# is.significant

0th

Percentile

##### Find Significant Rules

Provides the generic functions and the S4 method is.significant to find rules where the LHS and the RHS depend on each other. This uses Fisher's exact test and corrects for multiple comparisons.

Keywords
manip
##### Usage
is.significant(x, transactions, method = "fisher",
alpha = 0.01, adjust = "bonferroni")
##### Arguments
x

a set of rules.

transactions

set of transactions used to mine the rules.

method

test to use. Options are "fisher", "chisq". Note that the contingency table is likely to have cells with low expected values and that thus Fisher's Exact Test might be more appropriate than the chi-squared test.

alpha

required significance level.

method to adjust for multiple comparisons. Options are "none", "bonferroni", "holm", "fdr", etc. (see p.adjust)

##### Value

returns a logical vector indicating which rules are significant.

interestMeasure, p.adjust

##### Aliases
• is.significant
• is.significant,rules-method
##### Examples
# NOT RUN {
data("Income")
rules <- apriori(Income, parameter = list(support = 0.5))
is.significant(rules, Income)

inspect(rules[is.significant(rules, Income)])
# }

Documentation reproduced from package arules, version 1.5-4, License: GPL-3

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