apply | R Documentation |
Applies a function to a "timeSeries"
object over regular or
irregular time windows, possibly overlapping.
## S4 method for signature 'timeSeries'
apply(X, MARGIN, FUN, ..., simplify = TRUE)
fapply(x, from, to, FUN, ...)
applySeries(x, from = NULL, to = NULL, by = c("monthly", "quarterly"),
FUN = colMeans, units = NULL, format = x@format,
zone = x@FinCenter, FinCenter = x@FinCenter,
recordIDs = data.frame(), title = x@title,
documentation = x@documentation, ...)
rollDailySeries(x, period = "7d", FUN, ...)
x , X |
an object of class |
MARGIN |
a vector giving the subscripts which the function will be
applied over, see base R's |
FUN |
the function to be applied. For the function |
simplify |
simplify the result? |
from , to |
starting date and end date as |
by |
a character value either |
units |
an optional character string, which allows to overwrite the current
column names of a |
format |
the format specification of the input character vector in POSIX notation. |
zone |
the time zone or financial center where the data were recorded. |
FinCenter |
a character value with the the location of the financial center named as "continent/city", or "city". |
recordIDs |
a data frame which can be used for record identification information. Note, this is not yet handled by the apply functions, an empty data.frame will be returned. |
title |
an optional title string, if not specified the input's data name is deparsed. |
documentation |
optional documentation string, or a vector of character strings. |
period |
a character string specifying the rollling period composed by the
length of the period and its unit, e.g. |
... |
arguments passed to other methods. |
The "timeSeries"
method for apply
extracts the core data
(a matrix) from X
and calls apply
, passing on all the
remaining arguments. If the result is suitable, it converts it to
"timeSeries"
, otherwise returns it as is. ‘Suitable’
here means that it is a matrix or a vector (which is converted to a
matrix) and the number of observations is the same as X
.
Like apply
applies a function to the margins of an array, the
function fapply
applies a function to the time stamps or signal
counts of a financial (therefore the “f” in front of the
function name) time series of class "timeSeries"
.
applySeries
takes a "timeSeries"
object as input and
applies FUN
to windows of x
. The windows are specified
by from
and to
, which need to have the same length. Then
from[i], to[i]
specifies the i
-th window. If
time(x)
is a "timeDate"
object, then from
and
to
are converted to "timeDate"
(if they are not already
such objects), otherwise they are converted to integers.
An alternative way to specify the window(s) on which
applySeries
operates is with argument by
. It is used
only if from
and to
are missing or NULL
. by
= "monthly"
or by = "quarterly"
applies FUN
to the data
for each year-month or year-quarter, respectively. By year-month we
mean that there are separate windows for the months in different
years.
The resulting time stamps are the time stamps of the to
vector. The periods can be regular or irregular, and they can even
overlap.
If from = start(x)
and to = end(x)
, then the function
behaves like apply
on the column margin.
fapply
is the same as applySeries
(in fact, the former
calls the latter), except that the defaults for from
and
to
are start(x)
and end(x)
, respectively. (GNB:
in addition, fapply
throws error if x
is a
‘signal series’.)
rollDailySeries
rolls a daily 'timeSeries' on a given period.
for rollDailySeries
, an object of class "timeSeries"
with
rolling values, computed from the function FUN
.
## Percentual Returns of Swiss Bond Index and Performance Index -
LPP <- 100 * LPP2005REC[, c("SBI", "SPI")]
head(LPP, 20)
## Aggregate Quarterly Returns -
applySeries(LPP, by = "quarterly", FUN = colSums)
## Aggregate Quarterly every last Friday in Quarter -
oneDay <- 24*3600
from <- unique(timeFirstDayInQuarter(time(LPP))) - oneDay
from <- timeLastNdayInMonth(from, nday = 5)
to <- unique(timeLastDayInQuarter(time(LPP)))
to <- timeLastNdayInMonth(to, nday = 5)
data.frame(from = as.character(from), to = as.character(to))
applySeries(LPP, from, to, FUN = colSums)
## Alternative Use -
fapply(LPP, from, to, FUN = colSums)
## Count Trading Days per Month -
colCounts <- function(x) rep(NROW(x), times = NCOL(x))
applySeries(LPP, FUN = colCounts, by = "monthly")
## TODO: examples for rollDailySeries()
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