CORElearn (version 1.54.2)

ordDataGen: Artificial data for testing ordEval algorithms

Description

The generator produces ordinal data simulating different profiles of attributes: basic, performance, excitement and irrelevant.

Usage

ordDataGen(noInst, classNoise=0)

Arguments

noInst

Number of instances to generate.

classNoise

Proportion of randomly determined values in the class variable.

Value

The method returns a data.frame with noInst rows and 9 columns. Range of values of the attributes and class are integers in [1,5]

Details

Problem is described by six important and two irrelevant features. The important features correspond to different feature types from the marketing theory: two basic features (\(B_{weak}\) and \(B_{strong}\)), two performance features (\(P_{weak}\) and \(P_{strong}\)), two excitement features (\(E_{weak}\) and \(E_{strong}\)), and two irrelevant features (\(I_{uniform}\) and \(I_{normal}\)). The values of all features are randomly generated integer values from 1 to 5, indicating for example score assigned to each of the features by the survey's respondent. The dependent variable for each instance (class) is the sum of its features' effects, which we scale to the uniform distribution of integers 1-5, indicating, for example, an overall score assigned by the respondent. $$% C=b_w(B_{weak})+b_s(B_{strong})+p_w(P_{weak})+p_s(P_{strong})+e_w(E_{weak})+e_s(E_{strong})% $$

See Also

classDataGen, regDataGen, ordEval,

Examples

Run this code
# NOT RUN {
#prepare a data set
dat <- ordDataGen(200)

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

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