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# Rmetrics 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.
#
# Rmetrics 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
################################################################################
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'
# Create timeSeries Object:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
data = round(rnorm(12), 3)
charvec = timeCalendar(2006)
uTS = timeSeries(data, charvec, units = "uTS")
uTS
checkTrue(inherits(uTS, "timeSeries"))
checkTrue(is.timeSeries(uTS))
# Check Positions:
positions = timeCalendar()
class(positions)
whichFormat(format(positions))
whichFormat(as.character(positions))
# Data Input is a Vector - Returns a timeSeries with dummy positions:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
x = rnorm(12)
# as.numeric - add dummy dates:
data = as.numeric(x)
tS = as.timeSeries(data)
head(tS)
# as. numeric [as.vector] - add dummy dates:
data = as.vector(x)
tS = as.timeSeries(data)
head(tS)
# Data Inpiut is a data.frame:
data(MSFT)
x.df = as.data.frame(MSFT)
head(x.df)
# First Column holds Positions:
tS = MSFT
head(tS)
# Missing Positions - return signal series
# x.df = msft.dat[, -1]
# head(x.df)
# tS = as.timeSeries(x.df)
# head(tS)
# Data Input is a Matrix:
data(MSFT)
x.mat = as.matrix(MSFT)
# tS = as.timeSeries(x.mat)
# head(tS) # CHECK
# Data Input is an Univariate/Muiltivariate timeSeries:
x = MSFT
class(x)
tS = as.timeSeries(x)
head(tS)
# Note, data is a demo file ...
tS = MSFT
head(tS)
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.asTimeSeriesDJ1 =
function()
{
# Load Data:
# 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()
{
if (FALSE) {
# DW has to be fixed ...
# 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:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
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) # Not implemented !
# head(VEC)
# class(VEC)
# checkIdentical(class(VEC), "numeric")
# Matrix:
MAT = as.matrix(uTS)
head(MAT)
class(MAT)
checkIdentical(class(MAT), "matrix")
# Data Frame:
DF = as.data.frame(uTS)
head(DF)
checkIdentical(class(DF), "data.frame")
# Time Series:
TS = as.ts(uTS)
head(TS)
class(TS)
checkIdentical(class(TS), "ts")
}
# Return Value:
return()
}
# ------------------------------------------------------------------------------
test.fromTimeSeriesMV =
function()
{
if (FALSE) {
# DW has to be fixed ...
# 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'
# Multivariate Case:
RNGkind(kind = "Marsaglia-Multicarry", normal.kind = "Inversion")
set.seed(4711, kind = "Marsaglia-Multicarry")
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()
}
################################################################################
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