rts(x = NA, start = 1, deltat = 1, frequency = 1, end = <<see below>>,
units = <<see below>>, names = NULL, eps = .Options$ts.eps)
is.rts(x)
as.rts(x)
c(1990,2). If both
start and
end are
missing, then the default for
start is 1.
deltat=1/12 and bienial data has
deltat=2.
Only one of
frequency or
deltat should be provided.
Default is 1.
12 for monthly data
in a year.
frequency is the reciprocal of
deltat.
Only one of
deltat or
frequency should be provided.
Default is 1.
start and
end are given, they must be consistent with
the length of the time series.
deltat. Default is
NULL.
x if
x is a matrix or
a data frame, or the strings
"Series 1",
"Series 2", ..., etc. if
dimnames(x) = NULL.
In the case of data frames,
names overrides existing column names.
deltat and
frequency are integer values. Default is
.Options$ts.eps.
rts function returns a time series object of class
"rts" whose
data values are given by
x.
is.rts function returns
TRUE if
x is of class
"rts" and
FALSE
otherwise.
as.rts function coerces calendar time series (objects of class
"cts")
and
ts objects (time series objects from version 3.1 or earlier with a
tsp
attribute) to regular time series object of class
"rts". If
x
is neither, then
as.rts(x) returns
rts(x).
The
rts function checks for consistency in its arguments.
Arrays with more than two dimensions are treated as vectors.
The
as.rts function converts an old-style time series to a time
series of class
"rts".
Any data measured at regular time intervals can be represented as a regular
time series. Hourly data might use an integer representing days
for
start and
end with
frequency=24
or
deltat=1/24; daily data could use weeks with
frequency=7
or
deltat=1/7.
Time series objects are those that have a
tspar attribute. For regular time
series, the
tspar attribute is an ordered vector with three labeled
components
start,
deltat, and
frequency. The
tspar attribute may
optionally have a
units attribute giving the units in which
deltat
is measured.
Factors and data frames can be redefined as time series.
Beware that most time series operations are not well defined for factor data.
x <- rts(rnorm(100), start = c(1953, 4), frequency = 12)
is.rts(x)
lynx.rts <- as.rts(lynx)
corn.rts <- rts(cbind(corn.rain, corn.yield), start = 1890,
units = "years", names = c("rain", "yield"))