ordEval {CORElearn} | R Documentation |
The method evaluates the quality of ordered attributes specified by the formula with ordEval algorithm.
ordEval(formula, data, file=NULL, rndFile=NULL, variant=c("allNear","attrDist1","classDist1"), ...)
formula |
Formula specifies the attributes to be evaluated and the target variable. |
data |
Data frame with evaluation data. |
file |
Name of file where evaluation results will be written to. |
rndFile |
Name of file where evaluation of random normalizing attributes will be written to. |
variant |
Name of the variant of ordEval algorithm. Can be any of "allNear", "attrDist1" , or "classDist1" . |
... |
Other options used also by other context-sensitive evaluation methods (e.g., ReliefF). |
Parameter formula
is used as a mechanism to select features (attributes)
and prediction variable (class). Only simple terms can be used and
interaction expressed in formula syntax are not supported. The simplest way is
to specify just response variable as parameter: class ~ .
.
In this case all the other columns in the data set are evaluated.
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.
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 ... some of which are common with other context-sensitive evaluation methods (e.g., ReliefF).
Their list and short description is available in optionCore
(see subsection on ordEval algorithm and attribute evaluation therein).
Evaluation of attributes without specifics of ordered attributes is covered in function attrEval
.
The method returns a list with following components:
reinfPosAV |
a matrix of positive reinforcement for attributes' values, |
reinfNegAV |
a matrix of negative reinforcement for attributes' values, |
anchorAV |
a matrix of anchoring for attributes' values, |
noAV |
a matrix containing count for each value of each attribute, |
reinfPosAttr |
a vector of positive reinforcement for attributes, |
reinfNegAttr |
a matrix of negative reinforcement for attributes, |
anchorAttr |
a matrix of anchoring for attributes, |
noAVattr |
a vector containing count of valid values of each attribute, |
rndReinfPosAV |
a three dimensional array of statistics for random normalizing attributes' positive reinforcement for attributes' values, |
rndReinfPosAV |
a three dimensional array of statistics for random normalizing attributes' negative reinforcement for attributes' values, |
rndAnchorAV |
a three dimensional array of statistics for random normalizing attributes' anchoring for attributes' values, |
rndReinfPosAttr |
a three dimensional array of statistics for random normalizing attributes' positive reinforcement for attributes, |
rndReinfPosAttr |
a three dimensional array of statistics for random normalizing attributes' negative reinforcement for attributes, |
rndAnchorAttr |
a three dimensional array of statistics for random normalizing attributes' anchoring for attributes. |
attrNames |
the names of attributes |
valueNames |
the values of attributes |
noAttr |
number of attributes |
ordVal |
maximal number of attribute values |
variant |
the variant of the algorithm used |
file |
the file to store the results |
rndFile |
the file to store random normalizations |
The statistics used are median, 1st quartile, 3rd quartile, low and high percentile selected by
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.
Marko Robnik-Sikonja
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/
plot.ordEval
,
CORElearn
,
CoreModel
,
optionCore
,
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)