The method plot visualizes the results of ordEval algorithm with an adapted
box-and-whiskers plots. The method printOrdEval prints summary of the results
in a text format.
plotOrdEval(file, rndFile, ...)
# S3 method for ordEval
plot(x, graphType=c("avBar", "attrBar", "avSlope"), ...)
printOrdEval(x)The object containing results of ordEval algorithm obtained by calling ordEval.
If this object is not given, it has to be constructed from files file and rndFile.
Name of file where evaluation results of ordEval algorithm were written to.
Name of file where evaluation of random normalizing attributes by ordEval algorithm were written to.
The type of the graph to produce. Can be any of "avBar", "attrBar", "avSlope".
Other options controlling graphical output, used by specific graphical methods. See details.
The method returns no value.
The output of function ordEval either returned directly or stored in files file and rndFile
is read and visualized. The type of graph produced is controlled by graphType parameter:
avBar the positive and negative reinforcement of each value of each attribute is visualized
as the length of the bar. For each value also a normalizing modified box and whiskers plot
is produced above it, showing the confidence interval of the same attribute value under the assumption
that the attribute contains no information. If the length of the bar is outside the normalizing whiskers this
is a statistically significant indication that the value is important.
attrBar the positive and negative reinforcement for each attribute is visualized
as the length of the bar. This reinforcement is weighted sum of contributions of individual
values visualized with avBar graph type.
avSlope the positive and negative reinforcement of each value of each attribute is visualized
as the slope of the line segment connecting consequent values
The avBar and avSlope produce several graphs (one for each attribute). In order to see them all on
an interactive device use devAskNewPage. On some platforms graphical window has a menu item
history, where one can turn on recording and browse through recent pages. Alternatively use any of non-interactive devices
such as pdf or postscript. Some support for opening and handling of these devices is provided
by function preparePlot. The user should take care to call dev.off after completion of the operations.
There are some additional optional parameters … which are important to all or for some graph types.
ciType The type of the confidence interval in "avBar" and "attrBar" graph types. Can be "two.sided",
"upper", "lower", or "none".
Together with ordEvalNormalizingPercentile parameter in ordEval, ciType, and ciDisplay
controls the type, length and display of of confidence intervals for each value.
ciDisplay The way how confidence intervals are displayed. Can be "box" or "color". The value
"box" displays confidence interval as box and whiskers plot above the actual value with whiskers representing
confidence percentiles.
The value "color" displays only the upper limit of confidence interval, namely the value
(represented with a length of the bar) beyond the confidence interval is displayed with more intensive color or shade.
equalUpDown a boolean specifying if upward and downward reinforcement of the same value are to be displayed side by side on the same level; it usually makes sense to
set this parameter to TRUE when specifying a single value differences by setting variant="attrDist1" in ordEval function.
graphTitle specifies text to incorporate into the title.
attrIdx displays plot for a single attribute with specified index.
xlabel label of lower horizontal axis.
ylabLeft label of left vertical axis.
ylabRight label of right vertical axis; the default value is
colors a vector with four colors specifying colors of reinforcement bars for down, down_beyond, up, and up_beyond, respectively.
If set to NULL this produces black and white graph with shades of gray.
The colors down_beyond and up_beyond depict the confidence interval if parameter ciDisplay="color".
The default values are colors=c("green","lightgreen","blue","lightblue").
Marko Robnik-Sikonja, Koen Vanhoof: Evaluation of ordinal attributes at value level. Knowledge Discovery and Data Mining, 14:225-243, 2007
Marko Robnik-Sikonja, Igor Kononenko: Theoretical and Empirical Analysis of ReliefF and RReliefF. Machine Learning Journal, 53:23-69, 2003
Some of the references are available also from http://lkm.fri.uni-lj.si/rmarko/papers/
# NOT RUN {
# prepare a data set
dat <- ordDataGen(200)
# evaluate ordered features with ordEval
oe <- ordEval(class ~ ., dat, ordEvalNoRandomNormalizers=200)
plot(oe)
# printOrdEval(oe)
# the same effect we achieve by storing results to files
tmp <- ordEval(class ~ ., dat, file="profiles.oe",
rndFile="profiles.oer", ordEvalNoRandomNormalizers=200)
plotOrdEval(file="profiles.oe", rndFile="profiles.oer",
graphType="attrBar")
# }
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