ordEval(formula, data, file=NULL, rndFile=NULL, variant=c("allNear","attrDist1","classDist1"), ...)"allNear", "attrDist1", or "classDist1". ordEvalNormalizingPercentile, mean, standard deviation, and expected probability according to value distribution.
With these statistics we can visualize significance of reinforcements using adapted box and whiskers plot.
formula can be interpreted in three ways, where the formula interface is the most elegant one,
but inefficient and inappropriate for large data sets. See also examples below. As formula one can specify:
In the data frame data take care to supply the ordinal data as factors and to provide equal levels for them
(this is not necessary what one gets with read.table).
See example below.
The output can be optionally written to files file and rndFile,
in a format used by visualization methods in plotOrdEval.
The variant of the algorithm actually used is controlled with variant parameter
which can have values "allNear", "attrDist1", and "classDist1". The default value
is "allNear" which takes all nearest neighbors into account in evaluation of attributes.
Variant "attrDist1" takes only neighbors with attribute value at most 1 different from
current case into account (for each attribute separately). This makes sense when we want to
see the thresholds of reinforcement, and therefore observe just small change up or down
(it makes sense to combine this with equalUpDown=TRUE in plot.ordEval function).
The "classDist1" variant takes only neighbors with class value at most 1 different from
current case into account. This makes sense if we want to observe strictly small
changes in upward/downward reinforcement and has little effect in practical applications.
There are some additional parameters (note ... ) some of which are common with other context-sensitive evaluation methods (e.g., ReliefF).
Their list of common parameters is available in helpCore (see subsection on attribute evaluation therein).
The parameters specific to ordEval are:
Evaluation of attributes without specifics of ordered attributes is covered in function attrEval.
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/
plot.ordEval,
CORElearn,
CoreModel,
helpCore,
infoCore.
#prepare a data set
dat <- ordDataGen(200)
# evaluate ordered features with ordEval
est <- ordEval(class ~ ., dat, ordEvalNoRandomNormalizers=100)
# print(est)
printOrdEval(est)
plot(est)
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