day/run, dataEP05A2_3, VCnam=list(text=c("_Day", "_Run")))
#
# # re-plot now also indicating dayly means as blue horizontal lines
# varPlot(y~day/run, dataEP05A2_3, MeanLine=list(var=c("day", "int"), col="blue"))
#
# # now use variable-names in names of individual factor-levels and use a different
# # notation of the nesting structure
# varPlot(y~day+day:run, dataEP05A2_3, useVarNam=TRUE)
#
# # use alternating backgrounds for each level of factor "day"
# # (top-level factor is default)
# # use a simplified model formula (NOTE: only valid for function 'varPlot')
# varPlot(y~day+run, dataEP05A2_3, BG=list(col=c("lightblue", "lightgray"), border=NA))
#
# # now also color the corresponding row in the table accordingly
# varPlot( y~day+run, dataEP05A2_3,
# BG=list(col=c("lightblue", "lightgray"), border=NA, col.table=TRUE))
#
# # assign different point-colors according to a classification variable
# # not part of the model (artificial example in this case)
# varPlot( y~day+day:run, dataEP05A2_3,
# Points=list(col=list(var="user", col=c("red", "green"))) )
#
# # assign different plotting symbols according to a classification
# # variable not part of the model
# varPlot( y~day+day:run, dataEP05A2_3,
# Points=list(pch=list(var="user", pch=c(2, 8))) )
#
# # now combine point-coloring and plotting symbols
# # to indicate two additional classification variables
# varPlot( y~day+day:run, dataEP05A2_3,
# Points=list(col=list(var="user", col=c("red", "green")),
# pch=list(var="cls2", pch=c(2, 8))) )
#
# # use magenta lines between each level of factor "run"
# varPlot(y~day/run, dataEP05A2_3, BG=list(var="run", border="magenta"))
#
# # plot SDs for each run
# varPlot(y~day+day:run, dataEP05A2_3, type=2)
#
# # use CV instead of SD
# varPlot(y~day/run, dataEP05A2_3, type=2, VARtype="CV")
#
# # now plot variability-chart and SD-plot in one window
# varPlot(y~day/run, dataEP05A2_3, type=3, useVarNam=TRUE)
#
# # now further customize the plot
# varPlot( y~day/run, dataEP05A2_3, BG=list(col=c("lightgray", "wheat")),
# YLabel=list(font=2, col="green", cex=1, text="Custom Y-Axis Label"),
# VCnam=list(col="red", font=4),
# VarLab=list(col="magenta", font=3, srt=0))
#
# # create variability-chart of the example dataset in the CLSI EP05-A2
# # guidline (listed on p.25)
# data(Glucose)
# varPlot(result~day/run, Glucose, type=3)
#
# # use individual settings of 'VarLab' and 'VSpace' for each variance component
# varPlot(result~day/run, Glucose, type=3,
# VarLab=list(list(srt=45, col="red", font=2),
# list(srt=90, col="blue", font=3)), VSpace=c(.25, .75))
#
# # set individual titles for both plot when 'type=3'
# varPlot( result~day/run, Glucose, type=3,
# Title=list(list(main="Variability Chart"),
# list(main="Plot of SD-Values")))
#
# # more complex experimental design
# data(realData)
# Data <- realData[realData$PID == 1,]
# varPlot(y~lot/calibration/day/run, Data, type=3)
#
# # improve visual appearance of the plot
# varPlot(y~lot/calibration/day/run, Data, type=3, keep.order=FALSE,
# BG=list(var="calibration", col=c("white", "lightgray")))
#
# # add horizontal lines indicating mean-value for each factor-level of all variables
# varPlot(y~lot/calibration/day/run, Data, type=3, keep.order=FALSE,
# BG=list(var="calibration",
# col=c("aquamarine","antiquewhite2","antiquewhite4",
# "antiquewhite1","aliceblue","antiquewhite3",
# "white","antiquewhite","wheat" ),
# col.table=TRUE),
# MeanLine=list(var=c("lot", "calibration", "day", "int"),
# col=c("orange", "blue", "darkgreen", "yellow"),
# lwd=c(2,2,2,2)))
#
# # now also highlight bounds between factor levels of "lot" and "day"
# # as vertical lines and extend them into the table
# varPlot(y~lot/calibration/day/run, Data, type=3, keep.order=FALSE,
# BG=list(var="calibration",
# col=c("aquamarine","antiquewhite2","antiquewhite4",
# "antiquewhite1","aliceblue","antiquewhite3",
# "white","antiquewhite","wheat" ),
# col.table=TRUE),
# MeanLine=list(var=c("lot", "calibration", "day", "int"),
# col=c("orange", "blue", "darkgreen", "yellow"),
# lwd=c(2,2,2,2)),
# VLine=list(var=c("lot", "day"), col=c("black", "skyblue1"),
# lwd=c(2, 1), col.table=TRUE))
#
# # one can use argument 'JoinLevels' to join factor-levels or a variable
# # nested within a higher-level factor, 'VLine' is used to separate levels
# # of variables "calibration" and "lot" with different colors
# varPlot(y~calibration/lot/day/run, Data,
# BG=list(var="calibration",
# col=c("#f7fcfd","#e5f5f9","#ccece6","#99d8c9",
# "#66c2a4","#41ae76","#238b45","#006d2c","#00441b"),
# col.table=TRUE),
# VLine=list(var=c("calibration", "lot"),
# col=c("black", "darkgray"), lwd=c(2,1), col.table=TRUE),
# JoinLevels=list(var="lot", col=c("#ffffb2","orangered","#feb24c"),
# lwd=c(2,2,2)),
# MeanLine=list(var="lot", col="blue", lwd=2))
# ## End(Not run)
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