is.significant: Find Significant Rules

In arules: Mining Association Rules and Frequent Itemsets

Find Significant Rules

Description

Provides the generic functions is.significant() and the method to find significant rules.

Usage

is.significant(x, ...)

## S4 method for signature 'rules'
is.significant(
  x,
  transactions = NULL,
  method = "fisher",
  alpha = 0.01,
  adjust = "none",
  reuse = TRUE,
  ...
)

Arguments

x	a set of rules.
...	further arguments are passed on to interestMeasure().
transactions	optional set of transactions. Only needed if not sufficient interest measures are available in x. If the test should be performed on a transaction set different then the one used for mining (use reuse = FALSE).
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.
adjust	method to adjust for multiple comparisons. Some options are "none", "bonferroni", "holm", "fdr", etc. (see stats::p.adjust() for more methods)
reuse	logical indicating if information in the quality slot should be reuse for calculating the measures.

Details

The implementation for association rules uses Fisher's exact test with correction for multiple comparisons to test the null hypothesis that the LHS and the RHS of the rule are independent. Significant rules have a p-value less then the specified significance level alpha (the null hypothesis of independence is rejected). See Hahsler and Hornik (2007) for details.

Value

returns a logical vector indicating which rules are significant.

Author(s)

Michael Hahsler

References

Hahsler, Michael and Kurt Hornik (2007). New probabilistic interest measures for association rules. Intelligent Data Analysis, 11(5):437–455. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.3233/IDA-2007-11502")}

See Also

stats::p.adjust()

Other interest measures: confint(), coverage(), interestMeasure(), is.redundant(), support()

Other postprocessing: is.closed(), is.generator(), is.maximal(), is.redundant(), is.superset()

Other associations functions: abbreviate(), associations-class, c(), duplicated(), extract, inspect(), is.closed(), is.generator(), is.maximal(), is.redundant(), is.superset(), itemsets-class, match(), rules-class, sample(), sets, size(), sort(), unique()

Examples

data("Income")
rules <- apriori(Income, support = 0.2)
is.significant(rules)

rules[is.significant(rules)]

# Adjust P-values for multiple comparisons
rules[is.significant(rules, adjust = "bonferroni")]

arules documentation built on Sept. 11, 2024, 8:15 p.m.