ordEval {CORElearn}

Evaluation of ordered attributes

Description

The method evaluates the quality of ordered attributes specified by the formula with ordEval algorithm.

Usage

ordEval(formula, data, file=NULL, rndFile=NULL, 
        variant=c("allNear","attrDist1","classDist1"), ...)

Arguments

formula

Either a formula specifying the attributes to be evaluated and the target variable, or a name of target variable, or an index of 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 specific to ordEval or common to other context-sensitive evaluation methods (e.g., ReliefF).

Details

The parameter 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:

an object of class formula

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: class ~ .. In this case all other attributes in the data set are evaluated. Note that formula interface is not appropriate for data sets with large number of variables.

a character vector

specifying the name of target variable, all the other columns in data frame data are used as predictors.

an integer

specifying the index of of target variable in data frame data, all the other columns are used as predictors.

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:

ordEvalNoRandomNormalizers

type: integer, default value: 0, value range: 0, Inf,
number of randomly shuffled attributes for normalization of each attribute (0=no normalization). This parameter should be set to a reasonably high value (e.g., 200) in order to produce reliable confidence intervals with plot.ordEval. The parameters ordEvalBootstrapNormalize and ordEvalNormalizingPercentile only make sense if this parameter is larger than 0.

ordEvalBootstrapNormalize

type: logical, default value: FALSE
are features used for normalization constructed with bootstrap sampling or random permutation.

ordEvalNormalizingPercentile

type: numeric, default value: 0.025, value range: 0, 0.5
percentile defines the length of confidence interval obtained with random normalization. Percentile t forms interval by taking the n\cdot t and n(1-t) random evaluation as the confidence interval boundaries, thereby forming 100(1-2t)% confidence interval (t=0.025 gives 95% confidence interval). The value n is set by ordEvalNoRandomNormalizers parameter.

attrWeights

type: character,
a character vector representing a list of attribute weights in the ordEval distance measure.

Evaluation of attributes without specifics of ordered attributes is covered in function attrEval.

Value

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.

Author(s)

Marko Robnik-Sikonja

References

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/

See Also

plot.ordEval, CORElearn, CoreModel, helpCore, infoCore.

Examples

#prepare a data set
dat <- ordDataGen(200)

# evaluate ordered features with ordEval
est <- ordEval(class ~ ., dat, ordEvalNoRandomNormalizers=100)
# print(est)
printOrdEval(est)  
plot(est)