zooreg is the creator for the S3 class
"zoo" series. It inherits from
and is the analogue to
zooreg(data, start = 1, end = numeric(), frequency = 1, deltat = 1, ts.eps = getOption("ts.eps"), order.by = NULL, calendar = getOption("zoo.calendar", TRUE))
a numeric vector, matrix or a factor.
the time of the first observation. Either a single number or a vector of two integers, which specify a natural time unit and a (1-based) number of samples into the time unit.
the time of the last observation, specified in the same way
the number of observations per unit of time.
the fraction of the sampling period between successive
observations; e.g., 1/12 for monthly data. Only one of
time series comparison tolerance. Frequencies are considered
equal if their absolute difference is less than
a vector by which the observations in
Strictly regular series are those whose time points are equally spaced.
Weakly regular series are strictly regular time series in which some
of the points may have been removed but still have the original
underlying frequency associated with them.
"zooreg" is a subclass of
"zoo" that is used to represent both weakly
and strictly regular series. Internally, it is the same as
it also has a
"frequency" attribute. Its index class is more restricted
"zoo". The index: 1. must be numeric or a class which can be coerced
as.numeric (such as
2. when converted to numeric
must be expressible as multiples of 1/frequency. 3.
group generic functions
Ops should be defined, i.e.,
adding/subtracting a numeric to/from the index class should produce the correct
value of the index class again.
zooreg is the
zoo analogue to
ts. The arguments
are almost identical, only in the case where
order.by is specified,
zoo is called with
zoo(data, order.by, frequency). It
creates a regular series of class
"zooreg" which inherits from
It is essentially a
"zoo" series with an additional
attribute. In the creation of
"zooreg" objects (via
zooreg, or coercion functions) it is always check whether the
index specified complies with the frequency specified.
"zooreg" offers two advantages over code
"ts": 1. The
index does not have to be plain numeric (although that is the default), it just
must be coercible to numeric, thus printing and plotting can be customized.
2. This class can not only represent strictly regular series, but also series
with an underlying regularity, i.e., where some observations from a regular grid
"zooreg" is a bridge between
can be employed to coerce back and forth between the two classes. The coercion
as.zoo.ts returns therefore an object of class
"zoo". Coercion between
is also available and drops or tries to add a frequency respectively.
For checking whether a series is strictly regular or does have an underlying
regularity the generic function
is.regular can be used.
Methods to standard generics for regular series such as
cycle are available for both
"zoo" objects. In the latter case, it is checked first (in a data-driven way)
whether the series is in fact regular or not.
as.zooreg.tis has a
class argument whose value represents the
class of the index of the
zooreg object into which the
object is converted. The default value is
"ti". Note that the
frequency of the
zooreg object will not necessarily be the same
as the frequency of the
tis object that it is converted from.
An object of class
"zooreg" which inherits from
It is essentially a
"zoo" series with a
## equivalent specifications of a quarterly series ## starting in the second quarter of 1959. zooreg(1:10, frequency = 4, start = c(1959, 2)) as.zoo(ts(1:10, frequency = 4, start = c(1959, 2))) zoo(1:10, seq(1959.25, 1961.5, by = 0.25), frequency = 4) ## use yearqtr class for indexing the same series z <- zoo(1:10, yearqtr(seq(1959.25, 1961.5, by = 0.25)), frequency = 4) z z[-(3:4)] ## create a regular series with a "Date" index zooreg(1:5, start = as.Date("2000-01-01")) ## or with "yearmon" index zooreg(1:5, end = yearmon(2000)) ## lag and diff (as diff is defined in terms of lag) ## act differently on zoo and zooreg objects! ## lag.zoo moves a point to the adjacent time whereas ## lag.zooreg moves a point by deltat x <- c(1, 2, 3, 6) zz <- zoo(x, x) zr <- as.zooreg(zz) lag(zz, k = -1) lag(zr, k = -1) diff(zz) diff(zr) ## lag.zooreg wihtout and with na.pad lag(zr, k = -1) lag(zr, k = -1, na.pad = TRUE) ## standard methods available for regular series frequency(z) deltat(z) cycle(z) cycle(z[-(3:4)]) zz <- zoo(1:6, as.Date(c("1960-01-29", "1960-02-29", "1960-03-31", "1960-04-29", "1960-05-31", "1960-06-30"))) # this converts zz to "zooreg" and then to "ts" expanding it to a daily # series which is 154 elements long, most with NAs. ## Not run: length(as.ts(zz)) # 154 ## End(Not run) # probably a monthly "ts" series rather than a daily one was wanted. # This variation of the last line gives a result only 6 elements long. length(as.ts(aggregate(zz, as.yearmon, c))) # 6 zzr <- as.zooreg(zz) dd <- as.Date(c("2000-01-01", "2000-02-01", "2000-03-01", "2000-04-01")) zrd <- as.zooreg(zoo(1:4, dd))
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