shingle(x, intervals=sort(unique(x)))
equal.count(x, ...)
as.shingle(x)
is.shingle(x)
## S3 method for class 'shingle':
plot(x, col, aspect, \dots)
## S3 method for class 'shingle':
print(x, showValues = TRUE, \dots)
## S3 method for class 'shingleLevel':
print(x, \dots)
## S3 method for class 'shingle':
summary(object, \dots)
## S3 method for class 'shingle':
as.data.frame(x, row.names = NULL, optional = FALSE)
x[subset, drop = FALSE]
as.factorOrShingle(x, subset, drop)plot.shingle, x[]. An object (list of intervals) of class
"shingleLevel" in print.shingleLevelbar.fill$colco.intervalsx$intervals for levels.shingle(x),
logical for is.shingle, an object of class ``trellis'' for
plot (printed by default by print.trellis), and
an object of class ``shingle'' for the others.levels and
nlevels functions, usually applicable to factors, are also
applicable to shingles. There are print methods for shingles, as well as for printing the
result of levels() applied to a shingle.
The implementation of shingles is slightly different from S.
equal.count converts x to a shingle. Essentially a
wrapper around co.intervals. All arguments are passed to
co.intervals
shingle creates a shingle using the given intervals. If
intervels is a vector, these are used to form 0 length
intervals.
as.shingle returns shingle(x) if x is not a
shingle.
is.shingle tests whether x is a shingle.
plot.shingle displays the ranges of shingles via
rectangles. print.shingle and summary.shingle describe
the shingle object.
xyplot,
co.intervals, Latticez <- equal.count(rnorm(50))
plot(z)
print(z)
print(levels(z))
<testonly>data.frame(x = equal.count(rnorm(100)), y = rnorm(100))</testonly>Run the code above in your browser using DataLab