subset extracts a subset of a collection of sequences or sequence
rules which meet conditions specified with respect to their associated
(or derived) quality measures, additional information, or patterns of
items or itemsets.
[ extracts subsets from a collection of (timed) sequences or
unique extracts the unique set of sequences or sequence rules
from a collection of sequences or sequence rules.
lhs, rhs extract the left-hand (antecedent) or right-hand side
(consequent) sequences from a collection of sequence rules.
# S4 method for sequences subset(x, subset)
# S4 method for sequencerules subset(x, subset)
# S4 method for sequences [(x, i, j, ..., reduce = FALSE, drop = FALSE)
# S4 method for timedsequences [(x, i, j, k, ..., reduce = FALSE, drop = FALSE)
# S4 method for sequencerules [(x, i, j, ..., drop = FALSE)
# S4 method for sequences unique(x, incomparables = FALSE)
# S4 method for sequencerules unique(x, incomparables = FALSE)
# S4 method for sequencerules lhs(x)
# S4 method for sequencerules rhs(x)
- an object.
- an expression specifying the conditions where the columns
in quality and info must be referenced by their names, and the object
- a vector specifying the subset of elements to be extracted.
- a vector specifying the subset of event times to be extracted.
- a logical value specifying if the reference set of distinct itemsets should be reduced if possible.
- j, …, drop
- unused arguments (for compatibility with package Matrix only).
- not used.
unique returns an object of the
same class as
In package arules, somewhat confusingly, the object itself has
to be referenced as
items. We do not provide this, as well as
any of the references
After extraction the reference set of distinct itemsets may be larger than the set actually referred to unless reduction to this set is explicitly requested. However, this may increase memory consumption.
Event time indexes of mode character are matched against the time labels. Any duplicate indexes are ignored and their order does not matter, i.e. reordering of a sequence is not possible.
rhs impute the support of
a sequence from the support and confidence of a rule. This may
lead to numerically inaccuracies over back-to-back derivations.
## continue example example(ruleInduction, package = "arulesSequences") ## matching a pattern as(subset(s2, size(x) > 1), "data.frame") as(subset(s2, x %ain% c("B", "F")), "data.frame") ## as well as a measure as(subset(s2, x %ain% c("B", "F") & support == 1), "data.frame") ## matching a pattern in the left-hand side as(subset(r2, lhs(x) %ain% c("B", "F")), "data.frame") ## matching a derived measure as(subset(r2, coverage(x) == 1), "data.frame") ## reduce s <- s2[11, reduce = TRUE] itemLabels(s) itemLabels(s2) ## drop initial events z <- as(zaki, "timedsequences") summary(z[1,,-1])