is.significant  R Documentation 
Provides the generic functions is.significant()
and the method to
find significant rules.
is.significant(x, ...)
## S4 method for signature 'rules'
is.significant(
x,
transactions = NULL,
method = "fisher",
alpha = 0.01,
adjust = "none",
reuse = TRUE,
...
)
x 
a set of rules. 
... 
further arguments are passed on to 
transactions 
optional set of transactions. Only needed if not sufficient
interest measures are available in 
method 
test to use. Options are 
alpha 
required significance level. 
adjust 
method to adjust for multiple comparisons. Some options are

reuse 
logical indicating if information in the quality slot should be reuse for calculating the measures. 
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 pvalue less then the specified significance level alpha (the null hypothesis of independence is rejected). See Hahsler and Hornik (2007) for details.
returns a logical vector indicating which rules are significant.
Michael Hahsler
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/IDA200711502")}
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()
,
associationsclass
,
c()
,
duplicated()
,
extract
,
inspect()
,
is.closed()
,
is.generator()
,
is.maximal()
,
is.redundant()
,
is.superset()
,
itemsetsclass
,
match()
,
rulesclass
,
sample()
,
sets
,
size()
,
sort()
,
unique()
data("Income")
rules < apriori(Income, support = 0.2)
is.significant(rules)
rules[is.significant(rules)]
# Adjust Pvalues for multiple comparisons
rules[is.significant(rules, adjust = "bonferroni")]
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