CORElearn (version 0.9.29)

plot.ordEval: Visualization of ordEval results

Description

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.

Usage

plotOrdEval(file, rndFile, ...) 
    
    ## S3 method for class 'ordEval':
plot(x, graphType=c("avBar", "attrBar", "avSlope"), ...)
    
    printOrdEval(x)

Arguments

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.

file

Name of file where evaluation results of ordEval algorithm were written to.

rndFile

Name of file where evaluation of random normalizing attributes by ordEval algorithm were written to.

graphType

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.

Value

• The method returns no value.

Details

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:

• avBarthe 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.
• attrBarthe 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 withavBargraph type.
• avSlopethe 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.

• ciThe type of the confidence interval in "avBar" and "attrBar" graph types. Can be"two.sided","upper","lower", or"none". Together withordEvalNormalizingPercentileparameter inordEval,ci, andciDisplaycontrols the type, length and display of of confidence intervals for each value.
• ciDisplayThe 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.
• graphTitlespecifies text to incorporate into the title.
• attrIdxdisplays plot for a single attribute with specified index.
• xlabellabel of lower horizontal axis.
• ylabLeftlabel of left vertical axis.
• ylabRightlabel of right vertical axis
• bwif set to TRUE produces black and white graph.

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

ordEval, helpCore, preparePlot, CORElearn

Examples

# 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
    ordEval(class ~ ., dat, file="profiles.oe", rndFile="profiles.oer", ordEvalNoRandomNormalizers=200)
    plotOrdEval(file="profiles.oe", rndFile="profiles.oer",graphType="attrBar")