stackedBars {CPTtools} | R Documentation |
This produces a series of stacked bar plots staggered so that the baseline corresponds to a particular state level. This is primarily designed for producing plots of probability vectors coming out of Bayes net scoring.
stackedBars(data, profindex, ..., ylim = c(min(offsets) - 0.25, max(1 + offsets)), cex.names = par("cex.axis"), percent=TRUE, digits = 2*(1-percent), labrot=FALSE)
data |
A |
profindex |
The index of the proficiency which should be used as a baseline. |
... |
Graphical arguments passed to |
ylim |
Default limits for Y axis. |
cex.names |
Magnification for names. |
percent |
Logical value. If true data values are treated as percentages instead of probabilities. |
digits |
Number of digits for overlaid numeric variables. |
labrot |
If true, labels are rotated 90 degrees. |
This plot type assumes that each column in its first argument is a
probability vector. It then produces a stacked bar for each column.
The baseline of the bar is offset by the probability for being in the
category marked by profindex
or below.
The probability values are overlaid on the bars.
Russell Almond
This plot type was initially developed in Jody Underwood's Evolve project.
Almond, R. G., Shute, V. J., Underwood, J. S., and Zapata-Rivera, J.-D (2009). Bayesian Networks: A Teacher's View. International Journal of Approximate Reasoning. 50, 450-460.
compareBars
, colorspread
,
buildMarginTab
, marginTab
,
barplot
,stackedBarplot
margins <- data.frame ( Trouble=c(Novice=.19,Semester1=.24,Semester2=.28,Semseter3=.20,Semester4=.09), NDK=c(Novice=.01,Semester1=.09,Semester2=.35,Semseter3=.41,Semester4=.14), Model=c(Novice=.19,Semester1=.28,Semester2=.31,Semseter3=.18,Semester4=.04) ) stackedBars(margins,3, main="Marginal Distributions for NetPASS skills", sub="Baseline at 3rd Semester level.", cex.names=.75, col=hsv(223/360,.2,0.10*(5:1)+.5)) stackedBars(margins,3, main="Marginal Distributions for NetPASS skills", sub="Baseline at 3rd Semester level.", percent=FALSE,digits=2, cex.names=.75, col=hsv(223/360,.2,0.10*(5:1)+.5))