ts(data = NA, start = 1, end = numeric(0), frequency = 1,
deltat = 1, ts.eps = .Options$ts.eps, class, names)
as.ts(x)
is.ts(x)
print(ts.obj, calendar, ...)
plot(ts.obj, plot.type=c("multiple", "single"), ...)
lines(ts.obj, ...)
The function ts is used to create time-series objects. These
are vector or matrices with class of "ts" (and additional
attributes) which represent data which has been sampled at equispaced
points in time. In the matrix case, each column of the matrix
data is assumed to contain a single (univariate) time series.
as.ts and is.ts coerce an object to a
time-series and test whether an object is a time series.
Class "ts" has a number of methods. In particular arithmetic
will attempt to align time axes, and subsetting to extract
subsets of series can be used (e.g. EuStockMarkets[, "DAX"]).
However, subsetting the first (or only) dimension will return a
matrix or vector, as will matrix subsetting.
The value of argument frequency is used when the series is
sampled an integral number of times in each unit time interval. For
example, one could use a value of 7 for frequency when
the data are sampled daily, and the natural time period is a week, or
12 when the data are sampled monthly and the natural time
period is a year. Values of 4 and 12 are assumed in (e.g.)
print methods to imply a quarterly and monthly series respectively.
as.ts will use the tsp attribute of the object if
it has one to set the start and end times and frequency.
tsp,
frequency,
start,
end,
time,
window.
ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959
print( ts(1:10, freq = 7, start = c(12, 2)), calendar = TRUE) # print.ts(.)
## Using July 1954 as start date:
gnp <- ts(cumsum(1 + round(rnorm(100), 2)),
start = c(1954, 7), frequency = 12)
plot(gnp) # using `plot.ts' for time-series plot
## Multivariate
z <- ts(matrix(rnorm(300),100,3), start=c(1961,1), frequency=12)
plot(z)
plot(z, plot.type="single", lty=1:3)
ts(1:10, frequency = 4, start = c(1959, 2)) # 2nd Quarter of 1959
print( ts(1:10, freq = 7, start = c(12, 2)), calendar = TRUE) # print.ts(.)
## Using July 1954 as start date:
gnp <- ts(cumsum(1 + round(rnorm(100), 2)),
start = c(1954, 7), frequency = 12)
plot(gnp) # using `plot.ts' for time-series plot
## Multivariate
z <- ts(matrix(rnorm(300),100,3), start=c(1961,1), frequency=12)
plot(z)
plot(z, plot.type="single", lty=1:3)
## A phase plot:
data(nhtemp)
plot(nhtemp, c(nhtemp[-1],NA), cex = .8, col="blue",
main="Lag plot of New Haven temperatures")
## a clearer way to do this would be
library(ts)
plot(nhtemp, lag(nhtemp,1), cex = .8, col="blue",
main="Lag plot of New Haven temperatures")
## A phase plot:
data(nhtemp)
plot(nhtemp, c(nhtemp[-1],NA), cex = .8, col="blue",
main="Lag plot of New Haven temperatures")
## a clearer way to do this would be
library(ts)
plot(nhtemp, lag(nhtemp,1), cex = .8, col="blue",
main="Lag plot of New Haven temperatures")