Description Usage Arguments Value Examples
The function offers a method to select variables by univariate filtering based on the estimated loss of the univariate Bayesian Classifer. The statistic requires the parametric assumption that the variable consists of a mixture of Gaussian variables.
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class |
a factor vector indicating the class membership of the instances. Must have exactly two levels. |
data |
a data frame with the variables to filer in columns. |
oc |
a vector containing three elements. oc[1], the cost of misclassifying a negative instance, oc[2], the cost of missclassifying a positive instance, and oc[3], the share of negative instances in the population. |
positive |
a character object indicating the factor label of the positive class. |
robust |
a logical indicating whether a robust estimator of the mean and variance of the two classes should be used. |
p.val |
a logical indicating whether the p-values of ebc values under the null hypothesis that both classes are equal should be calculated. Currently the null distribution is calculated by permutation. |
adj.method |
a character string indicating the method with which to correct the p-values for multiple testing. See ?p.adjust. |
a list containing three components:
ebc |
a numerical vector containing the etc values for every variable of dat. |
p.val |
the corresponding p-values of etc. (optional) |
p.val.adj |
the corresponding adjusted p-values of ebc. (optional) |
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