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# This library is free software; you can redistribute it and/or
# modify it under the terms of the GNU Library General Public
# License as published by the Free Software Foundation; either
# version 2 of the License, or (at your option) any later version.
#
# This library is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Library General Public License for more details.
#
# You should have received a copy of the GNU Library General
# Public License along with this library; if not, write to the
# Free Foundation, Inc., 59 Temple Place, Suite 330, Boston,
# MA 02111-1307 USA
# Copyrights (C)
# for this R-port:
# 1999 - 2004, Diethelm Wuertz, GPL
# Diethelm Wuertz <wuertz@itp.phys.ethz.ch>
# info@rmetrics.org
# www.rmetrics.org
# for the code accessed (or partly included) from other R-ports:
# see R's copyright and license files
# for the code accessed (or partly included) from contributed R-ports
# and other sources
# see Rmetrics's copyright file
################################################################################
# METHODS: CREATE A TIMESERIES FROM OTHER OBJECTS:
# is.timeSeries S3: Tests for a 'timeSeries' object
# as.timeSeries S3: Defines method for a 'timeSeries' object
# as.timeS*.default S3: Returns the input
# as.timeS*.numeric S3: Transforms a numeric vector into a 'timeSeries'
# as.timeS*.data.frame S3: Transformas a 'data.frame' into a 'timeSeries'
# as.timeS*.matrix S3: Transformas a 'matrix' into a 'timeSeries'
# as.timeS*.ts S3: Transforms a 'ts' object into a 'timeSeries'
# as.timeS*.character S3: Loads and transformas from a demo file
# as.timeS*.zoo S3: Transforms a 'zoo' object into a 'timeSeries'
# METHODS: TRANSFORM A TIMESERIES INTO OTHER OBJECTS:
# as.vector.timeS* S3: Converts a univariate 'timeSeries' to a vector
# as.matrix.timeS* S3: Converts a 'timeSeries' to a 'matrix'
# as.data.frame.t* S3: Converts a 'timeSeries' to a 'data.frame'
# as.ts.timeSeries S3: Converts a 'timeSeries' to a 'ts'
# NEW METHODS
# .as.vector.zoo
# .as.matrix.zoo
# .quantile.zoo
# .t.timeSeries
# .mergeSeries
################################################################################
test.helpFile =
function()
{
# Help File:
helpFile = function() {
example(timeSeriesCoercion); return() }
checkIdentical(
target = class(try(helpFile())),
current = "NULL")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.asTimeSeries =
function()
{
# as.timeSeries.default Returns the input
# as.timeSeries.numeric Transforms a numeric vector into a 'timeSeries'
# as.timeSeries.data.frame Transformas a 'data.frame' into a 'timeSeries'
# as.timeSeries.matrix Trasformas a 'matrix' into a 'timeSeries'
# as.timeSeries.ts Tranf orms a 'ts' object into a 'timeSeries'
# as.timeSeries.character Loads and transformas from a demo file
# as.timeSeries.zoo Transforms a 'zoo' object into a 'timeSeries'
set.seed(4711)
data = round(rnorm(12), 3)
charvec = timeCalendar(2006)
uTS = timeSeries(data, charvec, units = "uTS")
uTS
checkTrue(inherits(uTS, "timeSeries"))
checkTrue(is.timeSeries(uTS))
positions = timeCalendar()
class(positions)
.whichFormat(format(positions))
.whichFormat(as.character(positions))
# Data Input is a Vector:
x = rnorm(12)
positions = timeCalendar()
tS = as.timeSeries(x, positions)
print(tS)
tS = as.timeSeries(x, format(positions))
print(tS)
tS = as.timeSeries(x, as.character(positions))
print(tS)
tS = timeSeries(x, positions)
print(tS)
tS = timeSeries(x, format(positions))
print(tS)
tS = timeSeries(x, as.character(positions))
print(tS)
# Data Input is a Data Frame:
x = data.frame(rnorm(12))
tS = as.timeSeries(x, positions)
print(tS)
tS = as.timeSeries(x, format(positions))
print(tS)
tS = as.timeSeries(x, as.character(positions))
print(tS)
tS = timeSeries(x, positions)
print(tS)
tS = timeSeries(x, format(positions))
print(tS)
tS = timeSeries(x, as.character(positions))
print(tS)
# Data Input is a Matrix:
x = matrix(rnorm(12))
tS = as.timeSeries(x, positions)
print(tS)
tS = as.timeSeries(x, format(positions))
print(tS)
tS = as.timeSeries(x, as.character(positions))
print(tS)
tS = timeSeries(x, positions)
print(tS)
tS = timeSeries(x, format(positions))
print(tS)
tS = timeSeries(x, as.character(positions))
print(tS)
# Data Input is an Univariate timeSeries:
x = as.ts(rnorm(12))
tS = as.timeSeries(x, positions)
print(tS)
tS = as.timeSeries(x, format(positions))
print(tS)
tS = as.timeSeries(x, as.character(positions))
print(tS)
tS = timeSeries(x, positions)
print(tS)
tS = timeSeries(x, format(positions))
print(tS)
tS = timeSeries(x, as.character(positions))
print(tS)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.asTimeSeriesDJ1 =
function()
{
# Load Data:
# require(rmetrics)
# data(DowJones30)
# use instead dummy data set just for testing ...
Data = matrix(exp(cumsum(rnorm(30*100, sd = 0.1))), ncol = 30)
Positions = format(timeSequence("2006-01-01", length.out = 100))
DowJones30 = data.frame(Positions, Data)
# Taking Dates from First Column:
DJ = DowJones30[21:30, c(1, 11:15)]
DJ
class(DJ)
as.timeSeries(DJ)
# Adding Dates through Rownames Assignment:
DJ = DowJones30[21:30, c(11:15)]
rownames(DJ)<-DowJones30[21:30, 1]
DJ
as.timeSeries(DJ)
# Missing Dates - Using Dummy Dates:
DJ = DowJones30[21:30, c(11:15)]
DJ
class(DJ)
as.timeSeries(DJ)
# With recordIDs:
if (FALSE) {
DJ = DowJones30[21:30, c(1,11:15)]
DJ = cbind(DJ, LETTERS[1:10])
class(DJ)
tsDJ = as.timeSeries(DJ)
tsDJ
tsDJ@recordIDs
}
DJ = DowJones30[21:30, c(11:15)]
rownames(DJ) = DowJones30[21:30, 1]
DJ = cbind(DJ, LETTERS[1:10])
tsDJ = as.timeSeries(DJ)
tsDJ
tsDJ@recordIDs
DJ = DowJones30[21:30, c(11:15)]
DJ =cbind(DJ, LETTERS[1:10])
tsDJ = as.timeSeries(DJ)
tsDJ
tsDJ@recordIDs
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.fromTimeSeriesUV =
function()
{
# as.vector.timeSeries Converts a univariate 'timeSeries' to a vector
# as.matrix.timeSeries Converts a 'timeSeries' to a 'matrix'
# as.data.frame.timeSeries Converts a 'timeSeries' to a 'data.frame'
# as.ts.timeSeries Converts a 'timeSeries' to a 'ts'
# Univariate Case:
set.seed(4711)
data = round(rnorm(12), 3)
charvec = timeCalendar(2006)
uTS = timeSeries(data, charvec, units = "uTS")
uTS
# Vector:
VEC = as.vector(uTS)
head(VEC)
class(VEC)
checkIdentical(class(VEC), "numeric")
# Numeric:
VEC = as.numeric(uTS)
head(VEC)
class(VEC)
checkIdentical(class(VEC), "numeric")
# Matrix:
MAT = as.matrix(uTS)
head(MAT)
class(MAT)
checkIdentical(class(MAT), "matrix")
checkIdentical(target = MAT[,1], current = VEC)
# Data Frame:
DF = as.data.frame(uTS)
head(DF)
checkIdentical(class(DF), "data.frame")
checkIdentical(target = as.matrix(DF)[,1], current = VEC)
# Time Series:
TS = as.ts(uTS)
head(TS)
class(TS)
checkIdentical(class(TS), "ts")
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.fromTimeSeriesMV =
function()
{
# as.vector.timeS* Converts a univariate 'timeSeries' to a vector
# as.matrix.timeS* Converts a 'timeSeries' to a 'matrix'
# as.data.frame.t* Converts a 'timeSeries' to a 'data.frame'
# as.ts.timeSeries Converts a 'timeSeries' to a 'ts'
# Multivariate Case:
set.seed(4711)
data = matrix(round(rnorm(24), 3), ncol = 2)
charvec = timeCalendar(2006)
mTS = timeSeries(data, charvec)
mTS
# Matrix:
MAT = as.matrix(mTS)
head(MAT)
class(MAT)
checkIdentical(
target = class(MAT),
current = "matrix")
checkIdentical(
target = as.vector(MAT[, 1]),
current = as.numeric(MAT)[1:12])
# Data Frame:
DF = as.data.frame(mTS)
head(DF)
class(DF)
checkIdentical(
target = class(DF),
current = "data.frame")
# Time Series:
TS = as.ts(mTS)
head(TS)
class(TS)
checkIdentical(
target = class(TS),
current = c("mts", "ts"))
# Return Value:
return()
}
# ------------------------------------------------------------------------------
if (FALSE) {
require(RUnit)
testResult <- runTestFile("C:/Rmetrics/SVN/trunk/fCalendar/test/runit4D.R")
printTextProtocol(testResult)
}
################################################################################
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