lag creates a lagged version of a time series, shifting the time base
forward by a given number of observations. Lag does exactly the
opposite, shifting the time base backwards by the given number of
observations. lag and Lag create a single lagged
series, while lags and Lags can create a multivariate
series with several lags at once.
# S3 method for tis
lag(x, k = 1, ...)Lag(x, k = 1, ...)
lags(x, lags, name = "")
Lags(x, lags, name = "")
A vector or matrix or univariate or multivariate time series
(including tis series)
The number of lags. For lag, this is
the number of time periods that the series is shifted forward,
while for Lag it is the number of periods that the series is
shifted backwards.
further arguments to be passed to or from methods
vector of lag numbers. For code lags, each element
gives a number of periods by which x is to be shifted
forward, while for Lags, each element gives a number
of periods by which x is to be shifted backwards.
string or a character vector of names to be used in constructing column names for the returned series
Both functions return a time series (ts or tis) object.
If the lags argument to the lags function argument has
more than one element, the returned object will have a column for each
lag, with NA's filling in where appropriate.
Vector or matrix arguments 'x' are coerced to time series.
For lags, column names are constructed as follows: If
name is supplied and has as many elements as x has
columns, those names are used as the base column names. Otherwise the
column names of x comprise the base column names, or if those
don't exist, the first ncols(x) letters of the alphabet are
used as base names. Each column of the returned series has a name
consisting of the basename plus a suffix indicating the lag number for
that column.