timeSeries/0000755000176000001440000000000012633467257012432 5ustar ripleyuserstimeSeries/inst/0000755000176000001440000000000012620124746013374 5ustar ripleyuserstimeSeries/inst/COPYING0000644000176000001440000004310712620124746014434 0ustar ripleyusers GNU GENERAL PUBLIC LICENSE Version 2, June 1991 Copyright (C) 1989, 1991 Free Software Foundation, Inc. 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Everyone is permitted to copy and distribute verbatim copies of this license document, but changing it is not allowed. Preamble The licenses for most software are designed to take away your freedom to share and change it. By contrast, the GNU General Public License is intended to guarantee your freedom to share and change free software--to make sure the software is free for all its users. This General Public License applies to most of the Free Software Foundation's software and to any other program whose authors commit to using it. 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If the program is interactive, make it output a short notice like this when it starts in an interactive mode: Gnomovision version 69, Copyright (C) year name of author Gnomovision comes with ABSOLUTELY NO WARRANTY; for details type `show w'. This is free software, and you are welcome to redistribute it under certain conditions; type `show c' for details. The hypothetical commands `show w' and `show c' should show the appropriate parts of the General Public License. Of course, the commands you use may be called something other than `show w' and `show c'; they could even be mouse-clicks or menu items--whatever suits your program. You should also get your employer (if you work as a programmer) or your school, if any, to sign a "copyright disclaimer" for the program, if necessary. Here is a sample; alter the names: Yoyodyne, Inc., hereby disclaims all copyright interest in the program `Gnomovision' (which makes passes at compilers) written by James Hacker. , 1 April 1989 Ty Coon, President of Vice This General Public License does not permit incorporating your program into proprietary programs. If your program is a subroutine library, you may consider it more useful to permit linking proprietary applications with the library. If this is what you want to do, use the GNU Library General Public License instead of this License.timeSeries/inst/extensionsTests/0000755000176000001440000000000012620124746016616 5ustar ripleyuserstimeSeries/inst/extensionsTests/attributesExtension.R0000644000176000001440000002312012620124746023022 0ustar ripleyusers # Enhancing the Documentation Slot # Series: # @.Data # @ positions # @ format # @ FinCenter # @ units # @ recordIDs # @ title # @ documentation # attributes(@documentation, "Attributes") # inspect the Information use # slotNames(object) # slot(object, name) # Load Library: require(timeSeries) ############################################################################### # Data obj1 <- timeSeries(rnorm(12), timeCalendar()) getAttributes(obj1) setAttributes(obj1) <- list(series=series(obj1)[1:6, , drop=FALSE]) getAttributes(obj1) obj2 <- timeSeries(rnorm(12), timeCalendar()) getAttributes(obj2) setAttributes(obj2) <- list(series=as.matrix(obj2)[7:12, , drop=FALSE]) getAttributes(obj2) ############################################################################### # Base Functions: # base-apply.R getAttributes( apply(obj1, 1, mean) ) # ok # base-applySeries.R # should be deprecated, use generic apply() and aggregate() functions # base-cbind.R cbind(obj1, obj2) getAttributes( cbind(obj1, obj2) ) # ok getAttributes( rbind(obj1, obj2) ) # ok # ... more base-cbind.R # cbind ... getAttributes(cbind(obj1, obj2)) # ok getAttributes(cbind(obj1, as.matrix(obj2))) # ok getAttributes(cbind(as.matrix(obj1), obj2)) # ok getAttributes(cbind(obj1)) # ok # rbind ... getAttributes(rbind(obj1, obj2)) # ok getAttributes(rbind(obj1, as.matrix(obj2))) # ok getAttributes(rbind(as.matrix(obj1), obj2)) # ok getAttributes(rbind(obj1)) # ok # base-diff.R getAttributes( diff(obj1) ) # ok # base-merge.R getAttributes( merge(obj1, obj2) ) # ok # base-rank.R getAttributes( rank(obj1) ) # ok # base-rev.R getAttributes( rev(obj1) ) # ok # base-sample.R getAttributes( sample(obj1) ) # ok # base-scale.R getAttributes( scale(obj1) ) # ok # base-sort.R getAttributes( sort(obj1) ) # ok ################################################################################ # Subsetting: # base-subsetting.R # .subset_timeSeries # .findIndex # $,timeSeries Subsets a time series by column names # $<-,timeSeries Replaces subset by column names # [,timeSeries Subsets a time series object # [<-,timeSeries Assigns value to subsets of a time series # Should work by dafault ... getAttributes( obj1[3:4, 1] ) # ok getAttributes( head(obj1) ) # ok getAttributes( tail(obj1) ) # ok ################################################################################ # Methods: # methods-mathOps.R # here the multiplications "*", works also with "+", "=", "/". ... getAttributes( obj1 * 2) # ok getAttributes( obj1 * (1:12) ) # ok getAttributes( obj1 * matrix(1:12, ncol=1) ) # ok getAttributes( obj1 * as.ts(1:12) ) # ok getAttributes( obj1 * obj2 ) # ok ??? getAttributes( 2 * obj2 ) # ok getAttributes( (1:12) * obj2 ) # ok getAttributes( matrix(1:12, ncol=1) * obj2) # ok getAttributes( as.ts(1:12) * obj2) # ok getAttributes( obj2 * obj1) # ok ??? # More Math Functions getAttributes( abs(obj1) ) # ok getAttributes( exp(obj1) ) # ok getAttributes( obj1^2 ) # ok # ... # Round and Truncate: getAttributes( round(obj1, digits=2) ) # ok getAttributes( trunc(obj1, digits=2) ) # ok getAttributes( signif(obj1, digits=3) ) # ok getAttributes( ceiling(100*obj1) ) # ok getAttributes( floor(100*obj1) ) # ok ################################################################################ # Financial 'timeSeries' Functions # fin-align.R getAttributes( align(obj1) ) # ok # fin-cumulated.R getAttributes( cumulated(obj1) ) # ok # fin-daily.R # align(obj1) and alignDailySeries(obj1) are the same # deprecate align Daily Series getAttributes( alignDailySeries(obj1) ) # ok # Can we use the generic function aggregate ? getAttributes( rollDailySeries(obj1, FUN=mean) ) # ok # fin-drawdowns.R getAttributes( drawdowns(obj1) ) # ok # fin-durations.R getAttributes( durations(obj1) ) # ok # fin-monthly.R getAttributes( rollMonthlySeries(obj1, "3m", FUN=mean) ) # ok getAttributes( countMonthlyRecords(obj1) ) # ok # fin-periodical.R # todo .endOfPeriodSeries .endOfPeriodStats .endOfPeriodBenchmarks # fin-returns.R OBJ1 <- cumulated(obj1) getAttributes( returns(OBJ1) ) # ok # fin-runlengths.R getAttributes( runlengths(obj1) ) # ok # fin-splits.R getAttributes( outlier(obj1) ) # ok # fin-spreads.R SPREADS <- spreads(obj3, which=c(1, 2)) getAttributes( SPREADS) # ok fails MIDQUOTES <- midquotes(obj3, which=c(1,2)) getAttributes( MIDQUOTES ) # ok fails # fin-turns.R INDEX <- cumulated(obj1) getAttributes( turns(INDEX) ) # ok ################################################################################ # Statistics timeSeries Functions # statistics-colCumsums.R getAttributes( colCumsums(obj1) ) # ok # statistics-colSums.R # returns no timeSeries objects # statistics-orderColnames.R # returns no timeSeries objects # statistics-orderStatistics.R # returns no timeSeries objects # statistics-rollMean.R getAttributes( rollStats(obj1, k=1, FUN=mean) ) # ok getAttributes( rollMean(obj1, k=1) ) # ok getAttributes( rollMin(obj1, k=1) ) # FAILS getAttributes( rollMax(obj1, k=1) ) # FAILS getAttributes( rollMedian(obj1, k=1) ) # ok # statistics-rowCumsums.R # statistics-smoothLowess.R getAttributes( smoothLowess(obj1) ) # ok getAttributes( smoothSupsmu(obj1) ) # ok getAttributes( smoothSpline(obj1) ) # ok ################################################################################ # stats # stats-aggregate.R by1 <- time(obj1[3*(1:4),]) getAttributes( aggregate(obj1, by=by1, FUN=mean) ) # ok # stats-filter.R getAttributes( filter(obj1, filter=c(1,1)) ) # ok # stats-lag.R getAttributes( lag(obj1) ) # ok # stats-na.contiguous.R # returns no timeSeries objects # stats-na.omit.R obj3 <- obj1; obj3[4, 1] <- NA; obj3 getAttributes( na.omit(obj3) ) # ok # What about? - They should be deprecated. # removeNA # substituteNA # interp NA # stats-window.R Time <- time(obj1) getAttributes( window(obj1, Time[3], Time[6]) ) # ok ################################################################################ # Attributes Functions getAttributes <- function (obj) { # FUNCTION: # Check Argument: stopifnot(class(obj) == "timeSeries") # Extract Attributes: ans <- attr(obj@documentation, "Attributes") # return Value: ans } # ----------------------------------------------------------------------------- `setAttributes<-` <- function(obj, value) { # Example: # obj <- dummySeries(); getAttributes(obj) # setAttributes(obj) <- list(mat=matrix(1:4, ncol=2)); getAttributes(obj) # getAttributes(obj)$mat[[1]] # FUNCTION: # Check Arguments: stopifnot(class(obj) == "timeSeries") stopifnot(is.list(value)) stopifnot(length(value) == 1) stopifnot(!is.null(value)) # Compose New Attribute: name <- names(value) names(value) <- NULL A <- list(value) names(A) <- name # print(A) # Get Already Existing Attribute B <- getAttributes(obj) if(is.null(B)) B <- list() # print(B) # Join Attributes: JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # print(JOINED) # Assign Attribute: attr(obj@documentation, "Attributes") <- JOINED # Return Value: obj } ############################################################################### timeSeries/inst/extensionsTests/endpointsWrappers.R0000644000176000001440000001457112620124746022500 0ustar ripleyusers require(timeSeries) ############################################################################### # FUNCTION: # timeNdayInWeek # timeLastBizdayInWeek # FUNCTION: # timeLastDayInMonth # timeLastNdayInMonth # timeLastBizdayInMonth # timeNthNdayInMonth # FUNCTION: # timeLastDayInQuarter # timeLastNdayInQuarter # timeLastBizdayInQuarter # timeNthNdayInQuarter ############################################################################### ############################################################################### # endpoints # extract index values of a given time Series object corresponding to # the last calendarical observation in the specified period require(timeSeries) # Daily and Monthly Series in 2011: tD <- timeCurrentYear(2011) tM <- timeCalendar(2011) ############################################################################### # Weekly Endpoints # ----------------------------------------------------------------------------- # On Given nDay of Week: timeNdayInWeek <- function(x, nday=5) { X <- align(x) DOW <- c("Sun", "Mon", "Tue", "Wed", "Thu", "Fri", "Sat") X[dayOfWeek(X) == DOW[nday+1], ] } LastDayInWeek <- timeNdayInWeek(tD) LastDayInWeek dayOfWeek(LastDayInWeek) # ----------------------------------------------------------------------------- # Last Bizday In Week: timeLastBizdayInWeek <- function(x, holidays=holidayNYSE) { # Extend time Sequence: x <- timeSequence( from = timeDate(x[1]), to = timeDate(x[length(x)]), by = "day") # Bizdays Function: FUN <- function(x, holidays = holidays) { holidays <- holidayNYSE() posix <- as.POSIXct(x, zone = "", origin = "1970-01-01") check <- isBizday(as.timeDate(posix), holidays = holidays, wday = 1:5) ans <- rev(x[check])[1] ans } # Create Periods from: by <- timeDayInWeek(x, nday=5) bySec <- as.numeric(by, "sec") xSec <- as.numeric(x, "sec") # Compute Index: INDEX <- findInterval(xSec, bySec + 1) INDEX <- INDEX + 1 is.na(INDEX) <- !(INDEX <= length(by)) dates <- matrix(apply(matrix(xSec, ncol=1), 2, tapply, INDEX, FUN), ncol=1) dates <- as.timeDate(as.POSIXct(dates, zone="GMT", origin="1970-01-01")) # Return Value: dates } LastNYBizdayInWeek <- timeLastBizdayInWeek(tD, holidays=holidayNYSE) LastNYBizdayInWeek dayOfWeek(LastNYBizdayInWeek) # Can we attribute the day of week? ans <- timeSeries( data = rnorm(length(LastBizdayInWeek)), charvec = timeLastBizdayInWeek(tD), recordIDs = data.frame(DOW=dayOfWeek(LastBizdayInWeek))) ############################################################################### # Monthly Endpoints # ----------------------------------------------------------------------------- # Last Calendar Day in Month: LastDayInMonth <- timeLastDayInMonth(tX, unique=TRUE) LastDayInMonth dayOfWeek(LastDayInMonth) # ----------------------------------------------------------------------------- # Last Friday in Month: LastFridayInMonth <- timeLastNdayInMonth(tX, nday=5, unique=TRUE) LastFridayInMonth dayOfWeek(LastFridayInMonth) # ----------------------------------------------------------------------------- # Last New-York Bizday in Month holidayNYSE(2011) LastNYBizdayInMonth <- timeLastBizdayInMonth(tD, holidays=holidayNYSE(), unique=TRUE) LastNYBizdayInMonth dayOfWeek(LastNYBizdayInMonth) # ----------------------------------------------------------------------------- # 2nd Tuesday in Month: SecondTuesdayInMonth <- timeNthNdayInMonth(tX, nth=2, nday=2, unique=TRUE) SecondTuesdayInMonth dayOfWeek(SecondTuesdayInMonth) ############################################################################### # Quarterly Endpoints # ----------------------------------------------------------------------------- # Last Day in Quarter: timeLastDayInQuarter <- function(charvec, format="%Y-%m-%d", zone="", FinCenter="", unique = FALSE) { # Description: # Returns Last Nday in Quarter ans <- timeLastDayInMonth(charvec, format, zone = "", FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastDayInQuarter <- timeLastDayInQuarter(tD, unique=TRUE) LastDayInQuarter dayOfWeek(LastDayInQuarter) # ----------------------------------------------------------------------------- # Last Friday in Quarter: timeLastNdayInQuarter <- function(charvec, nday = 1, format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { # Description: # Returns Last Nday first/mid/last inMonths of Quarters ans <- timeLastNdayInMonth(charvec, nday, format, zone, FinCenter,unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastFridayInQuarter <- timeLastNdayInQuarter(tD, nday=5, unique=TRUE) LastFridayInQuarter dayOfWeek(LastFridayInQuarter) # ----------------------------------------------------------------------------- # Last Bizday in Quarter: timeLastBizdayInQuarter <- function(charvec, holidays = holidayNYSE(), format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { ans <- timeLastBizdayInMonth(charvec, holidays, format, zone, FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% c(3, 6, 9, 12) ) ans[INDEX] } LastNYBizdayInQuarter <- timeLastBizdayInQuarter(tD, holidayNYSE(), unique=TRUE) LastNYBizdayInQuarter dayOfWeek(LastNYBizdayInQuarter) # ----------------------------------------------------------------------------- # nth-of Mar/Jun/Sep/Dec Nday in Quarter: timeNthNdayInQuarter <- function(charvec, nday = 1, nth = 1, inMonths = c(3, 6, 9, 12), format = "%Y-%m-%d", zone = "", FinCenter = "", unique = FALSE) { ans <- timeNthNdayInMonth(charvec, nday, nth, format, zone, FinCenter, unique) INDEX <- which ( atoms(ans)[, 2] %in% inMonths) ans[INDEX] } # 2nd Tuesday in (last) Months 3/6/9/12: SecondTuesdayInLastQuarterMonth <- timeNthNdayInQuarter(tD, nth=2, nday=2, unique=TRUE) SecondTuesdayInLastQuarterMonth dayOfWeek(SecondTuesdayInLastQuarterMonth) # 2nd Tuesday in (first) Months 1/4/7/10: SecondTuesdayInFirstQuarterMonth <- timeNthNdayInQuarter(tD, nth=2, nday=2, inMonths=c(1, 3, 7, 10), unique=TRUE) SecondTuesdayInFirstQuarterMonth dayOfWeek(SecondTuesdayInFirstQuarterMonth) # IMM Dates: # The dates are the third Wednesday of March, June, September and December datesIMM <- timeNthNdayInQuarter(tD, nth=3, nday=3, inMonths=c(3, 6, 9, 12), unique=TRUE) datesIMM dayOfWeek(datesIMM) ############################################################################### timeSeries/inst/extensionsTests/chicPlots.R0000644000176000001440000002521312620124746020674 0ustar ripleyusers x = tS1 FinCenter = NULL type = NULL plot.type = c("multiple", "single") format = "auto" at = c("pretty", "chic") main <- xlab <- ylab <- ""; nm = colnames(x); log = "" col = 1; pch = 19; cex = 1; lty = 1; lwd = 1 grid = TRUE; frame.plot = TRUE xlim = NULL; ylim = NULL axes = TRUE; ann = TRUE; cex.axis = 1, cex.lab =1, yax.flip = FALSE mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1) oma.multi = c(7.75, 1.1, 6.1, 1.1) # Plot Function Extensions written by Diethel Wuertz # ... first Version 2014-05-12 ############################################################################### # 1 Standard Plots # 1.1 Single Plots # 1.2 Multiple Plots # 1.2 Scatter Plots # 2 Time Axis Layout # 2.1 Pretty Axis Layout # 2.2 Chic Axis Layout # 2.3 Tailored Axis Layout # 3 Annotations # 3.1 Adding Title and Labels # 3.2 Removing Annotations # 3.3 Changing Font Size # 3.4 Flipping Value Axes # 4 Decorations # 4.1 Modifying Types # 4.2 Changing Colors # 4.3 Changing Line Styles # 4.4 Changing Plot Symbols # 4.5 Modifying Line Widths # 4.6 Modifying Plot Symbol Sizes ############################################################################### # First let us see what plot.ts can do in the multiple plot mode: require(timeSeries) tS1 <- 100 * cumulated(LPP2005REC[, 2]) tS2 <- 100 * cumulated(LPP2005REC[, 2:3]) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) tS7 <- 100 * cumulated(LPP2005REC[, 1:7]) # ----------------------------------------------------------------------------- # 1.3 Scatter Plots: mat <- getDataPart(tS2) par(mfrow=c(2,2)) plot(mat[, 1], mat[, 2]) plot(mat[, 1], mat[, 2], pch=19, cex=0.2) ################################################################################ # 2. Time Axis Layout: # Changing Time-Axis Size: # One Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS3, at="chic", plot.type="m", cex.axis=0.8) par(mfrow=c(1, 1)) plot(tS3, at="chic", plot.type="m", cex.axis=1.1) # Two Columns Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS6, at="chic", plot.type="m", cex.axis=0.8) par(mfrow=c(1, 1)) plot(tS6, at="chic", plot.type="m", cex.axis=1.1) ################################################################################ # 3 Annotations # ------------------------------------------------------------------------------ # 3.1 Adding Title and Labels # Single Plot - All Curves in one Graph: par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") # One Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS3, plot.type="m"); title(main = "Index", xlab = "Date") # Two Column Multiple Plots: par(mfrow=c(1, 1)) plot(tS6, plot.type="m"); title(main = "Index", xlab = "Date") # One Column Multiple Plots - User designed Title par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at = "chic") mtext("Swiss Market", side=3, line=1, adj=-0.025) # Two Column Multiple Plots - User designed Title par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at = "chic") mtext("Swiss Market", side=3, line=1, adj=-0.3) mtext("Foreign Market", side=3, line=1, adj=0.7) # ------------------------------------------------------------------------------ # 3.2 Remove all Annotations: # Two Column Multiple Plots - Each curve in its own Graph: par(mfrow=c(1, 1)) plot(tS6, plot.type="m", ann=FALSE) # Single Plot - All Curves in one Graph: par(mfrow=c(2, 1), mar = c(4, 4, 1, 2) + 0.1) plot(tS1, at="chic", ann=FALSE) title(ylab = colnames(tS1), cex.axis=0.8, cex.lab=0.8) plot(tS1, at="chic", ann=FALSE) title(ylab = colnames(tS1), cex.axis=1.2, cex.lab=1.2) ################################################################################ # 4 Decorations # ------------------------------------------------------------------------------ # 4.1 Modifying Types # "type" par(mfrow=c(1,1)) plot(tS3, type = c("l", "p", "h"), plot.type="m") # ------------------------------------------------------------------------------ # 4.2 Changing Colors # Selecting Colors: par(mfrow=c(2, 2)) plot(tS3, col = 1, plot.type="s") plot(tS3, col = 1:3, plot.type="s") plot(tS3, col = c("blue", "orange", "brown"), plot.type="s") # ------------------------------------------------------------------------------ # 4.3 Changing Line Styles # Single Plot: par(mfrow=c(1,1)) plot(tS3, lty = 3:1, plot.type="s") # One Column Multiple Plot: par(mfrow=c(1,1)) plot(tS3, lty = 3:1, plot.type="m") # ------------------------------------------------------------------------------ # 4.4 Changing Plot Symbols par(mfrow=c(1,1)) plot(tS3, pch = c(17, 18, 19), plot.type="m") # 4.5 Modifying Line Widths par(mfrow=c(1,1)) plot(tS3, lwd = c(17, 18, 19), plot.type="m") par(mfrow=c(2, 2)) plot(tS3, type=rep("p", 3), cex = rep(1.2, 3), plot.type="s") plot(tS3, type = rep("p", 3), pch = 1:3, col = c("blue", "orange", "brown"), plot.type="s") plot(tS3, type = "p", pch = 19, col = c("blue", "orange", "brown"), plot.type="s") # ----------------------------------------------------------------------------- chart.TimeSeries function (R, auto.grid = TRUE, xaxis = TRUE, yaxis = TRUE, yaxis.right = FALSE, type = "l", lty = 1, lwd = 2, main = NULL, ylab = NULL, xlab = "Date", date.format.in = "%Y-%m-%d", date.format = NULL, xlim = NULL, ylim = NULL, element.color = "darkgray", event.lines = NULL, event.labels = NULL, period.areas = NULL, event.color = "darkgray", period.color = "aliceblue", colorset = (1:12), pch = (1:12), legend.loc = NULL, ylog = FALSE, cex.axis = 0.8, cex.legend = 0.8, cex.lab = 1, cex.labels = 0.8, cex.main = 1, major.ticks = "auto", minor.ticks = TRUE, grid.color = "lightgray", grid.lty = "dotted", xaxis.labels = NULL, ...) { y = checkData(R) columns = ncol(y) rows = nrow(y) columnnames = colnames(y) if (is.null(date.format)) { freq = periodicity(y) yr_eq <- ifelse(format(index(first(y)), format = "%Y") == format(index(last(y)), format = "%Y"), TRUE, FALSE) switch(freq$scale, seconds = { date.format = "%H:%M" }, minute = { date.format = "%H:%M" }, hourly = { date.format = "%d %H" }, daily = { if (yr_eq) date.format = "%b %d" else date.format = "%Y-%m-%d" }, weekly = { if (yr_eq) date.format = "%b %d" else date.format = "%Y-%m-%d" }, monthly = { if (yr_eq) date.format = "%b" else date.format = "%b %y" }, quarterly = { if (yr_eq) date.format = "%b" else date.format = "%b %y" }, yearly = { date.format = "%Y" }) } rownames = as.Date(xts:::time.xts(y)) rownames = format(strptime(rownames, format = date.format.in), date.format) time.scale = periodicity(y)$scale ep = axTicksByTime(y, major.ticks, format.labels = date.format) logaxis = "" if (ylog) { logaxis = "y" } plot.new() if (is.null(xlim[1])) xlim = c(1, rows) if (is.null(ylim[1])) { ylim = as.numeric(range(y, na.rm = TRUE)) } plot.window(xlim, ylim, xaxs = "r", log = logaxis) if (is.null(ylab)) { if (ylog) ylab = "ln(Value)" else ylab = "Value" } if (ylog) dimensions = 10^par("usr") else dimensions = par("usr") if (!is.null(period.areas)) { period.dat = lapply(period.areas, function(x, y) c(first(index(y[x])), last(index(y[x]))), y = y) period.ind = NULL for (period in 1:length(period.dat)) { if (!is.na(period.dat[[period]][1])) { period.ind = list(grep(period.dat[[period]][1], index(y)), grep(period.dat[[period]][2], index(y))) rect(period.ind[1], dimensions[3], period.ind[2], dimensions[4], col = period.color, border = NA) } } } if (auto.grid) { abline(v = ep, col = grid.color, lty = grid.lty) grid(NA, NULL, col = grid.color) } abline(h = 0, col = element.color) if (!is.null(event.lines)) { event.ind = NULL for (event in 1:length(event.lines)) { event.ind = c(event.ind, grep(event.lines[event], rownames)) } number.event.labels = ((length(event.labels) - length(event.ind) + 1):length(event.labels)) abline(v = event.ind, col = event.color, lty = 2) if (!is.null(event.labels)) { text(x = event.ind, y = ylim[2], label = event.labels[number.event.labels], offset = 0.2, pos = 2, cex = cex.labels, srt = 90, col = event.color) } } if (length(lwd) < columns) lwd = rep(lwd, columns) if (length(lty) < columns) lty = rep(lty, columns) if (length(pch) < columns) pch = rep(pch, columns) for (column in columns:1) { lines(1:rows, y[, column], col = colorset[column], lwd = lwd[column], pch = pch[column], lty = lty[column], type = type, ...) } if (xaxis) { if (minor.ticks) axis(1, at = 1:NROW(y), labels = FALSE, col = "#BBBBBB") label.height = cex.axis * (0.5 + apply(t(names(ep)), 1, function(X) max(strheight(X, units = "in")/par("cin")[2]))) if (is.null(xaxis.labels)) xaxis.labels = names(ep) else ep = 1:length(xaxis.labels) axis(1, at = ep, labels = xaxis.labels, las = 1, lwd = 1, mgp = c(3, label.height, 0), cex.axis = cex.axis) title(xlab = xlab, cex = cex.lab) } if (yaxis) if (yaxis.right) axis(4, cex.axis = cex.axis, col = element.color, ylog = ylog) else axis(2, cex.axis = cex.axis, col = element.color, ylog = ylog) box(col = element.color) if (!is.null(legend.loc)) { legend(legend.loc, inset = 0.02, text.col = colorset, col = colorset, cex = cex.legend, border.col = element.color, lty = lty, lwd = 2, bg = "white", legend = columnnames, pch = pch) } if (is.null(main)) main = columnnames[1] title(ylab = ylab, cex = cex.lab) title(main = main, cex = cex.main) } timeSeries/inst/extensionsTests/aggregateWrappers.R0000644000176000001440000002151212620124746022414 0ustar ripleyusers require(timeSeries) X <- cumulated(LPP2005REC)[, 1:3] for (i in 1:3) X[, i] <- 100*X[, i]/as.vector(X[1,i]) Data <- alignDailySeries(X) # add: startDate Index <- time(Data) # Generate time Series: tS <- timeSeries(data=Data, charvec=format(Index)) tR <- returns(tS) ############################################################################### # aggregate: # from stats Package: The function aggregate splits the data into subsets, # computes summary statistics for each, and returns the result in a # convenient form. # AGGREGATION OVER NON-OVEWRLAPPING PERIODS # starting point: aligned daily Data # Aggregation Function: # function (x, by, FUN, ...) # Aggregation Levels: # weekly/biweekly: endOfWeek, onTuesdays, lastBusinessDay # monthly: endOMonth, lastFriday, lastBusinessDay # quarterly: 3-monthly # half-annually: 6-monthly # yearly: 12-monthly # Aggregation Statistics: # mean, sd, var, median, ... open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) spread <- function(x) max(x) - min(x) # ----------------------------------------------------------------------------- # Weekly # End-of-week: by <- timeLastDayInMonth(time(tS)) mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # Weekly - Last Zurich Business Day In Week by <- timeLastBizdayInMonth(time(tS), holidays = holidayZURICH()) mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # Weekly on Tuesdays by <- timeSequence(from=start(tD), to=end(tD), by = "week") mean.tR <- aggregate(tR[, "SPI"], by, mean) sd.tR <- aggregate(tR[, "SPI"], by, sd) plot(cbind(mean.tR, sd.tR), type="h") # ----------------------------------------------------------------------------- # Monthly # ----------------------------------------------------------------------------- # Quarterly ############################################################################### # ----------------------------------------------------------------------------- # End-of-Month Statistics # ----------------------------------------------------------------------------- # Monthly Open-High-Low-Close open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) OHLC <- cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) OHLC # ----------------------------------------------------------------------------- # Monthly Spread / Percentual Spread spread <- function(x) max(x) - min(x) pspread <- function(x) (max(x) - min(x)) / (0.5 * (max(x) + min(x))) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) SPREAD <- cbind( Points=aggregate(SPI, by, spread), Percent=100*aggregate(SPI, by, pspread)) SPREAD <- round(SPREAD, 2) SPREAD ################################################################################ # Rolling: Aggregation with Overlappinng Periods # ----------------------------------------------------------------------------- # rolling 52-weekly-highs and lows # xts: Mean on weekly Periods ep <- xts::endpoints(x.xts, on='weeks', k=1) by1 <- index(x.xts)[ep[-1]] period1 <- xts::period.apply(x.xts, INDEX=ep, FUN=mean) ############################################################################### # xts::apply.monthly FUN <- mean x <- x.xts apply.daily(x, FUN) apply.weekly(x, FUN) apply.monthly(x, FUN) apply.quarterly(x, FUN) apply.yearly(x, FUN) # timeDate::align FUN <- mean x <- unique(time(x.tS)) alignDaily(x, include.weekends=FALSE) by1 <- unique(alignMonthly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignMonthly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) by1 <- unique(alignQuarterly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignQuarterly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) cbind(x1,x2) ############################################################################### xts::first(x.xts) xts::last(x.xts) first2 <- function(x) x[start(x), ] last2 <- function(x) x[end(x), ] first2(x.tS) last2(x.tS) # ----------------------------------------------------------------------------- INDEX <- seq(1, nrow(xts), by=21) INDEX .period.apply(tS, INDEX, FUN=max) .period.max <- function(x, INDEX, FUN=max) .period.apply(x, INDEX, max) .period.max(tS[, 1], INDEX) .period.min <- function(x, INDEX) .period.apply(x, INDEX, min) .period.min(tS[, 1], INDEX) xts::period.apply(xts[, 1], INDEX, FUN=max) xts::period.max(xts[, 1], INDEX) xts::period.min(xts[, 1], INDEX) xts::period.prod(xts[, 1], INDEX) xts::period.sum(xts[, 1], INDEX) # ----------------------------------------------------------------------------- # timeBased is.timeBased <- function (x) { if (!any(sapply(c( "Date", "POSIXt", "chron", "dates", "times", "timeDate", "yearmon", "yearqtr", "xtime"), function(xx) inherits(x, xx)))) { ans <- FALSE } else { ans <- TRUE } ans } timeBased <- function(x) { is.timeBased(x) } # ----------------------------------------------------------------------------- alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x, drop.time=TRUE, name,...) xts::to.monthly(x, indexAt='yearmon', drop.time=TRUE,name,...) xts::to.quarterly(x, indexAt='yearqtr', drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- Convert an object to a specified periodicity lower than the given data object. For example, convert a daily series to a monthly series, or a monthly series to a yearly one, or a one minute series to an hourly series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") apply applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). timeSeries/inst/extensionsTests/alignWrappers.R0000644000176000001440000001720712620124746021566 0ustar ripleyusers # artificial 1 sec data with missing Data tX <- timeSequence("2014-03-07 00:00:00", "2014-03-07 23:59:59", by="sec") s <- sample(1:length(tX))[1:length(tX)/10] tX <- tX[-s] ############################################################################### # align # extract index values of a given xts object corresponding to the last # observations given a period specified by on require(timeSeries) # Random Seed: set.seed(1953) # Create a day of 1s time stamps: tX <- timeSequence("2014-03-07 09:03:17", "2014-03-07 15:53:16", by="sec") # Remove randomly 10% of the data: s <- sample(1:length(tX))[1:length(tX)/10] tX <- sort(tX[-s]) tS <- 201.7*cumulated(timeSeries(data=rnorm(length(tX))/(24*3600), charvec=tX)) plot(tS) head(tS) tZ <- align(tS, by="1min", method="fillNA", offset="42s") head(tZ) tZ <- align(tS, by="3min", method="fillNA", offset="162s") head(tZ) tZ <- align(tS, by="5min", method="fillNA", offset="102") head(tZ) tZ <- align(tS, by="15min", method="fillNA", offset="702s") head(tZ) tZ <- align(tS, by="30min", method="fillNA", offset="1602s") head(tZ) tZ <- align(tS, by="60min", method="fillNA", offset="3402") head(tZ) toPeriod <- function(x, by, method, offset="0s"") { open <- function(x) as.vector(x)[1] high <- function(x) max(x) low <- function(x) min(x) close <- function(x) rev(as.vector(x))[1] cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) } A1 <- timeSeries::align(tS, by="60min") A2 <- xts::to.period(as.xts(tS), period = "minutes", k = 2) open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) SPI <- tS[, "SPI"] by <- timeLastDayInMonth(time(tS)) OHLC <- cbind( aggregate(SPI, by, open), aggregate(SPI, by, high), aggregate(SPI, by, low), aggregate(SPI, by, close)) OHLC xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) # ----------------------------------------------------------------------------- # Time alignment: alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x,drop.time=TRUE,name,...) xts::to.monthly(x,indexAt='yearmon',drop.time=TRUE,name,...) xts::to.quarterly(x,indexAt='yearqtr',drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- Convert an object to a specified periodicity lower than the given data object. For example, convert a daily series to a monthly series, or a monthly series to a yearly one, or a one minute series to an hourly series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") apply applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). x1 <- xts(matrix(1:(9*6),nc=6), order.by=as.Date(13000,origin="1970-01-01")+1:9) x2 <- x1 xtsAttributes(x1) <- list(series1="1") xtsAttributes(x2) <- list(series2="2") xtsAttributes(x1) xtsAttributes(x2) x3 <- x1+x2 xtsAttributes(x3) x33 <- cbind(x1, x2) xtsAttributes(x33) x33 <- rbind(x2, x1) xtsAttributes(x33) ############################################################################### appendList <- function (x, value) { stopifnot(is.list(x), is.list(value)) xnames <- names(x) for (v in names(value)) { x[[v]] <- if (v %in% xnames && is.list(x[[v]]) && is.list(value[[v]])) appendList(x[[v]], value[[v]]) else c(x[[v]], value[[v]]) } x } "setAttributes<-" <- function(obj, value) { stopifnot(is.list(value)) ATTRIBUTES <- getAttributes(obj) VALUE <- appendList(ATTRIBUTES, value) attr(obj@documentation, "Attributes") <- VALUE obj } getAttributes <- function(obj) { attr(obj@documentation, "Attributes") } obj1 <- dummySeries() getAttributes(obj1) setAttributes(obj1) <- list(series="obj1") getAttributes(obj1) obj2 <- dummySeries() getAttributes(obj2) setAttributes(obj2) <- list(series="obj2") getAttributes(obj2) getAttributes(obj1+obj2) # returns the attributes only for the first getAttributes(obj1-obj2) # returns the attributes only for the first getAttributes(cbind(obj1, obj2)) getAttributes(cbind(obj1, as.matrix(obj2))) # matrix fails getAttributes(rbind(obj1, obj2)) getAttributes(rbind(obj1, as.matrix(obj2))) # matrix fails getAttributes( rev(obj) ) getAttributes( obj[, 1] ) getAttributes( sample(obj) ) getAttributes( sort(sample(obj)) ) getAttributes( scale(obj) ) getAttributes( returns(obj) ) getAttributes( cumulated(returns(obj)) ) BIND(# Add another Attribute: ATTRIBUTES <- attr(obj@documentation, "Attributes") ATTRIBUTES ATTRIBUTES <- appendList(ATTRIBUTES, list(say="hello")) ATTRIBUTES attr(obj@documentation, "Attributes") <- ATTRIBUTES cbind(obj, obj, documentation = obj@documentation) # Documentation # Series: # dim(@.Data) # @units # @positions # @format # @FinCenter # @recordIDs # @title # @documentation # attributes(@documentation, "attributes) timeSeries/inst/extensionsTests/xtsWrappers.R0000644000176000001440000002451012620124746021305 0ustar ripleyusers require(xts) require(timeSeries) X <- cumulated(LPP2005REC)[, 1:3] for (i in 1:3) X[, i] <- 100*X[, i]/as.vector(X[1,i]) Data <- alignDailySeries(X) # add: startDate Index <- time(Data) # Generate time Series: x.tS <- timeSeries(data=Data, charvec=format(Index)) x.xts <- xts(x=Data, order.by=strptime(Index, format="%Y-%m-%d"), tzone="GMT") ############################################################################### # Class: class(x.xts) class(x.tS) # ----------------------------------------------------------------------------- # coredata # "coredata" methods for time series objects strip off the index/time # attributes and return only the observations. # xts/zoo: COREDATA <- zoo::coredata(x=x.xts) class(COREDATA) head(COREDATA) dimnames(COREDATA) # timeSeries: coredata2 <- function(x) methods::getDataPart(x) COREDATA <- coredata2(tS) class(COREDATA) head(COREDATA) dimnames(COREDATA) # timeSeries: SERIES <- series(tS) class(SERIES) head(SERIES) dimnames(SERIES) # Extractor Function # getDataPart # setDataPart # ----------------------------------------------------------------------------- # index # Generic functions for extracting the index of an object and replacing it. # xts/zoo: INDEX <- zoo::index(x=x.xts) class(INDEX) head(INDEX) # xts/zoo: TIME <- stats::time(x=x.xts) class(TIME) head(TIME) # xts/zoo: INDEX <- index(x=x.tS) class(INDEX) head(INDEX) TIME <- stats::time(x=x.tS) class(TIME) head(TIME) # Extractor Function: # getTime # setTime # ----------------------------------------------------------------------------- # indexClass # The specified value for indexClass<- must be a character string # containing one of the following: Date, POSIXct, chron, yearmon, # yearqtr or timeDate. indexClass(x.xts) tclass(x.xts) indexClass <- function(x) class(time(x)) tclass <- function(x) class(time(x)) class(x.tS) indexClass(x.tS) tclass(x.tS) # ----------------------------------------------------------------------------- # indexFormat # Functions to extract, replace, and format the class of the index of # an xts object. FORMAT <- indexFormat(x.xts) FORMAT indexFormat <- function(x) getSlot(x, "format") indexFormat(x.tS) indexFormat <- function(x) slot(x, "format") indexFormat(x.tS) indexFormat <- function(x) x.tS@format indexFormat(x.tS) # Extractor Function # getFormat # setFormat # ----------------------------------------------------------------------------- # indexTZ xts::indexTZ(x.xts) xts::tzone(x.xts) indexTZ <- function(x) getSlot(x.tS, "FinCenter") tzone(x.xts) getFinCenter(x.tS) x.tS@FinCenter getSlot(x.tS, "FinCenter") ############################################################################### # endpoints # extract index values of a given xts object corresponding to the last # observations given a period specified by on require(timeSeries) tD <- timeCurrentYear(2011) tM <- timeCalendar(2011) ############################################################################### # aggregate: # from stats Package: The function aggregate splits the data into subsets, # computes summary statistics for each, and returns the result in a # convenient form. # AGGREGATION OVER NON-OVEWRLAPPING PERIODS # starting point: aligned daily Data # Aggregation Statistics: open <- function(x) as.vector(x)[1] close <- function(x) rev(as.vector(x))[1] high <- function(x) max(x) low <- function(x) min(x) spread <- function(x) max(x) - min(x) # Aggregation Levels: # weekly/biweekly: endOfWeek, onTuesdays, lastBusinessDay # monthly: endOMonth, lastFriday, lastBusinessDay # quarterly: 3-monthly # half-annually: 6-monthly # yearly: 12-monthly # timeSeries: Weekly - end of week tD <- time(x.tS) tD <- tD[dayOfWeek(tD) == "Fri"] by <- timeSequence(from=start(tD), to=end(tD), by = "week") endOfWeek <- aggregate(x.tS, by, mean) endOfWeek # timeSeries: Weekly on Tuesdays tD <- time(x.tS[-(1:5), ]) by <- timeSequence(from=start(tD), to=end(tD), by = "week") tuesdays.period <- aggregate(x.tS, by, last) dayOfWeek(time(tuesdays.period)) cbind( open=aggregate(x.tS[, 1], by, open), high=aggregate(x.tS[, 1], by, high), low=aggregate(x.tS[, 1], by, low), close=aggregate(x.tS[, 1], by, close)) period1 <- as.timeSeries(period1) cbind(period1, period2, period3) # Aggregate to Last Friday of Month - tD <- timeSequence(from=start(tD), to=end(tD), by = "week") by <- unique(timeLastNdayInMonth(tD, nday=5)) aggregate(x.tS, by, mean) # Aggregate to Last Day of Quarter - by <- unique(timeLastDayInQuarter(tD)) aggregate(x.tS, by, mean) # ----------------------------------------------------------------------------- # rolling 52-weekly-highs and lows # xts: Mean on weekly Periods ep <- xts::endpoints(x.xts, on='weeks', k=1) by1 <- index(x.xts)[ep[-1]] period1 <- xts::period.apply(x.xts, INDEX=ep, FUN=mean) ############################################################################### # xts::apply.monthly FUN <- mean x <- x.xts apply.daily(x, FUN) apply.weekly(x, FUN) apply.monthly(x, FUN) apply.quarterly(x, FUN) apply.yearly(x, FUN) # timeDate::align FUN <- mean x <- unique(time(x.tS)) alignDaily(x, include.weekends=FALSE) by1 <- unique(alignMonthly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignMonthly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) by1 <- unique(alignQuarterly(x, include.weekends=FALSE)) x1 <- timeSeries::aggregate(x.tS, by1, FUN) by2 <- unique(alignQuarterly(x, include.weekends=TRUE)) x2 <- timeSeries::aggregate(x.tS, by2, FUN) cbind(x1,x2) ############################################################################### xts::first(x.xts) xts::last(x.xts) first2 <- function(x) x[start(x), ] last2 <- function(x) x[end(x), ] first2(x.tS) last2(x.tS) # ----------------------------------------------------------------------------- INDEX <- seq(1, nrow(xts), by=21) INDEX .period.apply(tS, INDEX, FUN=max) .period.max <- function(x, INDEX, FUN=max) .period.apply(x, INDEX, max) .period.max(tS[, 1], INDEX) .period.min <- function(x, INDEX) .period.apply(x, INDEX, min) .period.min(tS[, 1], INDEX) xts::period.apply(xts[, 1], INDEX, FUN=max) xts::period.max(xts[, 1], INDEX) xts::period.min(xts[, 1], INDEX) xts::period.prod(xts[, 1], INDEX) xts::period.sum(xts[, 1], INDEX) # ----------------------------------------------------------------------------- # timeBased is.timeBased <- function (x) { if (!any(sapply(c( "Date", "POSIXt", "chron", "dates", "times", "timeDate", "yearmon", "yearqtr", "xtime"), function(xx) inherits(x, xx)))) { ans <- FALSE } else { ans <- TRUE } ans } timeBased <- function(x) { is.timeBased(x) } # ----------------------------------------------------------------------------- alignDaily(x=time(tS), include.weekends=FALSE) alignMonthly(x=time(tS), include.weekends=FALSE) # error alignQuarterly(x=time(tS), include.weekends=FALSE) # error tD <- Sys.timeDate() + 1:1000 timeDate::align(tD, by="10s") timeDate::align(tD, by="60s") timeDate::align(tD, by="10m") # error td <- as.xts(Sys.time()) + 1:1000 xts::align.time(td, n=10) # every 10 seconds xts::align.time(td, n=60) # align to next whole minute xts::align.time(td, n=10*60) # align to next whole 10 min interval xts::shift.time(td, n=10) xts::shift.time(td, n=60) xts::shift.time(td) # ----------------------------------------------------------------------------- xts::to.minutes(x,k,name,...) xts::to.minutes3(x,name,...) xts::to.minutes5(x,name,...) xts::to.minutes10(x,name,...) xts::to.minutes15(x,name,...) xts::to.minutes30(x,name,...) xts::to.hourly(x,name,...) xts::to.daily(x,drop.time=TRUE,name,...) xts::to.weekly(x,drop.time=TRUE,name,...) xts::to.monthly(x,indexAt='yearmon',drop.time=TRUE,name,...) xts::to.quarterly(x,indexAt='yearqtr',drop.time=TRUE,name,...) xts::to.yearly(x,drop.time=TRUE,name,...) xts::to.period( x, period = 'months', k = 1, indexAt, name=NULL, OHLC = TRUE, ...) # ----------------------------------------------------------------------------- # Convert an object to a specified periodicity lower than the given data # object. For example, convert a daily series to a monthly series, or a # monthly series to a yearly one, or a one minute series to an hourly # series. data(sample_matrix) xts <- as.xts(sample_matrix) # is daily to.weekly(xts) to.monthly(xts) to.quarterly(xts) to.yearly(xts) tS <- as.timeSeries(sample_matrix) % ----------------------------------------------------------------------------- as.numeric(as.POSIXct(time(tS))) getFinCenter(tS) indexTZ(xts, ) tzone(xts, ) tzone(xts) <- "GMT" .index(xts, ) indexClass(xts) class(time(tS)) % ----------------------------------------------------------------------------- .index <- function(x) as.numeric(as.POSIXct(time(x))) .indexDate <- function(x) .index(x)%/%86400L .indexday <- function(x) .index(x)%/%86400L .indexmday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$mday .indexwday <- function(x) as.POSIXlt(.POSIXct(.index(x)))$wday .indexweek <- function(x) .indexmon <- function(x) .indexyday <- function(x) .indexyear <- function(x) .indexhour <- function(x) .indexmin <- function(x) .indexsec <- function(x) # Atoms # atoms # Roll over fixed periods of length k point by point ... # Functions borrowed from zoo timeSeries::rollMin( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMax( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMean( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollMedian( x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) timeSeries::rollStats( x, k, FUN = mean, na.pad = FALSE, align = c("center", "left", "right"), ...) # Roll over Calendarical periods: rollDailySeries(x, period="7d", FUN, ...) rollMonthlySeries(x, period="12m", by="1m", FUN, ...) # e.g. rollQuarterlySeries(x, period="12m", by="3m", FUN) # e.g. rollYearlySeries rollMonthlyWindows(x, period="12m", by="1m") # apply # applySeries # period.apply # Apply a specified function to data over a given interval, where the # interval is taken to be the data from INDEX[k] to INDEX[k+1], for # k=1:(length(INDEX)-1). # ----------------------------------------------------------------------------- timeSeries/inst/README0000644000176000001440000000012012620124746014245 0ustar ripleyusersintroduction of timeSeries package in the Rmetrics suite after svn revision 3319timeSeries/inst/THANKS0000644000176000001440000000000112620124746014276 0ustar ripleyusers timeSeries/inst/COPYRIGHTS0000644000176000001440000000770512620124746015023 0ustar ripleyusers________________________________________________________________________________ Copyrights (C) for R: see R's copyright and license file Version R 2.0.0 claims: - The stub packages from 1.9.x have been removed. - All the datasets formerly in packages 'base' and 'stats' have been moved to a new package 'datasets'. - Package 'graphics' has been split into 'grDevices' (the graphics devices shared between base and grid graphics) and 'graphics' (base graphics). - Packages must have been re-installed for this version, and library() will enforce this. - Package names must now be given exactly in library() and require(), regardless of whether the underlying file system is case-sensitive or not. ________________________________________________________________________________ for Rmetrics: (C) 1999-2005, Diethelm Wuertz, GPL Diethelm Wuertz www.rmetrics.org info@rmetrics.org ________________________________________________________________________________ for non default loaded basic packages part of R's basic distribution MASS: Main Package of Venables and Ripley's MASS. We assume that MASS is available. Package 'lqs' has been returned to 'MASS'. S original by Venables & Ripley. R port by Brian Ripley . Earlier work by Kurt Hornik and Albrecht Gebhardt. methods: Formally defined methods and classes for R objects, plus other programming tools, as described in the reference "Programming with Data" (1998), John M. Chambers, Springer NY. R Development Core Team. mgcv: Routines for GAMs and other generalized ridge regression with multiple smoothing parameter selection by GCV or UBRE. Also GAMMs by REML or PQL. Includes a gam() function. Simon Wood nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models Original by Venables & Ripley. R port by Brian Ripley . Earlier work by Kurt Hornik and Albrecht Gebhardt. ________________________________________________________________________________ for the code partly included as builtin functions from other R ports: fSeries:bdstest.c C Program to compute the BDS Test. Blake LeBaron fSeries:fracdiff R functions, help pages and the Fortran Code for the 'fracdiff' function are included. S original by Chris Fraley R-port by Fritz Leisch since 2003-12: Martin Maechler fSeries:lmtest R functions and help pages for the linear modelling tests are included . Compiled by Torsten Hothorn , Achim Zeileis , and David Mitchell fSeries:mda R functions, help pages and the Fortran Code for the 'mars' function are implemeted. S original by Trevor Hastie & Robert Tibshirani, R port by Friedrich Leisch, Kurt Hornik and Brian D. Ripley fSeries:modreg Brian Ripley and the R Core Team fSeries:polspline R functions, help pages and the C/Fortran Code for the 'polymars' function are implemented Charles Kooperberg fSeries:systemfit Simultaneous Equation Estimation Package. R port by Jeff D. Hamann and Arne Henningsen fSeries:tseries Functions for time series analysis and computational finance. Compiled by Adrian Trapletti fSeries:UnitrootDistribution: The program uses the Fortran routine and the tables from J.G. McKinnon. fSeries:urca Unit root and cointegration tests for time series data. R port by Bernhard Pfaff . timeSeries/inst/doc/0000755000176000001440000000000012620124760014135 5ustar ripleyuserstimeSeries/inst/doc/timeSeriesPlot.Rnw0000644000176000001440000015272612620124760017612 0ustar ripleyusers%\VignetteIndexEntry{Plotting 'timeSeries' Objects} \documentclass[10pt,a4paper]{article} \usepackage{hyperref} \hypersetup{colorlinks,% citecolor=black,% linkcolor=blue,% urlcolor=darkgreen,% } \title{\bf Plotting 'timeSeries' Objects} \author{Diethelm W\"urtz and Tobias Setz\\ETH Zurich and Rmetrics Association Zurich} \date{May 12, 2014} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \setlength{\parskip}{20pt} %\SweaveOpts{strip.white=FALSE} \setkeys{Gin}{width=0.9\textwidth} % plot.ts <- function ( % x, y = NULL, plot.type = c("multiple", "single"), % xy.labels, xy.lines, panel = lines, nc, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) % plot.zoo <- function ( % x, y = NULL, screens, plot.type, panel = lines, % xlab = "Index", ylab = NULL, main = NULL, % xlim = NULL, ylim = NULL, % xy.labels = FALSE, xy.lines = NULL, % yax.flip = FALSE, % oma = c(6, 0, 5, 0), % mar = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % col = 1, lty = 1, lwd = 1, pch = 1, type = "l", log = "", % nc, widths = 1, heights = 1, ...) % plot.xts <- function ( % x, y = NULL, type = "l", auto.grid = TRUE, % major.ticks = "auto", minor.ticks = TRUE, major.format = TRUE, % bar.col = "grey", candle.col = "white", % ann = TRUE, axes = TRUE, ...) % .plot.timeSeries <-function( % x, y, FinCenter = NULL, type = NULL, plot.type = c("multiple", "single"), % format = "auto", at = c("chic", "pretty"), % col, pch, cex, lty, lwd, % grid = FALSE, frame.plot = TRUE, panel = lines, % axes = TRUE, ann = TRUE, cex.axis = 1, cex.lab = 1, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(7.75, 1.1, 6.1, 1.1), % ...) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 1 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Introduction} The Rmetrics \texttt{timeDate} and \texttt{timeSeries} packages are workhorses to deal with chronological objects. Since their inception 2009 under their original names \texttt{fCalendar} and \texttt{fSeries} they have been only slightly modified. With version R 3.1. we have essentially improved the \texttt{plot} function, but we also took care that the functionality is almost upward compatible. In this vignette we show how to work with the recently updated S4 generic plot function \texttt{plot}. The function is written to display Rmetrics S4 \texttt{timeSeries} objects. The basic functionality of the \texttt{plot} function is to display single and multiple views on univariae and multivariate \texttt{timeSeries} objects. The function \texttt{plot.ts} from R's base environment, which displays basic \texttt{ts} time series objects, served as a model for our design of the generic S4 \texttt{plot} function for \texttt{timeSeries} objects. Similarily, \texttt{plot.ts} can be considered as the prototype for the S3 \texttt{plot.zoo} method. The \texttt{xts} plot function was build to display univariate \texttt{xts} time series objects which inherit from \texttt{zoo}'s objects for ordered time series objects. The generic S4 time series plotting function can dispay \emph{univariate} and \emph{multivariate} time series in \emph{single} and \emph{multiple} frames. The plots can be tailored with respect to several viewing components: colors (col), line types (lty), plot symbols (pch), line widths (lwd), symbol sizes (cex), axis layout (pretty, chic, tailored), minor tick mark appearence, font styles and font sizes, frame positioning (mar, oma), as well as tailored panel functions (panel). \noindent\emph{General Plot Settings and Design Apects}: \noindent\emph{Plot Type}: Univariate time series are displayed by default in \texttt{plot.type="single"} frames, multivariate time series are displayed by default in \texttt{plot.type="multiple"} frames. The default line style for a plot is \texttt{type ="l"} is drawn with "lines". \noindent\emph{Time Axis Layout}: For the time axis layout the function \texttt{pretty} determines in an automative way the \texttt{at="pretty"} positions of the ticks. The \texttt{format="auto"} is extracted from the time stamps of the time series object or can be overwritten by the user with a POSIX format string. Alternatively, one can select \texttt{"chic"} to generate time axis styles. We called this method "chic" to give reference to the underlying function \texttt{axTicksByTime} from the Chicago \texttt{xts} package which generates tick positions and axis labels. Furthermore, a "tailored" method can be applied which allows for fully arbitrary user defined positions and formatted labels. Minor ticks can be added in several fashions. \noindent\emph{Annotations}: The annotations of the plots are reduced to the y-label. These are taken by default from the column names of the time series object. This gives the user the freedom to have full control about his views how the plot should be look like. Note, multivariate time series in single plots show the string \texttt{"Values"} as label on the y-axis. Main title, sub title, and the x-label on the time axis are not shown by default. We prefer and recommend to add these decorations calling the function \texttt{title}. This allows also much more flexibility compared to passing the arguments through the plot functions. All default annotations (including the y-label) can be suppressed setting the plot function argument to \texttt{ann=FALSE}. The argument \texttt{axes=FALSE} suppresses to draw both axes on the plot frame. \noindent\emph{Decorations}: There are several options to decorate the plot: These include colors (col), plotting symbols (pch), scaling factor of plotting characters and symbols (cex), line types (lty), and lindwidths (lwd). Note, all these parameters may be vectors of the same length as the number of time series, so that each series can be addressed to its own individual style, color, and size. A grid and the plot frame (box) can be added or suppresse specifying the arguments \texttt{grid} and \texttt{frame.plot} in the argument list of the \texttt{plot} function. \noindent\emph{Panel Function}: In the case of multiple plots the plot frames, are also called \emph{panels}. By default in each panel the appropriate curve is drawn calling R's \texttt{lines} function \texttt{panel=lines}. This function can be replaced by a user defined function. This offers a wide range of new views on your time series. So for example yo can show zero or any other reference lines on the panels, or you can add rugs to (return) charts, or you can add for an example an EMA indicator (or any other kind of indicator) to curves shown in individual panels. \noindent\emph{Example "timeSeries" Objects}: To demonstrate the wide range of options to dispaly S4 \texttt{timeSeries} objects, we use the the daily index values from the Swiss Pension Fund Benchmark \emph{LPP2005}. The time series is part of the \texttt{timeSeries} package. For this we have introduced some abbreviations: <>= Sys.setlocale("LC_ALL", "C") @ <>= require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 2 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Standard Time Series Plots} The \texttt{plot} function from the \texttt{timeSeries} package allows for five different views on standard plot layouts. These include \begin{itemize} \item Univeriate single plots \item Multivariate single plots \item One column multiple plots \item Two column multiple plots \item Scatter plots \end{itemize} \noindent The only argument we have to set is the \texttt{plot.type} parameter to determine the layout of the plot. The default value is \texttt{"multiple"}, and the alternative value is \texttt{"single"}. The arguments can be abbreviated as \texttt{"m"} or \texttt{"s"}, respectively. \noindent \emph{Univariate Single Plots} were designed to plot univariate \texttt{timeSeries} objects in one single graph frame. Nothing then the \texttt{timeSeries} object has to be specified, the \texttt{plot.type} is forced to \texttt{"s"}. \noindent \emph{Multivariate Single Plots} will be used when a set of multivariate \texttt{timeSeries} objects should be drawn in one common data frame. For this argument the vlue \texttt{plot.type="s"} has to be specified. \noindent \emph{One Column Multiple Plots} display multivariate \texttt{timeSeries} objects, such that each series is plotted in its own frame. Up to four series, the frames are displayed in one column, for more series the frames are arranged in a two colum column display. \noindent \emph{Two Column Multiple Plots} handel the case of more than four \texttt{timeSeries} objects. Then the the series are displayed in two colums. In total, the number of rows is not restricted. % ---------------------------------------------------------------------------- \pagebreak \subsection{Univariate Single Plots} The most simple time series plot shows an univariate curve in a single plot. The axis is designed from "pretty" positions calculated from R's base function \texttt{pretty}. The labels are printed in the ISO 8601 standard date/time format. <>= par(mfrow=c(1, 1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The chart shows an univariate time series (here the Swiss Bond Index) in a single frame. For all plot options default values have been chosen. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color (col), add a main title and x-label calling the function \texttt{title}, or remove the grid lines setting the argument \texttt{grid=FALSE}. You can also design the minor tick marks, setting instead of the value \texttt{"auto"} oe of the following spreads: \texttt{"day"}, the default, \texttt{"week"}, or \texttt{"month"}. } \end{figure} \end{center} % ---------------------------------------------------------------------------- \pagebreak \subsection*{} Tow other plot function implementations for \texttt{xts} time series objects can be found in the contributesd R packages \texttt{xts} and \texttt{PerformanceAnalytics}. Let us compare how they generate plot positions and time label formats. \vspace{-0.3cm} <>= require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The group of the three charts shows an univariate time series in a single frame for the plot functions as implemented in the packages \texttt{xts}, \texttt{PerformanceAnalytics}, and \texttt{timeSeries}. For example in the case of daily time series records \texttt{xts} uses U.S. style labels whereas \texttt{PerformanceAnalytics} and \texttt{timeSeries} use ISO standard date labels \texttt{YYYY-mm-dd}. The plot decorations are those from default settings.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multivariate Single Plots} Multivariate time series plots in a single panel are constructed by default in the way that the first curve is plotted calling the function \texttt{plot} and the remaining curves by calling the function \texttt{lines}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This chart shows a multivariate time series in a single frame. Note, we have to set the argument \texttt{plot.type="s"}. Again, for all plot options the default settings have been used. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color vector (col), add a main title and x-label calling the function \texttt{title}, or remove grid lines setting the argument \texttt{grid=FALSE}. Note, to change the color settings you can set the argument \texttt{col=1:3} which would result in "black", "red", "green" for the three curves, or you can just set the colors by name, or selecting them from a color palette.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us compare the plot function from the \texttt{timeSeries} package with the \texttt{chart.TimeSeries} plotting function from the \texttt{PerformanceAnalytics} function. (Note, the \texttt(xts) has no multivariate plot function implemented.) <>= par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The two charts show a multivariate time series plotted in a single frame. We use for the plot the functions as implemented in the packages \texttt{PerformanceAnalytics}, and \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multiple Plots} Multiple plots enormously simplify the display of different curves in multiple panels. These are the ideal plots when it comes to the task to create a quick overview over several time series. Multiple plotting is exclusive to \texttt{timeSeries} objects, \texttt(xts) and \texttt{PerformanceAnalytics} offer no multiple plotting tool. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{As long as we plot less than 4 time series in a multivariate frame, we get a one column layout. Annotations show by default only the y-labels which are taken from the colmun names of the time series to be drawn. Feel free to add main title, sub title, and x-label calling the function \texttt{title}}. \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} For more than four curves the frames of the plot design are arranged in two columns. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the layout how it is created for six curves. There are two columns with three panels to the left and also three panels to the right. Note, it is easily possible to adapt the margin sizes and the gap between the two columns of plots calling the function \texttt{mar} and \texttt{oma}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} If you like a design with a small gap between the panel rows, you can modify the \texttt{mar} parameter to introduce a small gap, here with a width of 0.3. Feel free to modify it. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in a multiple plot the \texttt{mar} parameter setting to create a small gap between the rows of the individual charts. This lets a plot look more elegant.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Combining Single Plots} You can also create your own multiple panel plots. Just combine single panels in an array of rows and columns using the parameter settings for \texttt{mfrow}, \texttt{mfcol}, and \texttt{mar}. <>= par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in single plots the \texttt{mfrow} and \texttt{mar} parameter settings to place single plots either row by row or column by column. Here, \texttt{mfrow} and \texttt{mfcol} to the job. In this case a vector of the form \texttt{c(nr, nc)} draws subsequent figures in an nr-by-nc array on the device by columns (mfcol) or rows (mfrow), respectively.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Layout of Single Plots} There is another option in R to create panel layouts, not necessarilly in an rectangular array. Have a look to the help page of the function \texttt{layout}, her comes a simple example. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} you can devide the plot device in rows and columns expressed in matrix form defined by the argument \texttt{mat}. } \end{figure} \end{center} %To be more specific, the graph \texttt{i} is allocated a region composed %from a subset of these rows and columns, based on the rows and columns %in which \texttt{i} occurs in the matrix \texttt{mat}. %The argument \texttt{layout.show(n)} plots (part of) the current layout, %namely the outlines of the next \texttt{n} figures. % ----------------------------------------------------------------------------- \pagebreak \subsection*{} In addition widths and heights of the layout can be different from row to row, and/or from column to column. The sizes are expressed by the arguments \texttt{widths} and \texttt{heights} of the function \texttt{layout}. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} one can also define the widths and heights of the columns and rows.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Bivariate Scatter Plots} For historical reasons, like in the function \texttt{plot.ts}, there is also the option to create an scatter plot from two univariaye time series. Since this is not a "true" time series plot, we will not go in further detail for this display. <>= par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{If \texttt(x) and \texttt(y) are univariate time series, then the plot function will display a scatter plot.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 3 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Time Axis Layout} The function \texttt{plot} comes with three options to design the time axis layout: \texttt{"pretty"}, \texttt{"chic"}, and \emph{} (note this not a string argument. \emph{} should just abbreviate that we have to input character strings of fully arbitray \texttt{at} positions. For the first two options the style of the axis annotation is generated in a fully automated way, whereas in the tailored case each tick on the axis to be user defined. \noindent The \emph{"pretty"} time axis layout is the default setting for the argument \texttt{at}. Internally the function \texttt{pretty} is used to compute a sequence of about \texttt{n+1} equally spaced round values which cover the range of the values in the time stamps \texttt{time(x)} of the series \texttt{x}. The values are chosen so that they are 1, 2 or 5 times a power of 10. \noindent The \emph{"chic"} time axis layout is the alternative setting for the argument \texttt{at}. Internally the function \texttt{axTicksByTime} from the package \texttt{xts} is used to compute the sequence of axis positions and the format labels. \noindent The \emph{} time axis layout leaves it to the user to specify by himself the positions (at), the time label formatting (format), and the minor tick marks (minor.ticks). % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis: "pretty" or "chic"?} Our plotting function comes with two axis-styles. The first is called \texttt{"pretty"}, which is the default style, and calculates positions from R's base function \texttt{pretty}. The other is called \texttt{"chic"} to remember its origin, arising from the "Chicago" \texttt{xts} package. \vspace{-0.7cm} <>= par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") @ \vspace{-0.3cm} \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the two flavours from the \texttt{at="pretty"} and the \texttt{"chic"} axis designs. The first style uses the function \texttt{pretty} from R's \texttt{base} environment to compute the positions for the major ticks. The second style uses the function \texttt{axTicksByTime} from the \texttt{xts} package to compute x-axis tick mark locations by time. In the upper graph the minor ticks are calendar days, whereas in the lower graph weekdays are drawn (therefore the small gaps between the minor ticks become visible). Note, the time series is in both cases an object of class \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us plot a multivariate 3-column time series in a single panel. Again we compare the outcome of the \texttt{"pretty"} and the \texttt{"chic"} axis style. <>= par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The only difference of this graph compared to the previous is the fact that we consider here a multivariate time series. Three curves are shown in a common plot. Note, when using the \texttt{"chic"} style, then the vertical gridlines are narrower compared to the \texttt{"pretty"} style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Selecting Minor Tick Marks} The \texttt{"pretty"} style allows to draw the minor tick marks on different time scales. These are: \texttt{"day"}, \texttt{"week"}, and \texttt{"month"}. <>= par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{It is worth to note that a good selection of minor tick marks makes a plot much better readable.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - One Column Multiple Plot Layout} In the multiple plot layout the axis are drawn along the same principles as they are drawn in the case of the single plot layout. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a one column multiple plot layout. The one column layout is generated for up to four time series. When the multivariate time series has more then four time series then a two column layout is displayed. It is up to you which axis style you prefer, \texttt{at="pretty"} or \texttt{at="chic".}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Two Column Multiple Plot Layout} Concerning the style of the axis, there is now difference between the one and two column plot designs. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we have more then four time series, then the display will be generated in two columns. Note, it is possible to modify the width of the gap between the two columns.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Tick and Format Layout: The axis style} The third alternative to style the axis offers the users to define format positions according to his preferences. Here comes an example: <>= par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows plots with user tailored positions and formatted axis labels.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 4 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Annotations} Plot annotations are elelents which can be added to plots or completely discarded. To discard all annotations you have to set \texttt{ann=FALSE} in the argument list of the timSeries \texttt{plot} function. To display annotation you can call the function \texttt{title}. This allows to add the main title, the sub title, and the x- and y-labels to a plot. Together with the appropriate character strings, you can also specify the placement of these annotations by the arguments \texttt{line} and \texttt{outer}. There are additional functions to add annotations to a plot. These are \texttt{text} and \texttt{mtext}. % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding all Annotations} In a default plot we display only the value-label(s) which are taken from the units or column names of the time time series object. Title, sub title, and time-label are not shown. To discard the appearance of all annotations on a plot you have to set the plot argument \texttt{ann=FALSE}. <>= par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a plot where all annotations have been discarded. Now feel free to add your own annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding Title and Labels} To add a main title, a sub, title, and x- and y-labels you can call the function \texttt{title}. <>= par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph displays in a two by two array four single plots. We have added title and x-lable annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Axis Font Size} Sometimes the axis font size may be considered as too small or too large. Then you can use the plot argument \texttt{cex.axis} to upsize or downsize the font. <>= par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This is an example how to change the size of the axis labels relatively to its default value. The upper graph shows a font size decreased by 20\%, the lower graph a font size increased by 25\%. You can proceed in the same way when using the \texttt{"pretty"} axis style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Flipping Value Axes} Flipping every second axis label in a multiple plot from left to rigth might be meaningful in the case when axis labels overwrite themselves. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows an one column multiple plot, where the axis of the middle panel is flipped from the left to the right. Note, the same procedure can also be applied two two column multiple plots.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 5 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Decorations} There exist several options to decorate plots in different ways. Plot types (lines, points, horizontal bars, etc.) can be modified, colors can be changed, lines can be modified by style and seize, points can be selected by symbol and size. \noindent In the following we will give some examples \begin{itemize} \item Modifying Types \item Changing Colors by Names \item Changing Colors by Color Palettes \item Changing Line Styles \item Modifying Line Widths \item Changing Plot Symbols \item Modifying Plot Symbol Sizes \item Discarding Grid Lines \item Drawing a Box \end{itemize} \noindent to show a few of the many types of cdecorations. Play around to achieve your perfect layout. % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Types} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{If we like to consider in a multiple plot for each panel its own plot style then we can set the parameter \texttt{type}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Names} Colors can be changed in several ways. Just by their numbers, e.g. 1 (black), 2 (red), 3 (green) etc., or by name, e.g. "black", "red", "green", etc. or by using well designed color palettes. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows how to assign colors by name in the case of a multiple plot. You can do it in the same way setting \texttt{plot.type="s"} if you like to display all three curves in a common single plot.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Color Palettes} When the number of curves increases, then it can become quite difficult to find a set of nice colors. In such cases it is convenient to select the colors from color palettes. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows an example of six curves diplayed in a sequence of reds. For this we called the function \texttt{seqPalette}.} \end{figure} \end{center} \pagebreak \begin{verbatim} FUNCTION: COLOUR PALETTE rainbowPalette Contiguous rainbow colour palette heatPalette Contiguous heat colour palette terrainPalette Contiguous terrain colour palette topoPalette Contiguous topo colour palette cmPalette Contiguous cm colour palette greyPalette R's gamma-corrected gray palette timPalette Tim's MATLAB-like colour palette rampPalette Colour ramp palettes seqPalette Sequential colour brewer palettes divPalette Diverging colour brewer palettes qualiPalette Qualified colour brewer palettes focusPalette Red, green and blue focus palettes monoPalette Red, green and blue mono palettes \end{verbatim} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Line Styles} In multiple plot to each curve an own line style \texttt{lty} can be assigned: 0 "blank", 1 "solid", 2 "dashed", 3 "dotted", 4 "dotdash", 5 "longdash", or 6 "twodash". <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we like to print plots in black and white, then its makes much sense to use different line types so that we can distinguish the curves one from each other.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Line Widths} Not only the line type, but also the line width can be modified for each curve in an individual kind. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows three line widths, the upper's curve width is thick, the middle's curve width is medium, and the lowest's curve width is the thinnest one.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Plot Symbols} To use different plot symbols we can assign them by the parameter \texttt{pch}. Don't forget also to set \texttt{type="p"}. %<>= %par(mfrow=c(1, 1)) %tS3weekly <- align(tS3, by="1w") %plot(tS3weekly, plot.type="s", type="p", col=1:3, pch=21:23, at="chic") %@ \medskip %\begin{center} %\begin{figure}[h] %<>= %<> %@ %\caption{This plot shows how to assign different plot symbols to the curves %in a single plot.} %\end{figure} %\end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Plot Symbol Sizes} The argument \texttt{cex.pch} allows to increase or decrease plot symbol sizes with respect to the current plot symbol size. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This plot show how to change the size of plot symbols in a single plot setting the argument \texttt{cex.pch}. Note, for each curve its own size can be set. The same approach can be used also for multiple plots.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding Grid Lines} By default grid lines are displayed. To discard the grid lines from the plot set the arguments \texttt{grid=FALSE}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default a grid is drawn on top of the plot. You can remove it by setting the argument \texttt{grid=FALSE}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Drawing a Box} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default the plots are displayed as frame plots. This means that the graphs are surrounded by a box. This box can be discarded setting the plot argument \texttt{frame.plot=FALSE}.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 6 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{The Panel Function} Multiple plots are very powerful plotting designs. Each panel in a graph can individually tailored by the user. By default each curve in a panel is generated by the function \texttt{lines}. You can define your own panel function(s) by setting the plot argument \texttt{panel} to a user dfined functions. In the following we will show three examples. % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding a Horizental Zero Line} In this example we show how to write a panel function which allows to add a horizontal zero line to each plot panel. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with curves having a horizontal zero reference line.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an Rug to Multiple Return Plots} This example shows how to add in each panel rugs to the righ Y-axis. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with rugs on the right Y-axis.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an EMA to Multiple Index Plots} This example shows how to add an EMA indicator to each plot panel. The \texttt{emaTA()} function is provided by the \texttt{fTrading} package. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{A multiple graph with EMA indicators in each panel.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 7 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Conclusions} The \texttt{plot} function in the \texttt{timeSeries} package is a very powerful tool to create plots from time series objects. This includes to display univariate and multivariate time series in single and multiple panels, to select from two styles for the time-axis or even to tailor positions and formats according to his own needs, and to modifiy annotations and decorations of plots. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 8 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Appendix} In the appendix we have summarized graphs and tables which are useful tools to create plots. We have reprinted the default color table from R, we have summarized several color palettes as available in the \texttt{fBasics} package and other contributed R packages, and two tables with font characters and plot symbols. % ----------------------------------------------------------------------------- \pagebreak \subsection{Margins: mar and oma} <>= # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") @ \pagebreak \subsection*{} \begin{center} <>= <> @ \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Character Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{characterTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Palettes I} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes1Fig.pdf} \end{figure} \end{center} \pagebreak \subsection{Color Palettes II} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes2Fig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Symbol Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{symbolTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "pretty"} <>= FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS @ % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "chic"} <>= axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } @ <>= ticks <- axTicksByTime(as.xts(tS1)) ticks @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About the Authors} % ----------------------------------------------------------------------------- % Diethelm Wuertz \noindent\textbf{Diethelm W\"urtz} is professor at the Institute for Theoretical Physics, ITP, and for the Curriculum Computational Science and Engineering, CSE, at the Swiss Federal Institute of Technology in Zurich. He teaches Econophysics at ITP and supervises seminars in Financial Engineering. Diethelm is senior partner of Finance Online, an ETH spin-off company in Zurich, and co-founder of the Rmetrics Association in Zurich.\\ % ----------------------------------------------------------------------------- % Tobias Setz \noindent \textbf{Tobias Setz} has a Bachelor and Master in Computational Science from ETH in Zurich and has contributed with his Thesis projects on wavelet analytics and Bayesian change point analytics to this handbook. He is now a PhD student in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics.\\ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About Rmetrics} \noindent\emph{Rmetrics Open Source Project} With hundreds of functions built on modern methods, the Rmetrics open source software combines exploratory data analysis, statistical modelling and rapid model prototyping. The R/Rmetrics packages are embedded in R, building an environment which creates a first class system for applications in teaching statistics and finance. Rmetrics covers Time Series Econometrics, Hypothesis Testing, GARCH Modelling and Volatility Forecasting, Extreme Value Theory and Copulae, Pricing of Derivatives, Portfolio Analysis, Design and Optimization, and much more. \noindent\emph{The Rmetrics Association}\\ is a non-profit taking association working in the public interest. The Rmetrics Association provides support for innovations in financial computing. We believe that the Rmetrics Open Source software has become a valuable educational tool and that it is worth ensuring its continued development and the development of future innovations in software for statistical and computational research in finance. Rmetrics provides a reference point for individuals and institutions that want to support or interact with the Rmetrics development community. Rmetrics encourages students to participate in Rmetrics' activities in the context of Student Internships. \noindent\emph{Rmetrics Software Evalution}\\ If you like to get an impression of the size and quality of the Open Source Rmetrics Program have a look on the Ohloh Rmetrics Software Evaluation. The Evalutions gives an overview about the Software Development (Code Analysis, Estimated Cost), the people behind it, and its community. \noindent\emph{Contributions to Rmetrics}\\ are coming from several educuational institutions world wide. These include the Rmetrics web site and documentation project supported by ITP/CSE ETH Zurich, the organization of Summerschools and Workshops supported by ITP/CSE ETH Zurich, the R-sig-Finance Help and Mailing List, supported by SfS ETH Zurich, the R-forge development server, supported by University of Economics in Vienna, CRAN Test and Distribution Server for R software, supported by University of Economics Vienna, the Debian Linux integration supported by the Debian Association. Many thanks to all behind these projects who gave us continuous support over the last years.\\ \noindent Rmetrics Association\\ www.rmetrics.org % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak % References \begin{thebibliography}{99} \bibitem{zoo} Achim Zeileis and Gabor Grothendieck (2005): \emph{ zoo: S3 Infrastructure for Regular and Irregular Time Series.} Journal of Statistical Software, 14(6), 1-27. URL http://www.jstatsoft.org/v14/i06/ \bibitem{tseries} Adrian Trapletti and Kurt Hornik (2007): \emph{tseries: Time Series Analysis and Computational Finance.} R package version 0.10-11. \bibitem{rmetrics} Diethelm W\"urtz et al. (2007): \emph{Rmetrics: Rmetrics - Financial Engineering and Computational Finance.} R package version 260.72. http://www.rmetrics.org \bibitem{ISO} International Organization for Standardization (2004): \emph{ISO 8601: Data elements and interchage formats --- Information interchange --- Representation of dates and time} URL http://www.iso.org \bibitem{R} R Development Core Team: \emph{R: A Language and Environment for Statistical Computing}, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org \bibitem{quantmod} Jeffrey A. Ryan (2008): \emph{quantmod: Quantitative Financial Modelling Framework.} R package version 0.3-5. URL http://www.quantmod.com URL http://r-forge.r-project.org/projects/quantmod \end{thebibliography} \end{document} timeSeries/inst/doc/timeSeriesPlot.R0000644000176000001440000006274112620124760017242 0ustar ripleyusers### R code from vignette source 'timeSeriesPlot.Rnw' ################################################### ### code chunk number 1: environment ################################################### Sys.setlocale("LC_ALL", "C") ################################################### ### code chunk number 2: library ################################################### require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes ################################################### ### code chunk number 3: univariateSingle ################################################### par(mfrow=c(1, 1)) plot(tS1) ################################################### ### code chunk number 4: univariateSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS1) ################################################### ### code chunk number 5: univariateSingle2 ################################################### require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) ################################################### ### code chunk number 6: univariateSingle2Plot ################################################### require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) ################################################### ### code chunk number 7: multivariateSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s") ################################################### ### code chunk number 8: multivariateSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s") ################################################### ### code chunk number 9: multivariateSingle2 ################################################### par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") ################################################### ### code chunk number 10: multivariateSingle2Plot ################################################### par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") ################################################### ### code chunk number 11: oneColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m") ################################################### ### code chunk number 12: oneColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m") ################################################### ### code chunk number 13: twoColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m") ################################################### ### code chunk number 14: twoColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m") ################################################### ### code chunk number 15: gapMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) ################################################### ### code chunk number 16: gapMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) ################################################### ### code chunk number 17: combineSingle ################################################### par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) ################################################### ### code chunk number 18: combineSinglePlot ################################################### par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) ################################################### ### code chunk number 19: layoutSingle ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 20: layoutSinglePlot ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 21: layout2Single ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 22: layout2SinglePlot ################################################### nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) ################################################### ### code chunk number 23: scatter ################################################### par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) ################################################### ### code chunk number 24: scatterPlot ################################################### par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) ################################################### ### code chunk number 25: pretty ################################################### par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") ################################################### ### code chunk number 26: prettyPlot ################################################### par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") ################################################### ### code chunk number 27: chicUnivariateSingle ################################################### par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") ################################################### ### code chunk number 28: chicUnivariateSinglePlot ################################################### par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") ################################################### ### code chunk number 29: minorTicks ################################################### par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") ################################################### ### code chunk number 30: minorTicksPlot ################################################### par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") ################################################### ### code chunk number 31: chicOneColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") ################################################### ### code chunk number 32: chicOneColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") ################################################### ### code chunk number 33: chicTwoColMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") ################################################### ### code chunk number 34: chicTwoColMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") ################################################### ### code chunk number 35: tailoredAxis ################################################### par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) ################################################### ### code chunk number 36: tailoredAxisPlot ################################################### par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) ################################################### ### code chunk number 37: annSingle ################################################### par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") ################################################### ### code chunk number 38: annSinglePlot ################################################### par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") ################################################### ### code chunk number 39: titleSingle ################################################### par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") ################################################### ### code chunk number 40: titleSinglePlot ################################################### par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") ################################################### ### code chunk number 41: axisFontSize ################################################### par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) ################################################### ### code chunk number 42: axisFontSizePlot ################################################### par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) ################################################### ### code chunk number 43: flipAxisOne ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) ################################################### ### code chunk number 44: flipAxisOnePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) ################################################### ### code chunk number 45: typeMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") ################################################### ### code chunk number 46: typeMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") ################################################### ### code chunk number 47: colorNamesMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) ################################################### ### code chunk number 48: colorNamesMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) ################################################### ### code chunk number 49: palettesMultiple ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") ################################################### ### code chunk number 50: palettesMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") ################################################### ### code chunk number 51: ltyMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") ################################################### ### code chunk number 52: ltyMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") ################################################### ### code chunk number 53: lwdMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") ################################################### ### code chunk number 54: lwdMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") ################################################### ### code chunk number 55: symbolsSizeMultiple ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") ################################################### ### code chunk number 56: symbolsSizeMultiplePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") ################################################### ### code chunk number 57: gridSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 58: gridSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 59: noBoxSingle ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid ################################################### ### code chunk number 60: gridSinglePlot ################################################### par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) ################################################### ### code chunk number 61: horizMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") ################################################### ### code chunk number 62: horizMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") ################################################### ### code chunk number 63: rugMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") ################################################### ### code chunk number 64: rugMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") ################################################### ### code chunk number 65: emaMultiple ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") ################################################### ### code chunk number 66: emaMultiplePlot ################################################### par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") ################################################### ### code chunk number 67: margins ################################################### # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") ################################################### ### code chunk number 68: marginsPlot ################################################### # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") ################################################### ### code chunk number 69: prettyAppendix ################################################### FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS ################################################### ### code chunk number 70: axTicks ################################################### axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } 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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.mathOps <- function() { # RUnit Test: tS = dummySeries(format = "counts") tS tS - 2 log(abs(tS)) diff(tS) scale(tS) tS = dummySeries() tS tS - 2 log(abs(tS)) diff(tS) scale(tS) } ################################################################################ timeSeries/inst/unitTests/runit.cor.R0000644000176000001440000000211712620124746017445 0ustar ripleyusers # 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.cor.timeSeries = function() { # RUnit Test: # Signal Series tS = dummySeries(format = "counts") tS cor(tS) cov(tS) # timeDate Series: tS = dummySeries() tS cor(tS) cov(tS) } ################################################################################ timeSeries/inst/unitTests/Makefile0000644000176000001440000000042412633465062017041 0ustar ripleyusersPKG=timeSeries TOP=../.. SUITE=doRUnit.R R=R all: inst test inst: # Install package -- but where ?? -- will that be in R_LIBS ? cd ${TOP}/..;\ ${R} CMD INSTALL ${PKG} test: # Run unit tests export RCMDCHECK=FALSE;\ cd ${TOP}/tests;\ ${R} --vanilla --slave < ${SUITE} timeSeries/inst/unitTests/runit.spreads.R0000644000176000001440000000177612620124746020335 0ustar ripleyusers # 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.spreads <- function() { # RUnit Test: tS = dummySeries(units = c("Bid", "Ask")) head(tS) midquotes(tS) spreads(tS) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesCoercion.R0000644000176000001440000001554112620124746022422 0ustar ripleyusers # 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() } ################################################################################ timeSeries/inst/unitTests/runit.lag.R0000644000176000001440000000303412620124746017424 0ustar ripleyusers # 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.lag <- function() { # RUnit Test: tS = round(dummySeries(flormat = "counts"), 3)[, 1] tS lag(tS) lag(tS, k = -2:2) lag(tS, k = -2:2, trim = TRUE) tS = round(dummySeries(), 3)[, 1] tS lag(tS) lag(tS, k = -2:2) lag(tS, k = -2:2, trim = TRUE) # check colnames when using multiple lag indexes. data <- matrix(runif(12), ncol = 2) charvec <- rev(paste("2009-0", 1:6, "-01", sep = "")) S <- timeSeries(data, charvec) colnames(S) <- paste("S", 1:2, sep = ".") ts <- lag(S, -1:1) checkIdentical(colnames(ts), c("S.1[-1]", "S.1[0]", "S.1[1]", "S.2[-1]", "S.2[0]", "S.2[1]")) } ################################################################################ timeSeries/inst/unitTests/runit.durations.R0000644000176000001440000000233712620124746020676 0ustar ripleyusers # 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.durations <- function() { # RUnit Test: # Signal Series: tS = sort(sample(dummySeries())[1:6, ]) tS durations(tS) durations(tS, trim = TRUE) durations(tS, trim = TRUE)/(24*3600) # Time Series: tS = sort(sample(dummySeries(format = "counts"))[1:6, ]) tS # BUG !!! # durations(tS) # durations(tS, trim = TRUE) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesPositions.R0000644000176000001440000000513212620124746022643 0ustar ripleyusers # 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.time = function() { # Generate nivariate daily random sequence set.seed(4711) data = round(rnorm(12), 2) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS # Get Positions: POS = time(uTS) POS checkIdentical(charvec, POS) # Return Value: return() } # ------------------------------------------------------------------------------ "test.time<-" = function() { # Generate nivariate daily random sequence set.seed(4711) data = round(rnorm(12), 2) charvec = timeCalendar(2006) uTS = timeSeries(data, charvec, units = "uTS") uTS # Add one Day to Positions: POS = time(uTS) time(uTS) <- POS + 24*3600 uTS # Return Value: return() } # ------------------------------------------------------------------------------ test.timeSeriesOrdering = function() { # sample.timeSeries - Resamples a 'timeSeries' object in time # sort.timeSeries - Sorts reverts a 'timeSeries' object in time # rev.timeSeries - Reverts a 'timeSeries' object in time # start.timeSeries - Extracts start date of a 'timeSeries' object # end.timeSeries - Extracts end date of a 'timeSeries' object # Generate univariate monthly random sequence: set.seed(4711) data = cbind(1:12, round(rnorm(12), 2)) positions = timeCalendar(2006) uTS = timeSeries(data, positions) uTS # Sample/Sort: target = uTS target # current = sort(sample(uTS)) # current # checkIdentical(target, current) # Revert: target = uTS target current = rev(rev(uTS)) current checkTrue(!sum(target - current)) # Start/End date of Series: start(uTS) end(uTS) # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.Omit.R0000644000176000001440000000346712620124746017603 0ustar ripleyusers # 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.naOmitMatrix = function() { x = as.timeSeries(data(LPP2005REC))[1:20, 1:4] colnames(x) = abbreviate(colnames(x), 6) x[1, 1] = NA x[3:4, 2] = NA x[18:20, 4] = NA show(x) timeSeries:::.naOmitMatrix(as.matrix(x)) timeSeries:::.naOmitMatrix(as.matrix(x), "s") timeSeries:::.naOmitMatrix(as.matrix(x), "z") timeSeries:::.naOmitMatrix(as.matrix(x), "ir") timeSeries:::.naOmitMatrix(as.matrix(x), "iz") timeSeries:::.naOmitMatrix(as.matrix(x), "ie") # Return Value: return() } # ------------------------------------------------------------------------------ test.na.omit = function() { x = as.timeSeries(data(LPP2005REC))[1:20, 1:4] colnames(x) = abbreviate(colnames(x), 6) x[1, 1] = NA x[3:4, 2] = NA x[18:20, 4] = NA show(x) na.omit(x) na.omit(x, "s") na.omit(x, "z") na.omit(x, "ir") na.omit(x, "iz") na.omit(x, "ie") # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.periodical.R0000644000176000001440000000163412620124746021000 0ustar ripleyusers # 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.periodical <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.cumulated.R0000644000176000001440000000211512620124746020643 0ustar ripleyusers # 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.cumulated = function() { # RUnit Test: # Signal Series: tS = dummySeries(format = "counts") # problem with Fincenter cumulated(tS) # timeDate Series: tS = dummySeries() cumulated(tS) } ################################################################################ timeSeries/inst/unitTests/runit.NA.R0000644000176000001440000000653612620124746017171 0ustar ripleyusers # 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.removeNA = function() { # Create matrix object: set.seed(1985) M = 5 N = 20 x = matrix(round(rnorm(M*N), 3), ncol = M) colnames(x) = 1:M rownames(x) = 1:N nNA = 10 nCol = trunc(runif(nNA, 1, M+1)) nRow = trunc(runif(nNA, 1, N+1)) for (i in 1:nNA) x[nRow[i], nCol[i]] = NA print(x) ans = removeNA(x) print(ans) # Create data.frame object: x.df = as.data.frame(x) class(x.df) ans = removeNA(x.df) print(ans) class(ans) # Create timeSeries object: tD = timeCalendar(m = 1, d = 1:N) x.tS = timeSeries(x, tD) print(x.tS) ans = removeNA(x.tS) print(ans) class(ans) # Return Value: return() } # ------------------------------------------------------------------------------ test.substituteNA = function() { # Create matrix object: set.seed(1985) M = 5 N = 20 x = matrix(round(rnorm(M*N), 3), ncol = M) colnames(x) = 1:M rownames(x) = 1:N nNA = 10 nCol = trunc(runif(nNA, 1, M+1)) nRow = trunc(runif(nNA, 1, N+1)) for (i in 1:nNA) x[nRow[i], nCol[i]] = NA print(x) # Substitute: ans = substituteNA(x) print(ans) ans = substituteNA(x, "mean") print(ans) ans = substituteNA(x, "median") print(ans) # Create data.frame object: x.df = as.data.frame(x) print(x.df) class(x.df) # Substitute: ans = substituteNA(x.df) print(ans) ans = substituteNA(x.df, "mean") print(ans) ans = substituteNA(x.df, "median") print(ans) # Create timeSeries object: tD = timeCalendar(m = 1, d = 1:N) x.tS = timeSeries(x, tD) print(x.tS) class(x.tS) # Substitute: ans = substituteNA(x.tS) print(ans) ans = substituteNA(x.tS, "mean") print(ans) ans = substituteNA(x.tS, "median") print(ans) # Return Value: return() } # ------------------------------------------------------------------------------ test.interpNA = function() { # Interpolate Column-by-Column # Create matrix object: set.seed(1985) M = 5 N = 20 x = matrix(round(rnorm(M*N), 3), ncol = M) colnames(x) = 1:M rownames(x) = 1:N nNA = 10 nCol = trunc(runif(nNA, 1, M+1)) nRow = trunc(runif(nNA, 1, N+1)) for (i in 1:nNA) x[nRow[i], nCol[i]] = NA print(x) # Interpolate: ans = interpNA(x, "linear") print(ans) ans = interpNA(x, "before") print(ans) ans = interpNA(x, "after") print(ans) # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.as.R0000644000176000001440000000252212620124746017265 0ustar ripleyusers # 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.as <- function() { # RUnit Test: # Note, you can also use ... is(timeSeries(), "timeSeries") # Series ts = dummySeries() x = timeSeries:::.signalSeries(as.matrix(ts)) y = timeSeries:::.timeSeries(as.matrix(ts), as.numeric(time(ts), "sec")) # A vector to a timeSeries as.vector(x) as.vector(x[,1]) as.vector(y) as.vector(y[,1]) # as.numeric: as.numeric(x) as.numeric(x[,1]) as.numeric(y) as.numeric(y[,1]) } ################################################################################ timeSeries/inst/unitTests/runit.bind.R0000644000176000001440000000511412620124746017576 0ustar ripleyusers # 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.bind <- function() { Documentation <- as.character(date()) Title <- "Dummy Series" ts <- dummySeries() ts@documentation <- Documentation ts@title <- Title # -------------------------------------------------------------------------- # if NULL are in args, result identical except @documentation !!! cts <- cbind(ts, NULL) rts <- rbind(ts, NULL) ##> checkTrue(!identical(slot(cts, "documentation")[[1]], Documentation)) ##> checkTrue(!identical(slot(rts, "documentation")[[1]], Documentation)) # ... DW [[1]] removes attributes, check this # ... also take care of the title! # check that the rest is identical cts@documentation <- Documentation rts@documentation <- Documentation cts@title <- Title rts@title <- Title checkIdentical(cts, ts) checkIdentical(rts, ts) # -------------------------------------------------------------------------- ts1 <- ts[seq(1, nrow(ts), by = 2),] ts0 <- ts[seq(2, nrow(ts), by = 2),] # test rbind checkTrue(all(time(rbind(ts1, ts0)) == c(time(ts1),time(ts0)))) # test cbind checkIdentical(as.vector(is.na(cbind(ts1, ts0))), c(rep(c(FALSE, TRUE), 12), rep(c(TRUE, FALSE), 12))) checkTrue(all(time(cbind(ts1, ts0)) == time(ts))) # -------------------------------------------------------------------------- # issues with single number element a <- timeSeries(1, as.Date(0, origin="2010-01-01") ) b <- timeSeries( 2:3, as.Date(1:2, origin="2010-01-01") ) d <- timeSeries( 2:10, as.Date(1:9, origin="2010-01-01") ) cbind(a, b) cbind(b, a) cbind(b, d) cbind(d, b) cbind(a, 1) cbind(b, 1) cbind(a, matrix(1)) cbind(b, matrix(1)) } ################################################################################ timeSeries/inst/unitTests/runit.monthly.R0000644000176000001440000000163112620124746020354 0ustar ripleyusers # 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.monthly <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.returns.R0000644000176000001440000000163112620124746020364 0ustar ripleyusers # 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.returns <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.methods-plot.R0000644000176000001440000000162612620124746021305 0ustar ripleyusers # 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.plot <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.dim.R0000644000176000001440000000344512620124746017440 0ustar ripleyusers # 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.dim.timeSeries = function() { # RUnit Test: # Univariate Case: tS = timeSeries(format = "counts")[, 1] dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Multivariate Case: tS = timeSeries(format = "counts") dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Univariate Case: tS = timeSeries()[, 1] dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) # Multivariate Case: tS = timeSeries() dim(tS) NCOL(tS) NROW(tS) ncol(tS) nrow(tS) dimnames(tS) colnames(tS) rownames(tS) # colnames<- # rownames<- is.array(tS) } ################################################################################ timeSeries/inst/unitTests/runit.timeSeries.R0000644000176000001440000000327612620124746021002 0ustar ripleyusers # 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.signalSeries.Internal <- function() { # RUnit Test: x = rnorm(12) y = rnorm(12) timeSeries:::.signalSeries(as.matrix(x), 1:12) timeSeries:::.signalSeries(as.matrix(cbind(x,y)), 1:12) } # ------------------------------------------------------------------------------ test.timeSeries.Internal <- function() { # this is to test the problem when a ts object is passed to # timeSeries. It seems that as.matrix does not convert the object # to a matrix !!! z <- ts(matrix(rnorm(300), 100, 3), start=c(1961, 1), frequency=12) # class(as.matrix(z)) #<< mts ts and not matrix in R 2.9.0 # Note that is is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check t <- timeSeries(z) checkTrue(identical(as(z, "matrix"), as(t, "matrix"))) } ################################################################################ timeSeries/inst/unitTests/runit.drawdowns.R0000644000176000001440000000234612620124746020676 0ustar ripleyusers # 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.drawdowns <- function() { # RUnit Test: tS = timeSeries( data = matrix(rnorm(200, sd = 1e-3), 100), charvec = format(timeSequence(length.out = 100)) ) tS drawdowns(tS) tS = timeSeries( data = matrix(rnorm(200, sd = 1e-3), 100), charvec = 1:100, format = "counts" ) tS drawdowns(tS) } ################################################################################ timeSeries/inst/unitTests/runit.aggregate.R0000644000176000001440000000447412620124746020620 0ustar ripleyusers # 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.aggregate.timeSeries <- function() { # RUnit Test: # aggregate.timeSeries(x, by = c("monthly", "quarterly"), FUN = colMeans, # units = NULL, ...) # A daily Series: charvec <- timeSequence(length.out = 365) x <- timeSeries(rnorm(365), charvec) # Aggregate Returns Monthly: by <- unique(timeFirstDayInMonth(charvec)) aggregate(x, by, sum, units = "MonthReturns") # Count Monthly Records: aggregate(sign(abs(x)), end(charvec), sum, units = "NoOfRecords") # Aggregate Returns Quarterly: by <- unique(timeLastDayInQuarter(charvec)) aggregate(x, by, sum, units = "QrtReturns") # Another example x <- as.timeSeries(data(LPP2005REC))[,1:4] by <- timeSequence(from = "2006-01-01", to = "2008-01-01", by = "quarter") aggregate(x, by, mean) x <- timeSeries(seq(12), timeCalendar()) # relative from and to to make test work for future years ... from <- timeCalendar(y=getRmetricsOptions("currentYear") - 1, m=1, d=1) to <- timeCalendar(y=getRmetricsOptions("currentYear") + 1, m=1, d=1) by <- timeSequence( from = from, to = to, by = "quarter") x a <- aggregate(x, by, sum) a ### DW here are mismatches - corrected above ... checkEquals(sum(x[1]), a[1]) checkEquals(sum(x[2:4]), a[2]) checkEquals(sum(x[5:7]), a[3]) checkEquals(sum(x[8:10]), a[4]) checkEquals(sum(x[11:12]), a[5]) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesClass.R0000644000176000001440000002602012620124746021720 0ustar ripleyusers # 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.timeSeries = function() { # timeSeries - Creates a 'timeSeries' object from scratch # Settings: setRmetricsOptions(myFinCenter = "GMT") set.seed(4711) data = matrix(round(rnorm(12), 3)) data class(data) charvec = format(timeCalendar(2006)) charvec class(charvec) # Compose Univariate daily random sequence setRmetricsOptions(myFinCenter = "GMT") uTS = timeSeries(data, charvec, units = "uTS") series(uTS) print(uTS) # FinCenter Functionality: timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "GMT") timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "Zurich") timeSeries(data, charvec, units = "uTS", zone = "GMT", FinCenter = "Zurich") timeSeries(data, charvec, units = "uTS", zone = "Zurich", FinCenter = "GMT") # Return Value: return() } # ------------------------------------------------------------------------------ test.readSeries = function() { # readSeries - Reads from a spreadsheet and creates a 'timeSeries' # Load Microsoft Data: data(MSFT) MSFT.df = as.data.frame(MSFT) # Read Data Frame: write.table(MSFT.df, file = "msft.dat.csv", sep = ";") read.table("msft.dat.csv", sep = ";") # Read Time Series: # X = readSeries("msft.dat.csv") # X = X[1:12, ] # class(X) # Show Part of Series: # head(X)[, 1:5] # head(X[, 1:5]) # head(X[, 1:5], 2) # Return Value: return() } # ------------------------------------------------------------------------------ test.returns = function() { # returns - Computes returns from a 'timeSeries' object # Load Time Series: X = MSFT head(X) # returns : OPEN = X[, 1] print(OPEN) MSFT.RET = returns(OPEN) print(MSFT.RET) # Return Value: return() } # ------------------------------------------------------------------------------ test.applySeries = function() { # applySeries - Applies a function to blocks of a 'timeSeries' NA # Return Value: return() } # ------------------------------------------------------------------------------ test.orderStatistics = function() { # orderStatistics - Compute order statistic of a 'timeSeries' # Load Data: X = MSFT head(X) # returns: OPEN = X[, 1] print(OPEN) # ORDER STATISTICS: orderStatistics(OPEN) orderStatistics(X[, -5]) orderStatistics(X[, -5])$Open # Return Value: return() } # ------------------------------------------------------------------------------ test.series = function() { # series - Extracts data slot from 'timeSeries' object # Load Microsoft Data: X = MSFT X = X[1:12, ] class(X) # Return Series: OPEN = X[, 1] OPEN returns(OPEN) # Volatility Series: abs(returns(OPEN)) # Data Matrix: series(OPEN) Y = series(X) Y class(Y) # Position Vector: PO = time(OPEN) PO PX = time(X) PX class(PX) checkEquals( target = sum(as.integer(PO - PX)), current = 0) # Return Value: return() } # ------------------------------------------------------------------------------ test.isUnivariate = function() { # isUnivariate Tests if an object of class 'timeSeries' is univariate # Load Microsoft Data: X = MSFT OPEN = X[, 1] # Is Univariate? checkTrue(!isUnivariate(X)) checkTrue(isUnivariate(OPEN)) checkTrue(isMultivariate(X)) checkTrue(!isMultivariate(OPEN)) # Return Value: return() } # ------------------------------------------------------------------------------ test.isMultivariate = function() { # isMultivariate - Tests if an object of class 'timeSeries' is multivariate # Load Microsoft Data: X = MSFT OPEN = X[, 1] # Is Multivariate? checkTrue(isMultivariate(X)) checkTrue(!isMultivariate(OPEN)) # Return Value: return() } # ------------------------------------------------------------------------------ test.displayMethods = function() { # print.timeSeries Print method for a 'timeSeries' object # plot.timeSeries Plot method for a 'timeSeries' object # lines.timeSeries Lines method for a 'timeSeries' object # points.timeSeries Points method for a 'timeSeries' object ## FIXME(MM) - if we store this -- make it a package data set! ## Microsoft Data: ## MSFT.df = data.frame(matrix(c( ## 20010326, 57.1250, 57.5000, 55.5625, 56.0625, 31559300, ## 20010327, 56.0625, 58.5625, 55.8750, 58.2500, 47567800, ## 20010328, 57.3750, 57.9375, 55.3750, 55.5625, 39340800, ## 20010329, 55.3750, 57.1875, 54.5625, 55.3750, 43492500, ## 20010330, 55.7500, 56.1875, 53.8750, 54.6875, 45600800, ## 20010402, 54.8125, 56.9375, 54.6250, 55.8125, 37962000, ## 20010403, 55.3125, 55.3125, 52.7500, 53.3750, 47093800, ## 20010404, 53.3750, 55.0000, 51.0625, 51.9375, 52023300, ## 20010405, 53.7500, 57.3750, 53.5000, 56.7500, 56682000, ## 20010406, 56.3750, 57.1875, 55.0625, 56.1875, 46311000, ## 20010409, 56.5700, 57.4200, 55.6600, 57.1500, 28147800, ## 20010410, 57.9500, 60.0900, 57.7800, 59.6800, 54599700, ## 20010411, 60.6500, 61.5000, 59.7000, 60.0400, 54939800, ## 20010412, 59.5600, 62.3100, 59.3500, 62.1800, 43760000, ## 20010416, 61.4000, 61.5800, 60.1200, 60.7900, 32928700, ## 20010417, 60.5200, 62.1100, 60.0400, 61.4800, 42574600, ## 20010418, 63.3900, 66.3100, 63.0000, 65.4300, 78348200, ## 20010419, 65.8100, 69.0000, 65.7500, 68.0400, 79687800, ## 20010420, 70.3000, 71.1000, 68.5000, 69.0000, 96459800, ## 20010423, 68.1100, 68.4700, 66.9000, 68.2500, 46085600, ## 20010424, 68.2000, 69.9300, 67.1400, 67.5500, 44588300, ## 20010425, 67.5700, 69.7900, 67.2500, 69.6900, 38372000, ## 20010426, 70.0700, 71.0000, 68.2500, 69.1300, 59368800, ## 20010427, 69.5300, 69.6800, 66.2100, 67.1200, 60786200, ## 20010430, 68.5300, 69.0600, 67.6800, 67.7500, 37184100, ## 20010501, 67.6600, 70.3000, 67.6000, 70.1700, 41851400, ## 20010502, 71.0000, 71.1500, 69.3500, 69.7600, 46432200, ## 20010503, 69.2500, 70.1800, 68.1400, 68.5300, 33136700, ## 20010504, 68.0000, 71.0500, 67.9600, 70.7500, 59769200, ## 20010507, 70.8300, 72.1500, 70.7000, 71.3800, 54678100), ## byrow = TRUE, ncol = 6)) ## colnames(MSFT.df) = c("YYMMDD", "Open", "High", "Low", "Close", "Volume") # Data: X = MSFT X = X[1:12, ] OPEN = X[, 1] # Print: print(X) print(OPEN) # Plot: par(mfrow = c(1, 1)) plot(OPEN, type = "l") # GMT - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "GMT") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC) plot(tS) # Zurich - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "Zurich") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC, zone = "GMT", FinCenter = "Zurich") plot(tS) # New York - Plot: tC = timeCalendar(2006, 1, 1, 0:23, 0, 0, zone = "GMT", FinCenter = "NewYork") tS = timeSeries(data = matrix(rnorm(24), ncol = 1), charvec = tC, zone = "GMT", FinCenter = "NewYork") plot(tS, type = "h") lines (tS, col = "red", lty = 3) points(tS, col = "blue", pch = 19) abline(h=0, col = "grey") # Return Value: return() } # ------------------------------------------------------------------------------ test.dummyDailySeries = function() { # dummyDailySeries - Creates a dummy daily 'timeSeries' object # Create Dummy Time Series: setRmetricsOptions(myFinCenter = "GMT") tS = dummyDailySeries(matrix(rnorm(12))) print(tS) # Return Value: return() } # ------------------------------------------------------------------------------ test.alignDailySeries = function() { # alignDailySeries - Aligns a 'timeSeries' object to new positions # Time Series: setRmetricsOptions(myFinCenter = "GMT") tS = MSFT[1:25, ] print(tS) dim(tS) # Align Daily Series: alignDailySeries(tS, method = "interp") # Align Daily Series: alignDailySeries(tS, method = "fillNA") # Align Daily Series: alignDailySeries(tS, method = "fillNA", include.weekends = TRUE) # Return Value: return() } # ------------------------------------------------------------------------------ ## DW > ## test.ohlcDailyPlot = ## function() ## { ## # ohlcDailyPlot - Plots open–high–low–close bar chart ## ## # Price or Incdex Series: ## setRmetricsOptions(myFinCenter = "GMT") ## tS = MSFT[1:25, ] ## print(tS) ## dim(tS) ## colnames(tS) ## ## # Graph Frame: ## par(mfrow = c(2, 1), cex = 0.7) ## ohlcDailyPlot(tS) ## ## # Return Value: ## return() ## } # ------------------------------------------------------------------------------ test.modelSeries = function() { if (FALSE) { # Move to fArma ... # Undocumented Material: Matrix = cbind(X = rnorm(10), Y = rnorm(10)) Matrix = cbind(Matrix, Z = Matrix[, "Y"] - Matrix[, "X"]) TS = dummyDailySeries(Matrix, units = c("X", "Y", "Z") ) head(TS) .modelSeries(Y ~ ar(2), data = TS, lhs = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE) .modelSeries(Y ~ ar(2), data = as.data.frame(TS), lhs = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, fake = TRUE) .modelSeries(log(abs(Z)) ~ lm(X + sin(Y)), data = TS, lhs = TRUE) require(timeSeries) .modelSeries(Y ~ ar(2), data = rnorm(10)) .modelSeries(Y ~ ar(2), data = as.ts(rnorm(10))) .modelSeries(x ~ arima(2, 0, 1), data = armaSim(n=10)) .modelSeries(~ ar(2), rnorm(10)) # attach(TS) # CHECK # .modelSeries(Y ~ ar(2), lhs = TRUE) .modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10)) .modelSeries(Y ~ ar(2) + garch(1,1), data = rnorm(10), lhs = TRUE) .modelSeries(Y ~ ar(2) + garch(1,1), data = TS, lhs = TRUE) } else { NA } # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.attach.R0000644000176000001440000000200212620124746020117 0ustar ripleyusers # 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.attach.timeSeries = function() { # RUnit Test: # Attach Signal Series tS = timeSeries() attach(tS) SS.1 detach(tS) } ################################################################################ timeSeries/inst/unitTests/runit.time.R0000644000176000001440000000162612620124746017624 0ustar ripleyusers # 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.time <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.model.frame.R0000644000176000001440000000163512620124746021057 0ustar ripleyusers # 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.model.frame <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.methods-summary.R0000644000176000001440000000163112620124746022020 0ustar ripleyusers # 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.summary <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.merge.R0000644000176000001440000000316012620124746017760 0ustar ripleyusers # 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.merge.timeSeries = function() { # RUnit Test: # Time Stamps: x = timeSeries()[,1] x y = timeSeries() y merge(x, y) # Signal Counts: x = timeSeries(format = "counts")[,1] x y = timeSeries(format = "counts") y merge(x, y) x <- dummySeries()[,1] x y <- dummySeries() y merge(x, y) # check that merge method can deal with timeSeries that have # colnames that are invalid data.frame colnames. For example # "S[-1]". data <- matrix(runif(18), ncol = 3) charvec <- rev(paste("2009-0", 1:6, "-01", sep = "")) S <- timeSeries(data, charvec) colnames(S) <- paste("S", 1:3, sep = ".") ts <- merge(S[,2], lag(S[,1], -1:1)) checkIdentical(dim(ts), c(6L,4L)) } ################################################################################ timeSeries/inst/unitTests/runit.subset.R0000644000176000001440000001243112620124746020167 0ustar ripleyusers # 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.subset <- function() { ts <- dummySeries() mat <- as.matrix(ts) # we want the same subset-ting rules as for a matrix # but we always print result in vertical style ! # -------------------------------------------------------------------------- # index checkIdentical( ts[], ts) checkTrue(suppressWarnings(is.na(ts[""]))) checkTrue(is.na(mat[""])) checkIdentical( as.matrix(ts[seq(4),2]), mat[seq(4),2,drop=FALSE]) checkIdentical( as.matrix(ts[rep(FALSE, 3), 1]), mat[rep(FALSE, 3), 1,drop=FALSE]) checkIdentical( as.matrix(ts[FALSE, 1]), mat[FALSE, 1, drop = FALSE]) checkIdentical( as.matrix(ts[rep(TRUE), 2]), mat[rep(TRUE), 2, drop=FALSE]) charvec <- as.character(timeCalendar()[1:3]) checkIdentical( as.matrix(ts[charvec, 1]), mat[charvec, 1, drop = FALSE]) checkIdentical( as.matrix(ts[seq(4),]), mat[seq(4),,drop=FALSE]) checkIdentical( as.matrix(ts[rep(FALSE, 3), ]), mat[rep(FALSE, 3), ,drop=FALSE]) checkIdentical( as.matrix(ts[FALSE, ]), mat[FALSE, ,drop=FALSE]) checkIdentical( as.matrix(ts[rep(TRUE), ]), mat[rep(TRUE), ,drop=FALSE ]) dd <- as.character(time(ts)[1]) checkIdentical( as.matrix(ts[dd, ]), mat[dd, ,drop=FALSE]) checkIdentical( as.matrix(ts[,2]), mat[,2,drop=FALSE]) checkIdentical( as.matrix(ts[2,FALSE]), mat[2,FALSE, drop=FALSE]) # prefer to have an empty timeSeries instead of empty data with row names checkIdentical( as.matrix(ts[,FALSE]), mat[,FALSE, drop = FALSE]) checkIdentical( as.matrix(ts[,TRUE ]), mat[,TRUE ,drop=FALSE]) checkIdentical( as.matrix(ts[, "TS.1"]), mat[, "TS.1", drop = FALSE]) # -------------------------------------------------------------------------- # timeDate checkIdentical( ts[timeCalendar()[1:5], 2], ts[1:5,2]) checkIdentical( ts[timeCalendar()[1:5], ], ts[1:5,]) # -------------------------------------------------------------------------- # logical matrix and timeSeries i <- ts < 0.4 checkException(ts[series(i), ], silent = TRUE) checkException(ts[i, ], silent = TRUE) checkException(mat[series(i), ], silent = TRUE) # it fails as expected checkIdentical( as.matrix(ts[series(i)[,1], ]), mat[series(i)[,1], , drop=FALSE]) checkIdentical( as.matrix(ts[i[,1], ]), mat[series(i)[,1], , drop=FALSE]) checkIdentical( as.matrix(ts[series(i)[,1],1]), mat[series(i)[,1],1,drop=FALSE]) checkIdentical( as.matrix(ts[i[,1],1]), mat[series(i)[,1],1,drop=FALSE]) # this should fail checkException(ts[series(i), 2], silent = TRUE) checkException(ts[i, 2], silent = TRUE) checkException(ts[series(i), 1], silent = TRUE) checkException(ts[series(i),1], silent = TRUE) checkException(ts[i,1], silent = TRUE) checkException(mat[series(i),1], silent = TRUE) checkException(ts[series(i),], silent = TRUE) checkException(mat[series(i),], silent = TRUE) checkIdentical( ts[series(i)], mat[series(i)]) checkIdentical( ts[i], mat[series(i)]) # -------------------------------------------------------------------------- # $,timeSeries method df <- as.data.frame(ts) checkIdentical( ts$TS., df$TS.) checkIdentical( ts$TS.1, df$TS.1) checkIdentical( ts$a, df$a) colnames(ts) <- c("aa", "bb") colnames(df) <- c("aa", "bb") checkIdentical( ts$a, df$a) checkIdentical( ts$b, df$b) } ################################################################################ timeSeries/inst/unitTests/runit.apply.R0000644000176000001440000000243412620124746020011 0ustar ripleyusers # 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.apply <- function() { x = timeSeries(rnorm(90), timeSequence(length.out = 90)) fapply applySeries <- function(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, ...) applySeries(x, from = start(x), to = end(x)) } ################################################################################ timeSeries/inst/unitTests/runit.colCum.R0000644000176000001440000000333212620124746020104 0ustar ripleyusers # 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.colCum <- function() { # RUnit Test: # Signal Series ts <- dummySeries(format = "counts") colCumsums(ts) colCummaxs(ts) colCummins(ts) colCumprods(ts) colCumreturns(ts) # Time Series: ts <- dummySeries() colCumsums(ts) colCummaxs(ts) colCummins(ts) colCumprods(ts) colCumreturns(ts) # check that timeSeries with one row still works ... t <- ts[1,] checkTrue(is(colCumsums(t), "timeSeries")) checkTrue(is(colCummaxs(t), "timeSeries")) checkTrue(is(colCummins(t), "timeSeries")) checkTrue(is(colCumprods(t), "timeSeries")) checkTrue(is(colCumreturns(t), "timeSeries")) checkEquals(nrow(colCumsums(t)), 1) checkEquals(nrow(colCummaxs(t)), 1) checkEquals(nrow(colCummins(t)), 1) checkEquals(nrow(colCumprods(t)), 1) checkEquals(nrow(colCumreturns(t)), 1) } ################################################################################ timeSeries/inst/unitTests/runit.align.R0000644000176000001440000000254712620124746017763 0ustar ripleyusers # 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.align.timeSeries <- function() { # RUnit Test: # .align.timeSeries(x, method = c("before", "after", "interp"), # startOn = "hours", by = "30 m") set.seed(1953) tD = timeCalendar( y = rep(2008, times = 6), m = rep(4, times = 6), d = rep(10:11, each = 3), h = sample(1:23)[1:6], min = sample(1:59)[1:6], s = sample(1:59)[1:6]) tS = timeSeries(rnorm(6), tD) align(tS) align(tS, method="interp") # Note, we should als add an argument to trim NAs } ################################################################################ timeSeries/inst/unitTests/runit.colStats.R0000644000176000001440000000264312620124746020462 0ustar ripleyusers # 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.colStats = function() { # RUnit Test: # Signal Series: tS = dummySeries(format = "counts") tS colStats(tS, mean) colSums(tS) colMeans(tS) colSds(tS) colVars(tS) colSkewness(tS) colKurtosis(tS) colMaxs(tS) colMins(tS) colProds(tS) colQuantiles(tS) # timDate Series: tS = dummySeries() tS colStats(tS, mean) colSums(tS) colMeans(tS) colSds(tS) colVars(tS) colSkewness(tS) colKurtosis(tS) colMaxs(tS) colMins(tS) colProds(tS) colQuantiles(tS) } ################################################################################ timeSeries/inst/unitTests/runit.daily.R0000644000176000001440000000162712620124746017771 0ustar ripleyusers # 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.daily <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.signalCounts.R0000644000176000001440000000255712620124746021343 0ustar ripleyusers # 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.signalCounts <- function() { # RUnit Test: int = c(1, 10, 100, 21, 135) print(timeSeries:::.signalCounts(sample(int))) nc = timeSeries:::.signalCounts(int) nc ns = sample(nc) ns sorted = sort(ns) sorted as.integer(sorted) ns ordered = order(ns) ordered ns[ordered] as.integer(ns[ordered]) timeSeries:::.signalCounts(1:12) timeSeries:::.signalCounts(sample(1:12)) timeSeries:::.signalCounts(timeSeries:::.signalCounts(1:12)) } ################################################################################ timeSeries/inst/unitTests/runTests.R0000644000176000001440000000453312620124746017355 0ustar ripleyuserspkg <- "timeSeries" if(require("RUnit", quietly = TRUE)) { library(package=pkg, character.only = TRUE) if(!(exists("path") && file.exists(path))) path <- system.file("unitTests", package = pkg) ## --- Testing --- ## Define tests testSuite <- defineTestSuite(name = paste(pkg, "unit testing"), dirs = path) if(interactive()) { cat("Now have RUnit Test Suite 'testSuite' for package '", pkg, "' :\n", sep='') str(testSuite) cat('', "Consider doing", "\t tests <- runTestSuite(testSuite)", "\nand later", "\t printTextProtocol(tests)", '', sep = "\n") } else { ## run from shell / Rscript / R CMD Batch / ... ## Run tests <- runTestSuite(testSuite) if(file.access(path, 02) != 0) { ## cannot write to path -> use writable one tdir <- tempfile(paste(pkg, "unitTests", sep="_")) dir.create(tdir) pathReport <- file.path(tdir, "report") cat("RUnit reports are written into ", tdir, "/report.(txt|html)", sep = "") } else { pathReport <- file.path(path, "report") } ## Print Results: printTextProtocol(tests, showDetails = FALSE) printTextProtocol(tests, showDetails = FALSE, fileName = paste(pathReport, "Summary.txt", sep = "")) printTextProtocol(tests, showDetails = TRUE, fileName = paste(pathReport, ".txt", sep = "")) ## Print HTML Version to a File: ## printHTMLProtocol has problems on Mac OS X if (Sys.info()["sysname"] != "Darwin") printHTMLProtocol(tests, fileName = paste(pathReport, ".html", sep = "")) ## stop() if there are any failures i.e. FALSE to unit test. ## This will cause R CMD check to return error and stop tmp <- getErrors(tests) if(tmp$nFail > 0 | tmp$nErr > 0) { stop(paste("\n\nunit testing failed (#test failures: ", tmp$nFail, ", R errors: ", tmp$nErr, ")\n\n", sep="")) } } } else { cat("R package 'RUnit' cannot be loaded -- no unit tests run\n", "for package", pkg,"\n") } ################################################################################ timeSeries/inst/unitTests/runit.na.contiguous.R0000644000176000001440000000245212620124746021460 0ustar ripleyusers # 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.na.contiguous = function() { ## Dummy timeSeries with NAs entries data1 <- matrix(c(NA, 1), ncol = 2) data2 <- matrix(rep(2, 4), ncol = 2) data3 <- matrix(c(NA, 3), ncol = 2) data4 <- matrix(rep(4, 4), ncol = 2) data <- rbind(data1, data2, data3, data4) ts <- timeSeries(data, timeCalendar()[1:6]) ## Find the longest consecutive non-missing values ans <- na.contiguous(ts) check <- getDataPart(ans) dimnames(check) <- NULL checkIdentical(data2, getDataPart(check)) } timeSeries/inst/unitTests/runit.order.R0000644000176000001440000000266212620124746020002 0ustar ripleyusers # 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.order <- function() { # RUnit Test: tS = timeSeries(matrix(rnorm(500), 100), units = sample(LETTERS[1:5])) head(tS) colnames(tS) sortColnames(tS) sampleColnames(tS) orderColnames(tS) statsColnames(tS, FUN = colMeans) pcaColnames(tS) hclustColnames(tS) tS = timeSeries(matrix(rnorm(500), 100), units = sample(LETTERS[1:5]), format = "counts") head(tS) colnames(tS) sortColnames(tS) sampleColnames(tS) orderColnames(tS) statsColnames(tS, FUN = colMeans) pcaColnames(tS) hclustColnames(tS) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesData.R0000644000176000001440000002416512620124746021534 0ustar ripleyusers # 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.diffTimeSeries = function() { # diff.timeSeries - Differences a 'timeSeries' object # Univariate Series: # Multivariate Data Set: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) uTS uTS@recordIDs # Differencing over 1 lag X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = NA) X X@recordIDs # X = diff(x = uTS, lag = 1, diff = 1, trim = TRUE, pad = NA) # X # X@recordIDs X = diff(x = uTS, lag = 1, diff = 1, trim = FALSE, pad = 0) X X@recordIDs # Differencing over 2 lags X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = NA) X X@recordIDs # X = diff(x = uTS, lag = 2, diff = 1, trim = TRUE, pad = NA) # X # X@recordIDs X = diff(x = uTS, lag = 2, diff = 1, trim = FALSE, pad = 0) X X@recordIDs # Differencing twice: # X = diff(x = uTS, lag = 1, diff = 2, trim = FALSE, pad = NA) #ERROR # X # X@recordIDs # X = diff(x = uTS, lag = 2, diff = 2, trim = FALSE, pad = NA) # ERROR # X # X@recordIDs # X = diff(x = uTS, lag = 1, diff = 2, trim = TRUE, pad = NA) # X # X@recordIDs # X = diff(x = uTS, lag = 2, diff = 2, trim = TRUE, pad = NA) # X # X@recordIDs # Return Value: return() } # ------------------------------------------------------------------------------ test.lagTimeSeries = function() { # lag.timeSeries - Lags a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Time Series Lags: X = lag(x = uTS, k = 1, trim = FALSE, units = NULL) X X@recordIDs X = lag(x = uTS, k = c(2,4), trim = FALSE, units = NULL) X X@recordIDs X = lag(x = uTS, k = c(2,4), trim = TRUE, units = NULL) X X@recordIDs X = lag(x = uTS, k = -1:1, trim = FALSE, units = LETTERS[1:3]) X X@recordIDs # Multivariaye Series: diff(mTS, 1, 1) lag(mTS, 1) # Return Value: return() } # ------------------------------------------------------------------------------ test.mergeTimeSeries = function() { # merge.timeSeries - Merges two 'timeSeries' objects # scale.timeSeries - Centers and/or scales a 'timeSeries' object # summary.timeSeries - Summarizes a 'timeDate' object # var.timeSeries - Returns variance for a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Merge: X = uTS Y = log(abs(uTS)) merge(x = X, y = Y, units = "One column") colnames(Y) <- "log" merge(x = X, y = Y, units = c("RN", "logAbsRN")) merge(x = X[-6,], y = Y[-3,], units = c("RN", "logAbsRN")) merge(x = X[2:5,], y = Y[4:6,], units = c("RN", "logAbsRN")) # Return Value: return() } # ------------------------------------------------------------------------------ test.scaleTimeSeries = function() { # scale.timeSeries - Centers and/or scales a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Scale: scale(uTS) scale(mTS) # Return Value: return() } # ------------------------------------------------------------------------------ test.summaryTimeSeries = function() { # summary.timeSeries - Summarizes a 'timeDate' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Summary: summary(uTS) summary(mTS) # Return Value: return() } # ------------------------------------------------------------------------------ test.varTimeSeries = function() { # var.timeSeries - Returns variance for a 'timeSeries' object # Univariate Series: set.seed(4711) data = cbind(RNORM = round(rnorm(6), 2)) charvec = timeCalendar()[1:6] recordIDs = data.frame(IDs = LETTERS[1:6]) uTS = timeSeries(data, charvec, recordIDs = recordIDs) # Multivariate Data Set: set.seed(4711) data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) # Covariance Matrix: var(x = uTS, y = NULL, na.rm = FALSE) var(x = mTS, y = NULL, na.rm = FALSE) # Note, using function cov() fails, since cov() requires an atomic # object as input. # Return Value: return() } # ------------------------------------------------------------------------------ test.mathOpsTimeSeries = function() { # Ops.timeSeries - Arith method for a 'timeSeries' object # abs.timeSeries - Returns abolute values of a 'timeSeries' object # sqrt.timeSeries - Returns sqrt values of a 'timeSeries' object # exp.timeSeries - Returns exponentials of a 'timeSeries' object # log.timeSeries - Returns logarithms of a 'timeSeries' object # quantile.timeSeries - produces sample quantiles of a 'timeSeries' object # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Univariate Ops: uTS < 0 uTS == abs(uTS) # Math Operations: uTS + 5 uTS - 5 100 * uTS uTS / 100 uTS^2 # mathematical Functions: log(abs(uTS)) sqrt(exp(uTS)) # Quantiles: quantile(uTS) quantile(uTS, probs = c(0.9, 0.95)) quantile(uTS, probs = c(0.9, 0.95), type = 5) # Logical Operations: mTS < 0 # Return Value: return() } # ------------------------------------------------------------------------------ test.subsetTimeSeries = function() { # [.timeSeries - subsets of a 'timeSeries' object # cut.timeSeries - cuts a block from a 'timeSeries' object # head.timeSeries - returns the head of a 'timeSeries' object # tail.timeSeries - returns the tail of a 'timeSeries' object # outlier.timeSeries - Removes outliers from a 'timeSeries' object # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Subsets: X = uTS[4:6, ] X X@recordIDs # Head and Tail: head(uTS) tail(uTS) head(mTS) tail(mTS) # Data Subsetting: mTS[, 1] # First Series mTS[4:6, 1] # Second Quarter # Return Value: return() } # ------------------------------------------------------------------------------ test.dimOpsTimeSeries = function() { # dim - Returns the dimension of a 'timeSeries' object # dimnames - Returns the dimension names of a 'timeSeries' object # colnames<-.timeS* - Assigns column names to a 'timeSeries' object # rownames<-.timeS* - Assigns row names to a 'timeSeries' object # is.array.timeSeries - Allows that NCOL and NROW work properly # Univariate Series: setRmetricsOptions(myFinCenter = "GMT") data = matrix(round(rnorm(12), 2)) charvec = format(timeCalendar(2006)) uTS = timeSeries(data, charvec, units = "RNORM") uTS # Multivariate Series: data = cbind(round(rnorm(12), 2), round(rt(12, df = 4), 2) ) charvec = format(timeCalendar(2006)) mTS = timeSeries(data, charvec, units = c("RNORM", "RT")) mTS # Dimension: dim(uTS) == c(12, 1) dimnames(uTS) # Column and Rownames: # X = uTS # colnames(X) = "X" # rownames(X) = as.character(timeCalendar()+24*3600) # X # series(X) # Array: is.array(uTS) # Number of Columns/Rows: NCOL(uTS) NROW(uTS) ncol(uTS) nrow(uTS) # Return Value: return() } ################################################################################ timeSeries/inst/unitTests/runit.rank.R0000644000176000001440000000162612620124746017621 0ustar ripleyusers # 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.rank <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.methods-print.R0000644000176000001440000000162712620124746021464 0ustar ripleyusers # 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.print <- function() { NA } ################################################################################ timeSeries/inst/unitTests/runit.rowCum.R0000644000176000001440000000163012620124746020135 0ustar ripleyusers # 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.rowCum <- function() { NA } ################################################################################ timeSeries/tests/0000755000176000001440000000000012620124746013561 5ustar ripleyuserstimeSeries/tests/doRUnit.R0000644000176000001440000000164212620124746015273 0ustar ripleyusers#### doRUnit.R --- Run RUnit tests ####------------------------------------------------------------------------ ### Origianlly follows Gregor Gojanc's example in CRAN package 'gdata' ### and the corresponding section in the R Wiki: ### http://wiki.r-project.org/rwiki/doku.php?id=developers:runit ### MM: Vastly changed: This should also be "runnable" for *installed* ## package which has no ./tests/ ## ----> put the bulk of the code e.g. in ../inst/unitTests/runTests.R : if(require("RUnit", quietly = TRUE)) { ## --- Setup --- wd <- getwd() pkg <- sub("\\.Rcheck$", '', basename(dirname(wd))) library(package = pkg, character.only=TRUE) path <- system.file("unitTests", package = pkg) stopifnot(file.exists(path), file.info(path.expand(path))$isdir) source(file.path(path, "runTests.R"), echo = TRUE) } ################################################################################ timeSeries/tests/msft.dat.csv0000644000176000001440000003146412620124746016026 0ustar ripleyusers"X.Y..m..d";"Open";"High";"Low";"Close";"Volume" "1";"2000-09-27";63.4375;63.5625;59.8125;60.625;53077800 "2";"2000-09-28";60.8125;61.875;60.625;61.3125;26180200 "3";"2000-09-29";61;61.3125;58.625;60.3125;37026800 "4";"2000-10-02";60.5;60.8125;58.25;59.125;29281200 "5";"2000-10-03";59.5625;59.8125;56.5;56.5625;42687000 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"247";"2001-09-25";52.27;53;50.16;51.3;42470300 "248";"2001-09-26";51.51;51.8;49.55;50.27;29262200 "249";"2001-09-27";50.1;50.68;48;49.96;40595600 timeSeries/NAMESPACE0000644000176000001440000001570112620124746013642 0ustar ripleyusers ################################################ ## import name space ################################################ import("graphics") import("grDevices") import("methods") import("stats") import("utils") import("timeDate") ################################################ ## S4 classes ################################################ exportClasses("index_timeSeries", "timeSeries", "time_timeSeries" ) export("colCummaxs", "colCummins", "colCumprods", "colCumreturns", "colCumsums", "outlier", "returns", "rowCumsums", "series", "series<-", "timeSeries" ) exportMethods("$", "$<-", "+", "-", "Ops", "[", "[<-", "aggregate", "align", "apply", "as.data.frame", "as.list", "as.matrix", "as.ts", "attach", "cbind2", "coerce", "colMeans", "colSums", "colnames", "colnames<-", "comment", "comment<-", "cummax", "cummin", "cumprod", "cumsum", "cut", "diff", "dim", "dim<-", "dimnames", "dimnames<-", "end", "filter", "finCenter", "finCenter<-", "frequency", "getDataPart", "head", "initialize", "is.na", "is.unsorted", "isDaily", "isMonthly", "isQuarterly", "isRegular", "lag", "lines", "merge", "na.contiguous", "na.omit", "names", "names<-", "plot", "points", "print", "quantile", "rank", "rbind2", "rev", "rownames", "rownames<-", "sample", #"scale", "setDataPart", "show", "sort", "start", "str", "t", "tail", "time", "window" ) ################################################ ## S3 classes ################################################ S3method(".DollarNames", "timeSeries") S3method("aggregate", "timeSeries") S3method("as.data.frame", "timeSeries") S3method("as.list", "timeSeries") S3method("as.matrix", "timeSeries") S3method("as.timeSeries", "character") S3method("as.timeSeries", "data.frame") S3method("as.timeSeries", "default") S3method("as.timeSeries", "ts") S3method("as.timeSeries", "zoo") S3method("as.ts", "timeSeries") S3method("cbind", "timeSeries") S3method("cumulated", "default") S3method("cut", "timeSeries") S3method("diff", "timeSeries") S3method("end", "timeSeries") S3method("getUnits", "default") S3method("head", "timeSeries") S3method("lag", "timeSeries") S3method("lines", "timeSeries") S3method("merge", "timeSeries") S3method("na.omit", "timeSeries") S3method("plot", "timeSeries") S3method("points", "timeSeries") S3method("pretty", "timeSeries") S3method("rbind", "timeSeries") S3method("rev", "timeSeries") S3method("scale", "timeSeries") S3method("sort", "timeSeries") S3method("start", "timeSeries") S3method("str", "timeSeries") S3method("tail", "timeSeries") S3method("time", "timeSeries") S3method("time<-", "timeSeries") S3method("window", "timeSeries") ################################################ ## functions ################################################ export( # MODIFY - DELETE/ADD: # "plotOHLC", ".plotOHLC", # .endOfPeriod*, "endOfPeriodBenchmarks", "endOfPeriodSeries", "endOfPeriodStats", # ADD: "returns0", "index2wealth", "daily2monthly", "daily2weekly", "splits", # DELETE # ".plotTimeSeries", # ADD ".xtplot.timeSeries", ".xtsPlot", ".axTicksByTime2", ".endpoints2", ".periodicity2", ".colorwheelPalette", # ADD: "getAttributes", "setAttributes<-", ".appendList" ) export( ".DollarNames.timeSeries", ".aggregate.timeSeries", ".align.timeSeries", ".applySeries", ".as.data.frame.timeSeries", ".as.list.timeSeries", ".as.matrix.timeSeries", ".as.ts.timeSeries", ".cut.timeSeries", ".description", #".diff.timeSeries", ".dollar_assign", ".end.timeSeries", ".extract.turnpointsPastecs", ".fapply", ".findIndex", ".head.timeSeries", ".isOHLC", ".isOHLCV", ".lines.timeSeries", ".lowessSmoother", ".merge.timeSeries", ".na.omit.timeSeries", ".naOmitMatrix", ".old2newRda", ".old2newTimeSeries", ".plot.timeSeries", ".plot.turnpointsPastecs", ".points.timeSeries", ".print.timeSeries", ".rev.timeSeries", ".rollmax.timeSeries", ".rollmean.timeSeries", ".rollmedian.timeSeries", ".rollmin.timeSeries", ".scale.timeSeries", ".signalCounts", ".signalSeries", ".sort.timeSeries", ".splineSmoother", ".start.timeSeries", ".str.timeSeries", ".subset_timeSeries", ".summary.turnpointsPastecs", ".supsmuSmoother", ".tail.timeSeries", ".time.timeSeries", ".timeSeries", ".turnpoints2", ".turnpointsPastecs", ".turnpointsSeries", ".turnpointsStats", ".validity_timeSeries", ".window.timeSeries", "alignDailySeries", "applySeries", "as.timeSeries", "colAvgs", "colKurtosis", "colMaxs", "colMins", "colProds", "colQuantiles", "colSds", "colSkewness", "colStats", "colStdevs", "colVars", "countMonthlyRecords", "cumulated", "description", "drawdowns", "drawdownsStats", "dummyDailySeries", "dummySeries", "durationSeries", "durations", "fapply", "getFinCenter", "getReturns", "getTime", "getUnits", "getUnits.default", "hclustColnames", "interpNA", "is.signalSeries", "is.timeSeries", "isMultivariate", "isUnivariate", "midquoteSeries", "midquotes", "newPositions<-", ".ohlcDailyPlot", "orderColnames", "orderStatistics", "pcaColnames", "readSeries", "removeNA", "returnSeries", "rollDailySeries", "rollMax", "rollMean", "rollMedian", "rollMin", "rollMonthlySeries", "rollMonthlyWindows", "rollStats", "runlengths", "sampleColnames", "seriesData", "seriesPositions", "setFinCenter<-", "setTime<-", "setUnits<-", "smoothLowess", "smoothSpline", "smoothSupsmu", "sortColnames", "spreadSeries", "spreads", "statsColnames", "substituteNA", "time<-", "turns", "turnsStats" ) timeSeries/data/0000755000176000001440000000000012620124746013330 5ustar ripleyuserstimeSeries/data/USDCHF.rda0000644000176000001440000042737012620124746015011 0ustar ripleyusersý7zXZi"Þ6!ÏXÌåÞïþ])TW"änRÊŸãXl΀8[ï Ïp ñ5¡*jÊãŒã«ª×iÚ8-›=.@âo ©-·âsI±Jɬ…±\Ö¥-½GÛAe3ÊС6¶ÒÌ>ªBŒ¥¡á0, `±;ÙÐ(%/âÅ…âg:åJï Êw¦?tÙ?ú¢<_]Îá W‘K{˜wwÆÎt˜úWº ²ŸðvEu, kðz^!E+_kŸMµºí^­©ã'iÞ¦žê×ó\µ–(ñ] Ï,j O¿2öçŠ 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See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # merge,timeSeries,ANY Merges 'timeSeries' object and ANY # merge,timeSeries,missing Merges 'timeSeries' object and missing # merge,timeSeries,timeSeries Merges two 'timeSeries' objects # merge,ANY,timeSeries Merges ANY and 'timeSeries' object ################################################################################ setMethod("merge", c("timeSeries", "ANY"), function(x, y, ...) { callGeneric(x, as(y, "timeSeries"), ...) } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "missing"), function(x, y, ...) { x } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "numeric"), function(x, y, ...) { # Deal with names of numeric vectors units <- names(y) if (is.null(units)) units <- paste((substitute(x)), collapse = ".") if (length(y) == 1) { y <- rep(y, times = nrow(x)) return(merge(x, timeSeries(y, time(x), units = units), ...)) } else if (length(y) == nrow(x)) { return(merge(x, timeSeries(y, time(x), units = units), ...)) } else { stop("number of rows must match") } } ) # ------------------------------------------------------------------------------ setMethod("merge", c("timeSeries", "matrix"), function(x, y, ...) { # deal with names of matrix units <- colnames(y) if (is.null(units)) { units <- paste((substitute(y)), collapse = ".") if ((nc <- ncol(y)) > 1) units <- paste(units, seq(nc), sep = ".") } if (nrow(y) != nrow(x)) stop("number of rows must match") else merge(x, timeSeries(y, time(x), units = units), ...) }) # ------------------------------------------------------------------------------' .merge.timeSeries <- function(x, y, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Merges two objects of class 'timeSeries' # Arguments: # x, y - two objects of class 'timeSeries' # FUNCTION: # Compose Attributes - Documentation : xAttributes <- getAttributes(x) yAttributes <- getAttributes(y) Attributes <- .appendList(xAttributes, yAttributes) Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # Merge: if (is.signalSeries(x) | is.signalSeries(y)) { data <- merge(getDataPart(x), getDataPart(x)) return(timeSeries(data = data, units = colnames(data))) } # Convert to Data Frame tx <- as.numeric(time(x), "sec") ty <- as.numeric(time(y), "sec") df.x <- if (prod(dim(rec.x <- x@recordIDs))) data.frame(positions = tx, getDataPart(x), rec.x) else data.frame(positions = tx, getDataPart(x)) df.y <- if (prod(dim(rec.y <- y@recordIDs))) data.frame(positions = ty, getDataPart(y), rec.y) else data.frame(positions = ty, getDataPart(y)) # Merge as Data Frame: df <- merge(df.x, df.y, all = TRUE) #-> To avoid problems when using invalid data.frame colnames nx <- make.names(colnames(x)) nxrec <- colnames(rec.x) ny <- make.names(colnames(y)) nyrec <- colnames(rec.y) dataIdx <- colnames(df) %in% c(nx, ny) recIdx <- colnames(df) %in% c(nxrec, nyrec) data <- as.matrix(df[,dataIdx, drop=FALSE]) recordIDs <- if (any(recIdx)) df[,recIdx, drop=FALSE] else data.frame() units <- names(df)[dataIdx] charvec <- as.numeric(df[,1]) # Return Value: ans <- timeSeries(data = data, charvec = charvec, units = units, zone = "GMT", FinCenter = finCenter(x), recordIDs = recordIDs) ans@documentation <- Documentation ans } setMethod("merge", c("timeSeries", "timeSeries"), function(x, y, ...) .merge.timeSeries(x, y, ...)) # until UseMethod dispatches S4 methods in 'base' functions merge.timeSeries <- function(x, y, ...) .merge.timeSeries(x, y, ...) # ------------------------------------------------------------------------------ setMethod("merge", c("numeric", "timeSeries"), function(x, y, ...) { # Deal with names of numeric vectors units <- names(x) if (is.null(units)) units <- paste((substitute(x)), collapse = ".") if (length(x) == 1) { x = rep(x, times = nrow(y)) return(merge(timeSeries(x, time(y), units = units), y, ...)) } else if (length(x) == nrow(y)) { return(merge(timeSeries(x, time(y), units = units), y, ...)) } else { stop("number of rows must match") } } ) # ------------------------------------------------------------------------------ setMethod("merge", c("matrix", "timeSeries"), function(x, y, ...) { # Deal with names of matrix units <- colnames(x) if (is.null(units)) { units <- paste((substitute(x)), collapse = ".") if ((nc <- ncol(x)) > 1) units <- paste(units, seq(nc), sep = ".") } if (nrow(x) != nrow(y)) stop("number of rows must match") else merge(timeSeries(x, time(y), units = units), y, ...) }) setMethod("merge", c("ANY", "timeSeries"), function(x, y, ...) { callGeneric(as(x, "timeSeries"), y, ...) } ) ################################################################################ timeSeries/R/statistics-rowCumsums.R0000644000176000001440000000330012620124746017313 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rowCumsums,ANY Computes cumulated sums by row # rowCumsums,timeSeries Computes cumulated sums by row for timeSeries ################################################################################ setMethod("rowCumsums", "ANY", function(x, na.rm = FALSE, ...) { # Transform: if (!inherits(x, 'matrix')) x <- as(x, "matrix") if (na.rm) x <- na.omit(x) ans <- apply(x, 1, cumsum, ...) # special treatment when x has one row because apply returns a vector if (NCOL(x) > 1) t(ans) else matrix(ans, ncol = 1, dimnames = dimnames(x)) }) # ------------------------------------------------------------------------------ setMethod("rowCumsums", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) ################################################################################ timeSeries/R/utils-old2new.R0000644000176000001440000000524412620124746015460 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ .old2newTimeSeries <- function(x) { # Version 1: if ("Data" %in% slotNames(x)) { data <- x@Data charvec <- timeDate(x@positions, zone = x@FinCenter, FinCenter = x@FinCenter) units <- x@units recordIDs <- x@recordIDs title <- x@title documentation <- x@documentation x <- timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, title = title, documentation = documentation) } # Version 2: if ((".Data" %in% slotNames(x)) && is.character(x@positions)) { data <- x@.Data charvec <- timeDate(x@positions, zone = x@FinCenter, FinCenter = x@FinCenter) units <- x@units recordIDs <- x@recordIDs title <- x@title documentation <- x@documentation x <- timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, title = title, documentation = documentation) } x } # ------------------------------------------------------------------------------ ## # Example ## library(timeSeries) ## setwd("~/r/fPortfolio/data") ## rda <- dir() ## sapply(rda, .old2newRda, suffix = "") .old2newRda <- function(file, suffix = "_new") { stopifnot(length(file) == 1) local({ load(file) nm <- ls() lold <- mget(nm, envir = environment(NULL)) test <- sapply(lold, is.timeSeries) lold <- lold[test] lnew <- lapply(lold, .old2newTimeSeries) objects <- names(lold) for (nm in objects) assign(nm, lnew[[nm]]) newFile <- paste(file, suffix, sep = "") save(list = objects, file = newFile) }) invisible(TRUE) } ################################################################################ timeSeries/R/stats-model.frame.R0000644000176000001440000000670512620124746016300 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # model.frame.default Allows to use model.frame for "timeSeries" ################################################################################ # YC : remove model.frame because more problems than benefits. Rely on # default model.frame as long as as.data.frame.timeSeries works in # 'base' function model.frame.default ## setMethod("model.frame.default", signature(data = "timeSeries"), ## function(formula, data = NULL, ## subset = NULL, na.action = na.fail, ## drop.unused.levels = FALSE, xlev = NULL, ...) ## { ## # A function implemented by Diethelm Wuertz ## # Description: ## # Extracting the Environment of a Model Formula ## # Arguments: ## # formula - a model formula ## # data - a 'timeSeries' object ## # Details: ## # Allows to use model.frame() for "timeSeries" objects. ## # Examples: ## # x = as.timeSeries(data(msft.dat))[1:12, ] ## # model.frame( ~ High + Low, data = x) ## # model.frame(Open ~ High + log(Low), data = x) ## # FUNCTION: ## data <- as(data, "data.frame") ## ### model.frame.default(formula, data, ## ### subset, na.action, ## ### drop.unused.levels, ## ### xlev, ...) ## model.frame(formula, data, ...) ## }) ## ## ### # Create Model Frame: ## ## ### format <- data@format ## ## ### FinCenter <- finCenter(data) ## ## ### recordIDs <- data@recordIDs ## ## ### title <- data@title ## ## data <- as(data, "data.frame") ## ## Model <- model.frame(formula, data, ...) ## ## #-> should be in parent.frame? ## ## ### recordIDs <- ## ## ### if (NROW(Model) == NROW(recordIDs)) ## ## ### recordIDs ## ## ### else ## ## ### data.frame() ## ## ### # Convert to timeSeries: ## ## ### ans <- timeSeries(data = as.matrix(Model), ## ## ### charvec = rownames(Model), ## ## ### units = colnames(Model), ## ## ### format = format, ## ## ### FinCenter = FinCenter, ## ## ### recordIDs = recordIDs, ## ## ### title = title, ## ## ### documentation = description() ## ## ### ) ## ## ### # Return value: ## ## ### ans ## ## Model ## ## }) ################################################################################ timeSeries/R/fin-returns.R0000644000176000001440000001130112620124746015213 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # returns,ANY Computes returns from a 'matrix' object # returns,timeSeries Computes returns from a 'timeSeries' object # FUNCTION: DESCRIPTION: # returns0 Compute untrimmed returns # OLD FUNCTIONS: KEEP THESE FUNCTIONS FOR COMPATIBILITY: # returnSeries Deprecated, use returns() # getReturns Deprecated, use returns() ################################################################################ setMethod("returns", "ANY", function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Computes returns from a 'matrix' object # Arguments: # x - data object containing ordered price observations # method - "continuous == "compound" and "discrete" == "simple" # percentage # Note: # To make it conform with PortfolioAnalytics: # "compound" == "continuous" # "simple" == "discrete" # FUNCTION: # Settings: method <- match.arg(method) # Calculate Returns: data <- as.matrix(x) positions <- time(x) if(method == "compound" || method == "continuous") { data <- rbind( data[1, , drop = FALSE]*NA, apply(log(data), 2, diff)) } if(method == "simple" || method == "discrete") { data <- apply(rbind(data, NA*data[1,]), 2, diff) / data data <- rbind(data[1, , drop = FALSE]*NA, data) data <- data[-(length(positions) + 1), , drop = FALSE] } if (percentage) data <- 100*data # Return Value: data } ) # ----------------------------------------------------------------------------- setMethod("returns", "timeSeries", function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, na.rm = TRUE, trim = TRUE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns the returns of an object of class 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # method - # percentage - # na.rm - # trim - # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Make sure that series is ordered: x <- sort(x) # Get Returns: if (na.rm) x <- na.omit(x, ...) series(x) <- returns(as(x, "matrix"), method, percentage) if (trim) x <- na.omit(x, "r") # Preserve Title and Documentation: x@title <- Title x@documentation <- Documentation # Return Value: x } ) ############################################################################### returns0 <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns the untrimmed returns of an object of class 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # FUNCTION: # Compute Untrimmed Returns: x <- returns(x = x, trim = FALSE) x[1, ] <-0 # Return Value: x } ############################################################################### # DEPRECATED: returnSeries <- function(...) { # A function implemented by Diethelm Wuertz # FUNCTION: # .Deprecated("returns", "timeSeries") returns(...) } # ----------------------------------------------------------------------------- getReturns <- function(...) { # A function implemented by Diethelm Wuertz # Description: # Computes returns # FUNCTION: # .Deprecated("returns", "timeSeries") # Return Value: returns(...) } ############################################################################### timeSeries/R/fin-splits.R0000644000176000001440000000726012620124746015040 0ustar ripleyusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # outlier,timeSeries Removes outliers from a 'timeSeries' object ################################################################################ # DW: # We should call this function no longer outlier, much better woud be # splits() since the function tries to detect splits by large outliers. # For outlier detection we should use better methods than just the sd(). # ------------------------------------------------------------------------------ splits <- function(x, sd = 3, complement = TRUE, ...) { # Return Value: outlier(x=x, sd=sd, complement=complement, ...) } # ------------------------------------------------------------------------------ setMethod("outlier", "ANY", function(x, sd = 3, complement = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns outlier splits # Arguments: # x - a numeric vector # sd - a numeric value of standard deviations, e.g. 5 # means that values larger or smaller tahn five # times the standard deviation of the series will # be detected. # complement - a logical flag, should the outlier series # or its complements be returned. # Note: # This function is thought to find splits in financial # price or index series If a price or index is splitted we # observe in the returns a big jump of several standard # deviations which is identified usual as an outlier. # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Find Outliers: SD <- sd * sd(x) if (complement) { ans <- x[x <= SD] } else { ans <- x[x > SD] names(ans) <- as.character(which(x > SD)) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans }) # ------------------------------------------------------------------------------ setMethod("outlier", "timeSeries", function(x, sd = 3, complement = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns outliers in a timeSeries object or the complement # Arguments: # x - an object of class 'timeSeries'. # sd - a numeric value of standard deviations, e.g. 5 # means that values larger or smaller tahn ten # times the standard deviation of the series will # be removed. # complement - a logical flag, should the outler series # or its complement be returned. # FUNCTION: # Check if univariate Series: if (!isUnivariate(x)) stop("Supports only univariate timeSeries Objects") # Find Outliers: SD = sd * sd(x) if (complement) { x = x[abs(x) <= SD,] } else { x = x[abs(x) > SD,] } # Return Value: x }) ################################################################################ timeSeries/R/statistics-orderStatistics.R0000644000176000001440000000301612620124746020321 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # orderStatistics Compute order statistic of a 'timeSeries' object ################################################################################ orderStatistics <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Compute the order statistics for a 'timeSeries object # Value: # A named list with the order statistics for each column of # the inputted series. # FUNCTION: # Order Statistics: td <- time(x) # Return Value: mapply( function(cl, nm) { S <- sort(cl, index.return = TRUE) timeSeries(data = S$x, charvec = td[S$ix], units = nm)}, as.list(x), colnames(x), SIMPLIFY = FALSE) } ################################################################################ timeSeries/R/methods-plot2.R0000644000176000001440000004371712620124746015460 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # .xtplot.timeSeries Plots a 'timeSeries' object # ... support for at = c("pretty", "chic") ############################################################################### # FUNCTION: DESCRIPTION: # .xtsPlot Internal xts plot unitility # .axTicksByTime2 Takes care of "chic" axis creation # .endpoints2 ... determines appropriate axis end points # .periodicity2 ... determines appropriate axis periodicity # .colorwheelPalette ############################################################################### # .plot.timeSeries <- # function( # x, y, FinCenter = NULL, # plot.type = c("multiple", "single"), # format = "auto", at = pretty(x), # widths = 1, heights = 1, # xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, # mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), # oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) # x=dummySeries(); y = NULL; FinCenter = NULL; plot.type = "s" # format = "auto"; at = "pretty"; panel = lines; yax.flip = FALSE # mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1) # oma.multi = c(7.75, 1.1, 6.1, 1.1) # dots <- list() .xtplot.timeSeries <- function( x, y = NULL, FinCenter = NULL, plot.type = c("single", "multiple"), format = "auto", at = c("pretty", "chic"), panel = lines, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(7.75, 1.1, 6.1, 1.1), # oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) { # A function implemented by Diethelm Wuertz # Description: # Plots timeSeries objects - Internal Function # Details: # A modified copy of R's internal 'plotts()' function, # see 'plot.ts()'. # FUNCTION: dots <- list(...) if (is.null(dots$minor.ticks)) minor.ticks <- "auto" else minor.ticks <- dots$minor.ticks if (is.null(dots$type)) type <- "l" else type <- dots$type if (is.null(dots$col)) col <- 1 else col <- dots$col if (is.null(dots$pch)) pch <- 20 else pch <- dots$pch if (is.null(dots$cex)) cex <- 1 else cex <- dots$cex if (is.null(dots$lty)) lty <- 1 else lty <- dots$lty if (is.null(dots$lwd)) lwd <- 1 else lwd <- dots$lwd if (is.null(dots$grid)) grid <- TRUE else grid <- dots$grid if (is.null(dots$frame.plot)) frame.plot <- TRUE else frame.plot <- dots$frame.plot if (is.null(dots$ann)) ann <- TRUE else ann <- dots$ann if (is.null(dots$cex.axis)) cex.axis <- 1 else cex.axis <- dots$cex.axis if (is.null(dots$cex.lab)) cex.lab <- 1 else cex.lab <- dots$cex.lab if (is.null(dots$cex.pch)) cex.pch <- 1 else cex.pch <- dots$cex.pch # Continue ... if (minor.ticks == "auto") minor.ticks <- .periodicity2(x)$units if (at[1] == "chic") minor.ticks = TRUE if (format != "auto") minor.ticks = TRUE # FinCenter - take care of it: if (!is.null(FinCenter)) { finCenter(x) <- FinCenter if (!missing(y)) finCenter(y) <- FinCenter if (is(at, "timeDate")) at@FinCenter <- FinCenter } # Plot Type: plot.type <- plot.type[1] if(isUnivariate(x)) plot.type <- "single" if(is.timeSeries(y)) plot.type <- "scatter" # Axis Positions and Format: AT <- at[1] FORMAT <- format[1] if (x@format == "counts") FORMAT <- "counts" # Main and Labels (We dont use them): main <- xlab <- ylab <- "" nm <- colnames(x) if(length(nm) > 1 && ( plot.type == "single" || plot.type == "s")) nm <- "Values" # Decorations: # if (is.null(col)) col <- 1:ncol(x) # if (col[1] == 0) col = 1 else col <- .colorwheelPalette(ncol(x)) # if (is.null(pch)) pch <- 20 # if (is.null(cex)) cex <- 1 # if (is.null(lty)) lty <- 1 # if (is.null(lwd)) lwd <- 2 if(is.null(type[1])) type <- "l" if (length(type) == 1) type = rep(type, times=NCOL(x)) if (length(col) == 1) col = rep(col, times=NCOL(x)) if (length(pch) == 1) pch = rep(pch, times=NCOL(x)) if (length(cex) == 1) cex = rep(cex, times=NCOL(x)) if (length(lty) == 1) lty = rep(lty, times=NCOL(x)) if (length(lwd) == 1) lwd = rep(lwd, times=NCOL(x)) if (length(cex.pch) == 1) cex.pch = rep(cex.pch, times=NCOL(x)) X <- as.POSIXct(time(x)) Y <- series(x) if (AT == "pretty") { at <- pretty(x) } if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) at <- time(x)[ep] } TIME <- time(x) # SINGLE PLOT: if (plot.type == "single" || plot.type == "s") { # All curves in one Frame: if (is.null(dots$ylim)) ylim <- range(Y, na.rm = TRUE) else ylim <- dots$ylim plot(X, Y[, 1], type= "n", ylim = ylim, axes = FALSE, main = "", xlab = "", ylab = "") for (i in 1:ncol(x)) { lines(X, series(x)[, i], type = type[i], col = col[i], lty = lty[i], lwd = lwd[i], pch = pch[i], cex = cex.pch[i]) } if (ann) { title(main = main, xlab = xlab, ylab = nm, cex.lab = cex.lab) } if (axes) { # X - Axis: if (AT == "counts") { axis(1, cex.axis = cex.axis) } else if (AT == "pretty") { at <- pretty(time(x)) if (FORMAT == "auto") format <- "%Y-%m-%d" if (!is.null(minor.ticks)) { minor.at <- timeSequence(time(x)[1], time(x)[nrow(x)], by = minor.ticks) axis.POSIXct(1, at=minor.at, labels=FALSE, col='#BBBBBB', cex.axis = cex.axis) } axis.POSIXct(1, at = at, format = format, cex.axis = cex.axis) } else if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = cex.axis) axis.POSIXct(1, at = TIME[ep], labels=names(ep), las=1, lwd=1, mgp=c(3, 2, 0), cex.axis = cex.axis) } else { if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = cex.axis) axis.POSIXct(1, at = at, format = format, cex.axis=cex.axis) } # Y - Axis: axis(2, cex.axis = cex.axis) } if (frame.plot) { box("plot") } if(grid) { abline(v = at, lty = 3, col = "darkgrey") grid(NA, NULL, lty = 3, col = "darkgrey") } return(invisible()) } # MULTIPLE PLOT: if (plot.type == "multiple" || plot.type == "m") { nser <- ncol(x) nc <- if (nser > 4) 2 else 1 nr <- ceiling(nser/nc) oldpar <- par(mar = mar.multi, oma = oma.multi, mfcol = c(nr, nc)) on.exit(par(oldpar)) for (i in 1:nser) { plot(X, Y[, i], axes = FALSE, ann = TRUE, type = "n", xlab = "", ylab = "", # log = log, col = col[i], pch = pch[i], lty = lty[i], lwd = lwd[i], cex = cex[i]) panel(X, Y[, i], type = type[i], xlab = "", ylab = "", col = col[i], pch = pch[i], lty = lty[i], lwd = lwd[i], cex = cex.pch[i]) y.side <- if (i%%2 || !yax.flip) 2 else 4 do.xax <- i%%nr == 0 || i == nser if (frame.plot) { box() } if (axes) { axis(y.side, xpd = NA, cex.axis=cex.axis) if (do.xax) { if (AT == "counts") { axis(1, cex.axis = 1.2 * cex.axis) } else if (AT == "pretty") { at <- pretty(time(x)) if (FORMAT == "auto") format <- "%Y-%m-%d" TIME <- time(x) if (!is.null(minor.ticks)) { minor.at <- timeSequence( time(x)[1], time(x)[nrow(x)], by=minor.ticks) axis.POSIXct(1, at=minor.at, labels=FALSE, cex.axis = 1.2 * cex.axis, col='#BBBBBB') } axis.POSIXct(1, at = at, format = format, cex.axis = 1.2 * cex.axis) } else if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) at <- time(x)[ep] format <- attr(ep, "format") formatLabels <- names(ep) TIME <- time(x) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = 1.2 * cex.axis) axis.POSIXct(1, at = TIME[ep], labels=names(ep), las=1, lwd=1, mgp=c(3, 2, 0), cex.axis = 1.2 * cex.axis) } else { TIME <- time(x) if (minor.ticks) axis.POSIXct(1, at=TIME, labels=FALSE, col='#BBBBBB', cex.axis = 1.2 * cex.axis) axis.POSIXct(1, at = at, format = format, cex.axis = 1.2 *cex.axis) } } } if (ann) { mtext(text = nm[i], side = y.side, line = 3, cex = cex.lab) if (do.xax) mtext(xlab, side = 1, line = 3, cex = cex.lab) } if(grid) { abline(v = at, lty = 3, col = "darkgrey") grid(NA, NULL, lty = 3, col = "darkgrey") } } # end of nser loop return(invisible()) } plot # SCATTER PLOT: if (!is.null(y)) { stopifnot (isUnivariate(x)) stopifnot (isUnivariate(y)) plot(series(x), series(y), xlab="", ylab="", col=col, pch=pch, cex=cex) return(invisible()) } } ############################################################################### # Test function for xts-plot-like axis positions and labels. .xtsPlot <- function(x, y=NULL, type = "l", ann = TRUE, axes = TRUE, major.ticks = 'auto', minor.ticks = TRUE, major.format = TRUE, grid = TRUE, box = TRUE, ...) { # A function written by Diethelm Wuertz # Descroption: # A simple example to test the xts functions to generate # nice axis positions and Lebels # Example: # x = 100 * cumulated(LPP2005REC[, 2]); xtsPlot(x) # Settings: # time.scale <- periodicity2(x)$scale ep <- .axTicksByTime2(x, major.ticks, format.labels=major.format) # PLOT COORDS: xycoords <- xy.coords(time(x), x[, 1]) # RAW PLOT: plot(xycoords$x, xycoords$y, type=type, axes=FALSE, ann=FALSE, ...) # ADD GRID: if (grid) { abline(v=xycoords$x[ep], col='grey', lty=3) grid(NA, NULL) } # ADD AXIS: if(axes) { if(minor.ticks) axis(1, at=xycoords$x, labels=FALSE, col='#BBBBBB') axis(1, at=xycoords$x[ep], labels=names(ep), las=1, lwd=1, mgp=c(3,2,0)) axis(2) } # ADD BOX: box() } # ----------------------------------------------------------------------------- # Borrowed from ... # Package: xts # Title: eXtensible Time Series # Version: 0.9-7 # Date: 2013-06-26 # Author: Jeffrey A. Ryan, Joshua M. Ulrich # Maintainer: Jeffrey A. Ryan # License: GPL (>= 2) # URL: http://r-forge.r-project.org/projects/xts/ # Packaged: 2014-01-02 18:00:13 UTC; ripley # NeedsCompilation: yes # Repository: CRAN # Date/Publication: 2014-01-02 19:18:28 .axTicksByTime2 <- function( x, ticks.on='auto', k=1, labels=TRUE, format.labels=TRUE, ends=TRUE, gt = 2, lt = 30, format = "auto") { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeSerie' Object # Example: # x = 100 * cumulated(LPP2005REC[, 2]); .axTicksByTime2(x) tick.opts <- c( "years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c( 10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0,length(tick.opts)), .Names = tick.opts) for(i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], ' ')[[1]] ep <- .endpoints2(x, y[1], as.numeric(y[2])) is[i] <- length(ep) -1 if(is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- .endpoints2(x, cl, ck) if(ends) ep <- ep + c(rep(1,length(ep)-1),0) if (labels) { if(is.logical(format.labels) || is.character(format.labels)) { # format by level of time detail, and platform unix <- ifelse(.Platform$OS.type=="unix", TRUE, FALSE) time.scale <- .periodicity2(x)$scale fmt <- ifelse(unix, '%n%b%n%Y', '%b %Y') if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, '%b %d%n%Y', '%b %d %Y') if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, '%b %d%n%H:%M', '%b %d %H:%M') if (time.scale == "seconds") fmt <- ifelse(unix, '%b %d%n%H:%M:%S', '%b %d %H:%M:%S') if(is.character(format.labels)) fmt <- format.labels if (format != "auto") fmt <- format names(ep) <- format(time(x)[ep], fmt) } else { names(ep) <- as.character(time(x)[ep]) } } attr(ep, "format") <- fmt # Return Value: ep } ################################################################################ .endpoints2 <- function (x, on = c("months", "years", "quarters", "weeks", "days", "hours", "minutes", "seconds"), k = 1) { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeDate' object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .endpoints2(x) stopifnot(is(x, "timeSeries")) x <- time(x) on <- match.arg(on) posix <- as.POSIXct(x, origin="1970-01-01") .posix <- unclass(posix) if (on == "years") { ans <- as.integer(which(diff(as.POSIXlt(posix)$year%/%k + 1) != 0)) } else if (on == "quarters") { ans <- as.integer(which(diff((as.POSIXlt(posix)$mon%/%3) + 1) != 0)) } else if (on == "months") { ans <- as.integer(which(diff(as.POSIXlt(posix)$mon%/%k + 1) != 0)) } else if (on == "weeks") { ans <- as.integer( which(diff((.posix + (3L * 86400L))%/%604800L%/%k + 1) != 0)) } else if (on == "days") { ans <- as.integer(which(diff(.posix%/%86400L%/%k + 1) != 0)) } else if (on == "hours") { ans <- as.integer(which(diff(.posix%/%3600L%/%k + 1) != 0)) } else if (on == "minutes" || on == "mins") { ans <- as.integer(which(diff(.posix%/%60L%/%k + 1) != 0)) } else if (on == "seconds" || on == "secs") { ans <- as.integer(which(diff(.posix%/%k + 1) != 0)) } ans <- c(0, ans, NROW(x)) # Return Value: ans } ############################################################################### .periodicity2 <- function (x) { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeDate' object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .periodicity2(x) # FUNCTION: # Check Argument: stopifnot(is(x, "timeSeries")) x <- time(x) p <- median(diff(as.integer(as.POSIXct(x, origin="1970-01-01")))) if (is.na(p)) stop("cannot calculate periodicity of 1 observation") units <- "days" scale <- "yearly" label <- "year" if (p < 60) { units <- "secs" scale <- "seconds" label <- "second" } else if (p < 3600) { units <- "mins" scale <- "minute" label <- "minute" p <- p/60L } else if (p < 86400) { units <- "hours" scale <- "hourly" label <- "hour" } else if (p == 86400) { scale <- "daily" label <- "day" } else if (p <= 604800) { scale <- "weekly" label <- "week" } else if (p <= 2678400) { scale <- "monthly" label <- "month" } else if (p <= 7948800) { scale <- "quarterly" label <- "quarter" } # Return Value: list( difftime = structure(p, units = units, class = "difftime"), frequency = p, start = start(x), end = end(x), units = units, scale = scale, label = label) } ############################################################################### .colorwheelPalette <- function(n) { # A function implemented by Diethelm Wuertz # FUNCTION: # Color Wheel: orig <- c( "#FFF200", "#FBAA19", "#F26522", "#EF4823", "#ED1D24", "#A9285F", "#662D91", "#4D2F91", "#2E3092", "#00707E", "#00A650", "#8CC63F") orig <- orig[-1] # Splice Wheel if (n == 11) return(orig) rgb.tim <- t(col2rgb(orig)) temp <- matrix(NA, ncol = 3, nrow = n) x <- seq(0, 1, , 11) xg <- seq(0, 1, , n) for (k in 1:3) { hold = spline(x, rgb.tim[, k], n = n)$y hold[hold < 0] = 0 hold[hold > 255] = 255 temp[, k] = round(hold) } ans <- rgb(temp[, 1], temp[, 2], temp[, 3], maxColorValue = 255) # Return Value: ans } ############################################################################### timeSeries/R/timeSeries-readSeries.R0000644000176000001440000001025612620124746017144 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # readSeries Reads a CSV file and creates a 'timeSeries' ################################################################################ # DW: # I think we should add a similar function for writeSeries() using # write.table(). Proceed in the same way as in the case of the read # function. # ------------------------------------------------------------------------------ readSeries <- function(file, header = TRUE, sep = ";", zone = "", FinCenter = "", format, ...) { # A function implemented by Diethelm Wuertz # Description: # Reads from a spreadsheet and creates a 'timeSeries' object # Arguments: # file - the name of the file which the data are to be read # from. Each row of the table appears as one line of the # file. If it does not contain an absolute path, the file # name is relative to the current working directory, # getwd(). Tilde-expansion is performed where supported. # As from R 2.10.0 this can be a compressed file. # header - a logical value indicating whether the file contains # the names of the variables as its first line. If missing, # the value is determined from the file format: header is # set to TRUE if and only if the first row contains one fewer # field than the number of columns. # sep - he field separator character. Values on each line of # the file are separated by this character. If sep = "" (the # default for read.table) the separator is ?white space?, # that is one or more spaces, tabs, newlines or carriage # returns. # zone - the time zone or financial center where the data were # recorded. # FinCenter - a character with the the location of the # financial center named as "continent/city". By default # an empty string which means that internally "GMT" will # be used. # format - the format of the timestamps as recoreded in the # first column of the data in the.. # ... - optional arguments passed to the function read.table(). # Value: # Returns a S4 object of class 'timeSeries'. # Notes: # Note we expect that the header of the spreadsheet file in # the first cell holds the time/date format specification! # FUNCTION: # Read Data: df <- read.table(file = file, header = header, sep = sep, check.names = FALSE, ...) # Get 'timeDate' from first column with header specifying the format charvec <- as.character(df[[1]]) if (missing(format)) format <- names(df)[1] td <- try(timeDate(charvec = charvec, format = format, zone = zone, FinCenter = FinCenter), silent=TRUE) # DW: 2014-09-16 # If sep=";" fails try with sep=",": if (sep ==";" && class(td) == "try-error") { return(readSeries(file, header = header, sep = ",", zone = zone, FinCenter = FinCenter, ...)) } # If format provided in file or with format argument, try to guess it if (all(is.na(td))) warning("Conversion of timestamps to timeDate objects produced only NAs. Are you sure you provided the proper format with argument 'format' or in the header of your file ?") # Extract data data <- as.matrix(df[-1]) # Create Time Series from Data Frame: ans <- timeSeries(data = data, charvec = td) # Return Value: ans } ################################################################################ timeSeries/R/stats-na.contiguous.R0000644000176000001440000000371612620124746016702 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # na.contiguous,timeSeries Finds the longest consecutive of non-missing values ################################################################################ setMethod("na.contiguous", "timeSeries", function(object, ...) { # A function imlemented by Diethelm Wuertz and Yohan Chalabi # Description: # Finds the longest consecutive of non-missing values # Details: # adapted stats:::na.contingous.default to timeSeries objects # Yohan Chalabi # FUNCTION: good <- apply(!is.na(object), 1L, all) if (!sum(good)) stop("all times contain an NA") tt <- cumsum(!good) ln <- sapply(0:max(tt), function(i) sum(tt == i)) seg <- (seq_along(ln)[ln == max(ln)])[1L] - 1 keep <- (tt == seg) st <- min(which(keep)) if (!good[st]) st <- st + 1 en <- max(which(keep)) omit <- integer(0L) n <- NROW(object) if (st > 1) omit <- c(omit, 1L:(st - 1)) if (en < n) omit <- c(omit, (en + 1):n) if (length(omit)) { object <- object[st:en, ] attr(omit, "class") <- "omit" attr(object, "na.action") <- omit } # Return Value: object }) ################################################################################ timeSeries/R/base-diff.R0000644000176000001440000000630512620124746014567 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # diff,timeSeries Differences a 'timeSeries' object ############################################################################### .diff.timeSeries <- function(x, lag = 1, diff = 1, trim = FALSE, pad = NA, ...) { # A function implemented by Diethelm Wuertz # Modified by Yohan Chalabi # Description: # Differences 'timeSeries' objects. # Arguments: # x - a 'timeSeries' object. # lag - an integer indicating which lag to use. # By default 1. # diff - an integer indicating the order of the difference. # By default 1. # trim - a logical. Should NAs at the beginning of the # series be removed? # pad - a umeric value with which NAs should be replaced # at the beginning of the series. # Value: # Returns a differenced object of class 'timeSeries'. # FUNCTION: # Ceck Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Convert: y <- getDataPart(x) # as.matrix(x) # Check NAs: # if (any(is.na(y))) stop("NAs are not allowed in time series") # Difference: z <- diff(y, lag = lag, difference = diff) diffNums = dim(y)[1] - dim(z)[1] # Trim Positions: if (!trim) { zpad <- matrix(0*y[1:diffNums, ] + pad, nrow = diffNums) z <- rbind(zpad, z) } pos <- if (!trim) x@positions else x@positions[-(1:diffNums)] # Record IDs: df <- x@recordIDs if (trim && sum(dim(df)) > 0) { df <- df[-seq.int(diffNums), , drop = FALSE] rownames(df) <- seq.int(NROW(df)) } # Diff Result: ans <- timeSeries(data = z, charvec = pos, units = colnames(z), format = x@format, zone = x@FinCenter, FinCenter = x@FinCenter, recordIDs = df) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ----------------------------------------------------------------------------- setMethod("diff", "timeSeries", function(x, lag = 1, diff = 1, trim = FALSE, pad = NA, ...) .diff.timeSeries(x, lag, diff, trim, pad, ...) ##x <- getDataPart(x) ##callGeneric() ) # until UseMethod dispatches S4 methods in 'base' functions diff.timeSeries <- function(x, ...) .diff.timeSeries(x, ...) ############################################################################### timeSeries/R/stats-aggregate.R0000644000176000001440000001326512620124746016034 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # aggregate,timeSeries Aggregates a 'timeSeries' object # FUNCTION: DESCRIPTION: # daily2monthly Aggregates a daily to monthly 'timeSeries' object # daily2weekly Aggregates a daily to weekly 'timeSeries' object ################################################################################ .aggregate.timeSeries <- function(x, by, FUN, ...) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Aggregates a 'timeSeries' object # Details: # This function can be used to aggregate and coursen a # 'timeSeries' object. # Arguments: # x - a 'timeSeries' object to be aggregated # by - a calendarical block # FUN - function to be applied, by default 'colMeans' # ... - additional argument to be passed to the newly generated # 'timeSeries' object # Value: # Returns a S4 object of class 'timeSeries'. # Examples: # Quarterly Aggregation: # m = matrix(rep(1:12,2), ncol = 2) # ts = timeSeries(m, timeCalendar()) # Y = getRmetricsOptions("currentYear"); Y # from = paste(Y, "04-01", sep = "-"); to = paste(Y+1, "01-01", sep = "-") # by = timeSequence(from, to, by = "quarter") - 24*3600; by # ts; aggregate(ts, by, sum) # Weekly Aggregation: # dates = timeSequence(from = "2009-01-01", to = "2009-02-01", by = "day") # data = 10 * round(matrix(rnorm(2*length(dates)), ncol = 2), 1); data # ts = timeSeries(data = data, charvec = dates) # by = timeSequence(from = "2009-01-08", to = "2009-02-01", by = "week") # by = by - 24*3600; aggregate(ts, by, sum) # FUNCTION: # Check Arguments: if (!((inherits(by, "timeDate") && x@format != "counts") || (is.numeric(by) && x@format == "counts"))) stop("'by' should be of the same class as 'time(x)'", call.=FALSE) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Make sure that x is sorted: if (is.unsorted(x)) x <- sort(x) # Sort and remove double entries in by: by <- unique(sort(by)) INDEX <- findInterval(x@positions, as.numeric(by, "sec") + 1) INDEX <- INDEX + 1 is.na(INDEX) <- !(INDEX <= length(by)) # YC : ncol important to avoid problems of dimension dropped by apply data <- matrix(apply(getDataPart(x), 2, tapply, INDEX, FUN), ncol=ncol(x)) rownames(data) <- as.character(by[unique(na.omit(INDEX))]) colnames(data) <- colnames(x) ans <- timeSeries(data, ...) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } setMethod("aggregate", "timeSeries", function(x, by, FUN, ...) .aggregate.timeSeries(x, by, FUN, ...)) # until UseMethod dispatches S4 methods in 'base' functions aggregate.timeSeries <- function(x, ...) .aggregate.timeSeries(x, ...) ################################################################################ daily2monthly <- function (x, init = FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts daily to monthly series # Arguments: # x - daily time series # init - should the index series converted to a wealth series # FUNCTION: # Save Colnames: colNames <- colnames(x) # Fill to end of Month: Time <- unique(sort(timeLastDayInMonth(time(x)))) x.endOfMonth <- x[nrow(x), ] time(x.endOfMonth) <- rev(Time)[1] x <- rbind(x, x.endOfMonth) x <- alignDailySeries(x, include.weekends=TRUE) # Cut Properly on end of Month: today <- timeDate(Sys.Date()) first <- timeFirstDayInMonth(today) x <- x[time(x) < first, ] Time <- unique(sort(timeLastDayInMonth(time(x)))) # Align Properly: mSeries <- alignDailySeries(x, include.weekends=TRUE) mSeries <- mSeries[Time, ] # Optionally Initialize: if (init) for (i in 1:ncol(mSeries)) mSeries[, i] <- mSeries[, i]/as.vector(mSeries[1, i]) colnames(mSeries) <- colNames # Return Value: mSeries } # ----------------------------------------------------------------------------- daily2weekly <- function(x, startOn="Tue", init=FALSE) { # A function implemented by Diethelm Wuertz # Description: # Converts daily to weekly series # Arguments: # x - daily time series # init - should the index series converted to a wealth series # FUNCTION: # Convert Series: mSeries <- alignDailySeries(x, include.weekends = TRUE) start <- which(dayOfWeek(time(mSeries[1:7, ])) == startOn) mSeries <- mSeries[seq(start, nrow(mSeries), by = 7), ] # Wealth Initialization: if (init) for (i in 1:ncol(mSeries)) mSeries[, i] <- mSeries[, i]/as.vector(mSeries[1, i]) # Return Value: mSeries } ############################################################################### timeSeries/R/timeSeries-isUnivariate.R0000644000176000001440000000314212620124746017515 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # isUnivariate Tests if a 'timeSeries' object is univariate # isMultivariate Tests if a 'timeSeries' object is multivariate ################################################################################ isUnivariate <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Tests if a time series or rectangular object is univariate # FUNCTION: # Return Value: if (NCOL(x) == 1) TRUE else FALSE } # ------------------------------------------------------------------------------ isMultivariate <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Tests if a time series or rectangular object is multivariate # FUNCTION: # Return Value: if (NCOL(x) > 1) TRUE else FALSE } ################################################################################ timeSeries/R/timeSeries-slotDocumentation.R0000644000176000001440000000553412620124746020574 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # getDocumentation # setDocumentation ################################################################################ # FUNCTION: MANAGING ATTRIBUTES # getAttributes # setAttributes<- # INTERNAL FUNCTION: # .appendList ################################################################################ getAttributes <- function (obj) { # A function implemented by Diethelm Wuertz # Description: # FUNCTION: # Check Argument: stopifnot(class(obj) == "timeSeries") # Extract Attributes: ans <- attr(obj@documentation, "Attributes") # Return Value: ans } # ----------------------------------------------------------------------------- `setAttributes<-` <- function(obj, value) { # A function implemented by Diethelm Wuertz # Description: # Example: # obj <- dummySeries(); getAttributes(obj) # setAttributes(obj) <- list(mat=matrix(1:4, ncol=2)); getAttributes(obj) # getAttributes(obj)$mat[[1]] # FUNCTION: # Check Arguments: stopifnot(class(obj) == "timeSeries") stopifnot(is.list(value)) stopifnot(length(value) == 1) stopifnot(!is.null(value)) # Compose New Attribute: name <- names(value) names(value) <- NULL A <- list(value) names(A) <- name # print(A) # Get Already Existing Attribute B <- getAttributes(obj) if(is.null(B)) B <- list() # print(B) # Join Attributes: JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # print(JOINED) # Assign Attribute: attr(obj@documentation, "Attributes") <- JOINED # Return Value: obj } # ----------------------------------------------------------------------------- .appendList <- function (A, B) { # A function implemented by Diethelm Wuertz # Description: # Appends list B to list A # Arguments: # A - first named list element # B - second named list element # FUNCTION: # Append list B to list A JOINED <- sapply(unique(c(names(A), names(B))), function(x) list(c(A[[x]], B[[x]]))) # Return Value: JOINED } ############################################################################### timeSeries/R/data-examples.R0000644000176000001440000000000012620124746015456 0ustar ripleyuserstimeSeries/R/fin-turnpoints.R0000644000176000001440000002363212620124746015750 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # turns Returns turnpoints from a 'timeSeries' # turnsStats Computes statistics of turn points # BUILTIN: DESCRIPTION: # .turnpointsPastecs Builtin function from package pastecs # .extract.turnpointsPastecs Extractor function from package pastecs # .plot.turnpointsPastecs Plot function from package pastecs # .summary.turnpointsPastecs Summary function from package pastecs # DEPRECATED: DESRIPTION: # .turnpoints2 Deprecated, use function turns # .turnpointsSeries Deprecated, use function turns # .turnpointsStats Deprecated, use function turnsStats ################################################################################ # DW: # This function is originally borrowed from the contributesd R package pastecs. # It is not necessary to load pastecs, the functions required are builtin. # ----------------------------------------------------------------------------- turns <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns turnpoints from a timeSeries # Arguments: # x - an univariate timeSeries object, e.g. a price or index series. # ... - arguments passed to the function na.omit() # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x, ...) # Convert to Vector: X <- x x <- as.vector(x) # Turnpoints: ans <- .turnpointsPastecs(x) tp <- .extract.turnpointsPastecs(ans) data <- cbind(x, tp) colnames(data) <- c(colnames(X), "TP") series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ---------------------------------------------------------------------------- turnsStats <- function(x, doplot = TRUE) { # A function implemented by Diethelm Wuertz # Description: # Computes turnpoints statistics # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # doplot - a logical flag, should an optional plot be displayed? # Value: # Returns an object of class turnpoints. # FUNCTION: # Settings stopifnot(isUnivariate(x)) X <- x x <- as.vector(x) # Turnpoints: ans <- .turnpointsPastecs(x) # Summary Statistics: .summary.turnpointsPastecs(ans) # Optional Plot: if(doplot) .plot.turnpointsPastecs(ans) # Return Value: invisible(ans) } ################################################################################ .turnpoints2 <- function(...) { # Deprecated: .Deprecated(new = "turns", package = "timeSeries") # Return Value: turns(...) } # ----------------------------------------------------------------------------- .turnpointsStats <- function(...) { # Deprecated: .Deprecated(new = "turnsStats", package = "timeSeries") # Return Value: turnsStats(...) } ################################################################################ # Package: pastecs # Title: Package for Analysis of Space-Time Ecological Series # Version: 1.3-4 # Date: 2006-11-28 # Author: Frederic Ibanez , # Philippe Grosjean & # Michele Etienne # Description: Regulation, decomposition and analysis of space-time series. # The pastecs library is a PNEC-Art4 and IFREMER # (Benoit Beliaeff ) initiative # to bring PASSTEC 2000 # (http://www.obs-vlfr.fr/~enseigne/anado/passtec/passtec.htm) # functionnalities to R. # URL: http://www.sciviews.org/pastecs # Maintainer: Philippe Grosjean # License: GNU Public Licence 2.0 or above at your convenience # Depends: boot, stats # Packaged: Tue Nov 28 15:33:42 2006; Philippe Grosjean .turnpointsPastecs <- function(x) { data <- deparse(substitute(x)) if (is.null(ncol(x)) == FALSE) stop("Only one series can be treated at a time") # if (exists("is.R") && is.function(is.R) && is.R()) # We are in R # Now done with Depends: field require(stats) x <- as.vector(x) n <- length(x) diffs <- c(x[1]-1, x[1:(n-1)]) != x uniques <- x[diffs] n2 <- length(uniques) poss <- (1:n)[diffs] exaequos <- c(poss[2:n2], n+1) - poss - 1 if (n2 < 3) { # We need at least 3 unique values!!! warning("Less than 3 unique values, no calculation!") nturns <- NA firstispeak <- FALSE peaks <- rep(FALSE, n2) pits <- rep(FALSE, n2) tppos <- NA proba <- NA info <- NA } else { # The following code is faster in R, but do not work all the time! #if (exists("is.R") && is.function(is.R) && is.R()) { # We are in R # ex <- embed(uniques, 3) # Works only in R! # peaks <- c(FALSE, max.col(ex) == 2, FALSE) # pits <- c(FALSE, max.col(-ex) == 2, FALSE) #} else { # We are in S+ m <- n2 - 2 ex <- matrix(uniques[1:m + rep(3:1, rep(m, 3)) - 1], m) peaks <- c(FALSE, apply(ex, 1, max, na.rm=TRUE) == ex[, 2], FALSE) pits <- c(FALSE, apply(ex, 1, min, na.rm=TRUE) == ex[, 2], FALSE) #} tpts <- peaks | pits if (sum(tpts) == 0) { # No turning point nturns <- 0 firstispeak <- FALSE peaks <- rep(FALSE, n2) pits <- rep(FALSE, n2) tppos <- NA proba <- NA info <- NA } else { # This way, we consider the last element of duplicates, as # in PASSTEC 2000 tppos <- (poss + exaequos)[tpts] tptspos <- (1:n2)[tpts] firstispeak <- tptspos[1] == (1:n2)[peaks][1] nturns <- length(tptspos) if (nturns < 2) { inter <- n2 + 1 posinter1 <- tptspos[1] } else { inter <- c(tptspos[2:nturns], n2) - c(1, tptspos[1:(nturns-1)]) + 1 posinter1 <- tptspos - c(1, tptspos[1:(nturns-1)]) } posinter2 <- inter - posinter1 posinter <- pmax(posinter1, posinter2) proba <- 2 / (inter * gamma(posinter) * gamma(inter - posinter + 1)) info <- -log(proba, base = 2) } } res <- list(data = data, n = n, points = uniques, pos = (poss + exaequos), exaequos = exaequos, nturns = nturns, firstispeak = firstispeak, peaks = peaks, pits = pits, tppos = tppos, proba = proba, info = info) class(res) <- "turnpoints" res } # ------------------------------------------------------------------------------ .extract.turnpointsPastecs <- function(e, n, no.tp = 0, peak = 1, pit = -1, ...) { if (missing(n)) n <- -1 res <- rep(no.tp, length.out= e$n) res[e$pos[e$peaks]] <- peak res[e$pos[e$pits]] <- pit # Keep only the first n points if (n < length(res) & n > 0) res <- res[1:n] res } # ------------------------------------------------------------------------------ .plot.turnpointsPastecs <- function(x, level = 0.05, lhorz = TRUE, lcol = 2, llty = 2, type = "l", xlab = "data number", ylab = paste("I (bits), level = ", level*100, "%", sep = ""), main = paste("Information (turning points) for:",x$data), ...) { # The next function actually draws the graph turnpoints.graph <- function(X, Level, Lhorz, Lcol, Llty, Type, Xlab, Ylab, Main, Sub, ...) { plot(X$tppos, X$info, type = Type, xlab = Xlab, ylab = Ylab, main = Main, ...) abline(h = -log(Level, base = 2), lty = Llty, col = Lcol) } # Return Value: invisible(turnpoints.graph(x, level[1], lhorz, lcol, llty, type, xlab, ylab, main, ...)) } # ------------------------------------------------------------------------------ .summary.turnpointsPastecs <- function(object, ...) { cat("Turning points for:", object$data, "\n\n") cat("nbr observations :", object$n, "\n") cat("nbr ex-aequos :", sum(object$exaequos), "\n") if (object$firstispeak) { cat("nbr turning points:", object$nturns, "(first point is a peak)\n") typep <- c("peak", "pit") } else { cat("nbr turning points:", object$nturns, "(first point is a pit)\n") typep <- c("pit", "peak") } cat("E(p) =", 2 / 3 * (object$n - 2), "Var(p) =", (16 * object$n - 29) / 90, "(theoretical)\n") cat("\n") # construct the table summarizing all turning points typepts <- rep(typep, length.out=object$nturns) tablepts <- as.data.frame(list(point = object$tppos, type = typepts, proba = object$proba, info = object$info)) print(tablepts) # Return Value: invisible(object) } ################################################################################ .turnpointsSeries = function(...) { # Deprecated: .Deprecated(new = "turns") # Return Value: turns(...) } ################################################################################ timeSeries/R/statistics-colCumsums.R0000644000176000001440000001443612620124746017275 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: COLUMN CUMULATIVE SUMS: # colCumsums Computes sample cumulated sums by column # colCumsums,matrix S3 default method (for matrix objects) # colCumsums,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MAXIMA: # colCummaxs Computes cumulated maximum values # colCummaxs,matrix S3 default method (for matrix objects) # colCummaxs,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MAXIMA: # colCummins Computes cumulated maximum values # colCummins,matrix S3 default method (for matrix objects) # colCummins,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE MINIMA: # colCumprods Computes cumulated product values # colCumprods,matrix S3 default method (for matrix objects) # colCumprods,timeSeries S3 method for timeSeries objects # FUNCTION: COLUMN CUMULATIVE RETURNS: # colCumreturns Computes cumulated product values # colCumreturns,matrix S3 default method (for matrix objects) # colCumreturns,timeSeries S3 method for timeSeries objects ################################################################################ # ------------------------------------------------------------------------------ setMethod("colCumsums", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cumsum, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCumsums", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) # ------------------------------------------------------------------------------ setMethod("colCummaxs", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cummax, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCummaxs", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) # ------------------------------------------------------------------------------ setMethod("colCummins", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cummin, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCummins", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) # ------------------------------------------------------------------------------ setMethod("colCumprods", "matrix", function(x, na.rm = FALSE, ...) { if (na.rm) x <- na.omit(x) ans <- apply(x, 2, cumprod, ...) # special treatment when x has one row because apply returns a vector if (NROW(x) == 1) ans <- matrix(ans, nrow = 1, dimnames = dimnames(x)) ans }) # ------------------------------------------------------------------------------ setMethod("colCumprods", "timeSeries", function(x, na.rm = FALSE, ...) setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...))) # ------------------------------------------------------------------------------ setMethod("colCumreturns", "matrix", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Cumulates Returns from a stream of returns # Arguments: # x : a matrix object # method : generate geometric or simple returns, # default "geometric". # FUNCTION: # Handle Missing Values: if (na.rm) x <- na.omit(x, ...) method <- match.arg(method) # Return Value switch(method, "geometric" = colCumsums(x), "simple" = colCumprods(1+x) - 1) }) # ------------------------------------------------------------------------------ setMethod("colCumreturns", "timeSeries", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Cumulates Returns from a stream of returns # Arguments: # x : a timeSeries object # method : generate geometric or simple returns, # default "geometric". # FUNCTION: # Handle Missing Values: if (na.rm) x <- na.omit(x, ...) method <- match.arg(method) # Return Value switch(method, "geometric" = colCumsums(x), "simple" = colCumprods(1+x) - 1) }) ################################################################################ timeSeries/R/base-start.R0000644000176000001440000000403012620124746015005 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # start,timeSeries Extracts start date of a 'timeSeries' object # end,timeSeries Extracts end date of a 'timeSeries' object ################################################################################ .start.timeSeries <- function(x, ...) { # Description: # Extracts start date of a 'timeSeries' object # FUNCTION: # Extract Date: if (length(x@positions)>0) timeDate(min(x@positions), zone = "GMT", FinCenter = x@FinCenter) else NULL } setMethod("start" , "timeSeries", function(x, ...) .start.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions start.timeSeries <- function(x, ...) .start.timeSeries(x, ...) # ------------------------------------------------------------------------------ .end.timeSeries <- function(x, ...) { # Description: # Extracts start date of a 'timeSeries' object # FUNCTION: # Extract Date: if (length(x@positions)>0) timeDate(max(x@positions), zone = "GMT", FinCenter = x@FinCenter) else NULL } setMethod("end", "timeSeries", function(x, ...) .end.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions end.timeSeries <- function(x, ...) .end.timeSeries(x, ...) ################################################################################ timeSeries/R/aaa-Deprecated.R0000644000176000001440000000352012620124746015523 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # DEPRECATED: # .description # durationSeries # midquoteSeries # spreadSeries ################################################################################ .description <- function() { # Deprecated: .Deprecated(new = "description", package = "timeSeries") # Return Value: description() } # ------------------------------------------------------------------------------ durationSeries <- function(...) { # Deprecated: .Deprecated(new = "returns", package = "timeSeries") # Return Value: durations(...) } # ------------------------------------------------------------------------------ midquoteSeries = function(...) { # Deprecated: .Deprecated(new = "midquotes", package = "timeSeries") # Return Value: midquotes(...) } # ------------------------------------------------------------------------------ spreadSeries = function(...) { # Deprecated: .Deprecated(new = "spreads", package = "timeSeries") # Return Value: spreads(...) } ################################################################################ timeSeries/R/AllClass.R0000644000176000001440000001272512620124746014450 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # CLASS: REPRESENTATION: # setClass Classify 'timeSeries' # setValidity Validate 'timeSeries' # setMethod Initialize 'timeSeries' ################################################################################ # CLASS: REPRESENTATION: # 'signalSeries' Deprecated S4 Class representation # 'timeSeries' Deprecated S4 Class representation ################################################################################ # YC: Note if slots are added or removed, don't forget to edit # getDataPart,timeSeries-method and setDataPart,timeSeries-method !! setClass("timeSeries", representation(.Data = "matrix", units = "character", positions = "numeric", format = "character", FinCenter = "character", recordIDs = "data.frame", title = "character", documentation = "character"), contains = "structure", prototype(matrix(NA), units = character(0), positions = numeric(0), format = character(0), FinCenter = character(0), recordIDs = data.frame(), title = character(0), documentation = character(0))) # ------------------------------------------------------------------------------ .validity_timeSeries <- function(object) { if ((length(object@positions) > 0) && NROW(object) != length(object@positions)) return("length of '@positions' not equal to '@.Data' extent") if (NCOL(object) != length(object@units)) return("length of '@units' not equal to '@.Data' extent") if (NROW(object@recordIDs) > 0 & NROW(object@recordIDs) != nrow(object)) return("length of '@recordIDs' not equal to '@.Data' extent") # Return Value: TRUE } setValidity("timeSeries", .validity_timeSeries) # ------------------------------------------------------------------------------ # Note it is faster to assign manually all slots of the timeSeries objects. setMethod("initialize", "timeSeries", function(.Object, .Data = new("matrix"), units = character(0), positions = numeric(0), format = character(0), FinCenter = "", #<< FIXME: use identical in code rather than FinCenter == "" recordIDs = data.frame(), title = character(0), documentation = character(0)) { # as.double -> crucial for speed improvement in subsetting if (!is.double(positions)) positions <- as.double(positions) .Object <- timeSeries::setDataPart(.Object, value = .Data) `slot<-`(.Object, "units", value = units) `slot<-`(.Object, "positions", value = positions) `slot<-`(.Object, "format", value = format) `slot<-`(.Object, "FinCenter", value = FinCenter) `slot<-`(.Object, "recordIDs", value = recordIDs) `slot<-`(.Object, "title", value = title) `slot<-`(.Object, "documentation", value = documentation) # Check only one we needs rather than using validObject anyStrings <- function(x) if (identical(x, TRUE)) character() else x error <- anyStrings(.validity_timeSeries(.Object)) if (length(error) > 0) stop(paste("Initialize timeSeries :", error, collapse = "\n"), call. = FALSE, domain = NA) # Return Value: .Object }) ################################################################################ ## setClass("signalSeries", ## representation( ## .Data = "matrix", ## units = "character", ## recordIDs = "data.frame", ## title = "character", ## documentation = "character"), ## contains = "structure", ## validity = function(object) { ## if (NCOL(getDataPart(object)) != length(object@units)) ## return("length of '@units' not equal to '@.Data' extent") ## TRUE ## }) ## # ------------------------------------------------------------------------------ ## setClass("timeSeries", ## representation(positions = "numeric", ## format = "character", ## FinCenter = "character"), ## contains = "signalSeries", ## validity = function(object) { ## if (NROW(getDataPart(object)) != length(object@positions)) ## return("length of '@positions' not equal to '@.Data' extent") ## if (NCOL(getDataPart(object)) != length(object@units)) ## return("length of '@units' not equal to '@.Data' extent") ## TRUE ## }) ################################################################################ timeSeries/R/methods-plot.R0000644000176000001440000003557112620124746015375 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # plot,timeSeries Plots a 'timeSeries' object # .plot.timeSeries Internal function called by plot.timeSeries # lines,timeSeries Adds lines to a 'timeSeries' plot # points,timeSeries Adds points to a 'timeSeries' plot # FUNCTION: DESCRIPTION: # pretty.timeSeries Returns a sequence of equally spaced round values ################################################################################ .plot.timeSeries <- function(x, y, FinCenter = NULL, plot.type = c("multiple", "single"), format = "auto", at = pretty(x), widths = 1, heights = 1, xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plots 'timeSeries' objects # Arguments: # see plot.ts() # Additional Arguments: # format, at to beautify axis.POSIXct() function # widths, heights to handle layout() function # Details: # This function is build in exactly the same way as the function # plot.ts() for regular time series (R's ts) objects... # Examples: # x = as.timeSeries(data(msft.dat))[, 1:4] # .plot.timeSeries(x) # .plot.timeSeries(x[,1], x[,2], pch = 19) # .plot.timeSeries(x, plot.type = "single", col = 2:5) # FUNCTION: # Check Missing: if (missing(y)) y <- NULL # Check for "pretty' and "chic": if (is.character(at)) { if (at[1] == "pretty" || at[1] == "chic") { return(.xtplot.timeSeries( x=x, y=y, FinCenter = FinCenter, plot.type = plot.type, format = format, at = at, panel = panel, yax.flip = yax.flip, mar.multi = mar.multi, oma.multi = oma.multi, axes=axes, ...) ) } } # Labels: xlabel <- if (!missing(x)) deparse(substitute(x)) ylabel <- if (!missing(y)) deparse(substitute(y)) # Take care of FinCenter: if (!is.null(FinCenter)) { finCenter(x) <- FinCenter if (!is.null(y)) finCenter(y) <- FinCenter if (is(at, "timeDate")) at@FinCenter <- FinCenter } # Return Value: .plotTimeSeries(x = x, y = y, plot.type = plot.type, xy.labels = xy.labels, xy.lines = xy.lines, panel = panel, nc = nc, xlabel = xlabel, ylabel = ylabel, axes = axes, mar.multi = mar.multi, oma.multi = oma.multi, yax.flip = yax.flip, format = format, at = at, widths = widths, heights = heights, ...) } setMethod("plot", "timeSeries", function(x, y, FinCenter = NULL, plot.type = c("multiple", "single"), format = "auto", at = pretty(x), widths = 1, heights = 1, xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) .plot.timeSeries(x = x, y = y, FinCenter = FinCenter, plot.type = plot.type, format = format, at = at, widths = widths, heights = heights, xy.labels=xy.labels, xy.lines=xy.lines, panel = panel, nc = nc, yax.flip = yax.flip, mar.multi = mar.multi, oma.multi = oma.multi, axes = axes, ...)) # until UseMethod dispatches S4 methods in 'base' functions plot.timeSeries <- function(x, y, ...) .plot.timeSeries(x, y, ...) # ------------------------------------------------------------------------------ # Internal Function called by plot(): .plotTimeSeries <- function(x, y = NULL, plot.type = c("multiple", "single"), xy.labels, xy.lines, panel = lines, nc, xlabel, ylabel, type = "l", xlim = NULL, ylim = NULL, xlab = "Time", ylab, log = "", col = 1:ncol(x), bg = NA, pch = 1:ncol(x), cex = par("cex"), lty = par("lty"), lwd = par("lwd"), axes = TRUE, frame.plot = axes, ann = par("ann"), main = NULL, mar.multi, oma.multi, yax.flip, format, at, widths, heights, grid = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plots timeSeries objects - Internal Function # Details: # A modified copy of R's internal 'plotts()' function, # see 'plot.ts()'. # FUNCTION: # Utility Function: plot.type <- match.arg(plot.type) nser <- NCOL(x) if (format == "auto") format = x@format X <- if (x@format == "counts") time(x) else as.POSIXct(time(x)) if (is.character(at) && at == "auto") { # Index = round(seq(1, length(time(x)), length = 6)) # at = X[Index] at = seq(X[1], X[length(X)], length = 6) } if(is(at, "timeDate")) at = as.POSIXct(at) # YC : force col and pch to be of same length as NCOL(x) otherwise # time series might not be plotted at all. col <- rep(col, length.out = nser) pch <- rep(pch, length.out = nser) # Multiple Plots, each one Curve, on one Page: if (plot.type == "multiple" && nser > 1) { ngraph = nser panel <- match.fun(panel) nser <- NCOL(x) if (nser > 10) stop("cannot plot more than 10 series as \"multiple\"") if (is.null(main)) main <- xlabel nm <- colnames(x) if (is.null(nm)) nm <- paste("Series", 1:nser) if (missing(nc)) nc <- if (nser > 4) 2 else 1 nr <- ceiling(nser/nc) oldpar <- par(mar = mar.multi, oma = oma.multi, mfcol = c(nr, nc)) on.exit(par(oldpar)) nr <- ceiling(ngraph/nc) layout(matrix(seq(nr * nc), nr), widths = widths, heights = heights) for (i in 1:nser) { plot(X, series(x)[, i], axes = FALSE, xlab = "", ylab = "", log = log, col = col[i], bg = bg, pch = pch[i], ann = ann, type = "n", ...) panel(X, series(x)[, i], col = col[i], bg = bg, pch = pch[i], type = type, ...) if (frame.plot) box(...) y.side <- if (i%%2 || !yax.flip) 2 else 4 do.xax <- i%%nr == 0 || i == nser if (axes) { axis(y.side, xpd = NA) if (do.xax) { if (x@format == "counts") { axis(1) } else { axis.POSIXct(1, at = at, format = format) } } } if (ann) { mtext(nm[i], y.side, line = 3, ...) if (do.xax) mtext(xlab, side = 1, line = 3, ...) } if(grid) abline(v = at, lty = 3, col = "grey") } if (ann && !is.null(main)) { par(mfcol = c(1, 1)) cex.main = par("cex.main") font.main = par("font.main") col.main = par("col.main") mtext(main, side = 3, line = 3, cex = cex.main, font = font.main, col = col.main, ...) } return(invisible()) } # Scatter Plot: if (!is.null(y)) { stopifnot(isUnivariate(x)) stopifnot(isUnivariate(y)) xy = cbind(x, y) xy <- xy.coords(series(xy)[, 1], series(xy)[, 2], xlabel, ylabel, log) xlab <- if (missing(xlab)) xy$xlab else xlab ylab <- if (missing(ylab)) xy$ylab else ylab xlim <- if (is.null(xlim)) range(xy$x[is.finite(xy$x)]) else xlim ylim <- if (is.null(ylim)) range(xy$y[is.finite(xy$y)]) else ylim n <- length(xy$x) if (missing(xy.labels)) xy.labels <- (n <= 150) if (!is.logical(xy.labels)) { if (!is.character(xy.labels)) stop("'xy.labels' must be logical or character") do.lab <- TRUE } else { do.lab <- xy.labels } ptype <- if (do.lab) "n" else if (missing(type)) "p" else type plot.default(xy, type = ptype, xlab = xlab, ylab = ylab, xlim = xlim, ylim = ylim, log = log, col = col, bg = bg, pch = pch, axes = axes, frame.plot = frame.plot, ann = ann, main = main, ...) if (missing(xy.lines)) { xy.lines <- do.lab } if (do.lab) text(xy, labels = if (is.character(xy.labels)) xy.labels else seq_along(xy$x), col = col, cex = cex) if (xy.lines) { type = if (do.lab) "c" else "l" lines(xy, col = col, lty = lty, lwd = lwd, type = type) } return(invisible()) } # Multiple Curves all in one Plot, on one Page: if (missing(ylab)) { ylab <- colnames(x) if (length(ylab) != 1) ylab <- xlabel } if (is.null(ylim)) ylim <- range(x, na.rm = TRUE) i = 1 X <- if (x@format == "counts") time(x) else as.POSIXct(time(x)) plot(X, series(x)[, i], ylim = ylim, col = col[(i - 1)%%length(col) + 1], lty = lty[(i - 1)%%length(lty) + 1], lwd = lwd[(i - 1)%%length(lwd) + 1], bg = bg[(i - 1)%%length(bg) + 1], pch = pch[(i - 1)%%length(pch) + 1], type = type, axes = FALSE, ylab = "", xlab = "") if (NCOL(x) > 1) for (i in 2:NCOL(x)) lines(X, series(x)[, i], col = col[(i - 1)%%length(col) + 1], lty = lty[(i - 1)%%length(lty) + 1], lwd = lwd[(i - 1)%%length(lwd) + 1], bg = bg[(i - 1)%%length(bg) + 1], pch = pch[(i - 1)%%length(pch) + 1], type = type) if (ann) title(main = main, xlab = xlab, ylab = ylab, ...) if (axes) { if (x@format == "counts") axis(1, ...) else axis.POSIXct(1, at = at, format = format) axis(2, ...) } if (frame.plot) box(...) if(grid) abline(v = at, lty = 3, col = "grey") return(invisible()) } # ------------------------------------------------------------------------------ .lines.timeSeries <- function(x, FinCenter = NULL, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # NEW Lines method for an object of class "timeSeries" # Arguments: # x - a "timeSeries" object # Example: # plot(MSFT[, 1]); lines(MSFT[, 1], col = "red") # FUNCTION: # Change FinCenter: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Lines: positions <- time(x) if (x@format == "counts") { lines(x = positions, y = series(x), ...) } else { lines(x = as.POSIXct(positions), y = series(x), ...) } # Return Value: invisible(x) } setMethod("lines", "timeSeries", function(x, FinCenter = NULL, ...) .lines.timeSeries(x, FinCenter, ...)) # until UseMethod dispatches S4 methods in 'base' functions lines.timeSeries <- function(x, FinCenter = NULL, ...) .lines.timeSeries(x, FinCenter = FinCenter, ...) # ------------------------------------------------------------------------------ .points.timeSeries <- function(x, FinCenter = NULL, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Plot method for an object of class "timeSeries" # Arguments: # x - a "timeSeries" object # Value: # Plots a 'timeSeries' object. # FUNCTION: # Change FinCenter: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Points: positions <- time(x) if (x@format == "counts") { points(x = positions, y = series(x), ...) } else { points(x = as.POSIXct(positions), y = series(x), ...) } # Return Value: invisible(x) } setMethod("points", "timeSeries", function(x, FinCenter = NULL, ...) .points.timeSeries(x, FinCenter = FinCenter, ...)) # until UseMethod dispatches S4 methods in 'base' functions points.timeSeries <- function(x, FinCenter = NULL, ...) .points.timeSeries(x, FinCenter = FinCenter, ...) ################################################################################ pretty.timeSeries <- function(x, n = 5, min.n = n%/%3, shrink.sml = 0.75, high.u.bias = 1.5, u5.bias = 0.5 + 1.5 * high.u.bias, eps.correct = 0, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns a sequence of equally spaced round values. # Details: # Computes a sequence of about n+1 equally spaced ?round? # values which cover the range of the values in x. # The values are chosen so that they are 1, 2 or 5 times # a power of 10. # Arguments: # x - a timeSeries object from which the time is # extracted # n - integer giving the desired number of intervals. # min.n - nonnegative integer giving the minimal # number of intervals. # shrink.sml - positive numeric by a which a default # scale is shrunk in the case when range(x) is # very small. # high.u.bias - non-negative numeric, typically > 1. # Larger high.u.bias values favor larger units. # u5.bias - non-negative numeric multiplier favoring # factor 5 over 2. # eps.correct - integer code, one of {0,1,2}. If # non-0, a correction is made at the boundaries. # ... - further arguments for methods. # FUNCTION: td <- time(x) if (inherits(x, "timeDate")) { x <- as.POSIXct(td) as.timeDate(pretty(x, n=n, min.n=min.n, shrink.sml=shrink.sml, high.u.bias=high.u.bias, u5.bias=u5.bias, eps.correct=eps.correct, ...)) } else { #-> signal series pretty(td) } } ############################################################################### timeSeries/R/fin-daily.R0000644000176000001440000002014312620124746014617 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # alignDailySeries Aligns a 'timeSeries' object to new positions # rollDailySeries Rolls daily a 'timeSeries' on a given period # OBSOLETE: DESCRIPTION: # .ohlcDailyPlot Plots open high low close bar chart # .plotOHLC Internal called by function ohlcDailyPlot() ################################################################################ alignDailySeries <- function (x, method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, units = NULL, zone = "", FinCenter = "", ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Aligns a 'timeSeries' object to new positions # Arguments: # x - an object of class "timeSeries". # method - # "before" - use the data from the row whose position is # just before the unmatched position; # "after" - use the data from the row whose position is # just after the unmatched position; # "linear" - interpolate linearly between "before" and # "after". # "fillNA" - fill missing days with NA values # include.weekends - a logical value. Should weekend dates be # included or removed? # Note: alignDailySeries is now based on align timeSeries method. # FUNCTION: # Preserve Title and Documentation: Title <- x@title Documentation <- x@documentation # Adjust zone and FinCenter if provided if (zone != "" || FinCenter != "") { if (zone == "") zone <- getRmetricsOptions("myFinCenter") if (FinCenter == "") FinCenter <- getRmetricsOptions("myFinCenter") x <- timeSeries(x, zone = zone, FinCenter = FinCenter) } # Run Generic Function align() ans <- .align.timeSeries(x = x, by = "1d", offset = "0s", method = method, include.weekends = include.weekends, ...) ans@title <- Title ans@documentation <- Documentation # Add New Units: if (!is.null(units)) colnames(ans) = units # Return Value: ans } # ------------------------------------------------------------------------------ rollDailySeries <- function(x, period = "7d", FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Rolls daily a 'timeSeries' on a given period # Arguments: # x - an univariate "timeSeries" object or a numeric vector. # n - an integer specifying the number of periods or # terms to use in each rolling/moving sample. # trim - a logical flag: if TRUE, the first n-1 missing values in # the returned object will be removed; if FALSE, they will # be saved in the returned object. The default is TRUE. # FUN - the rolling function, arguments to this function can be # passed through the \code{\dots} argument. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Check for Signal Series: Message <- " is for time series and not for signal series." if (x@format == "counts") stop(as.character(match.call())[1], Message) # Preserve Title and Documentation: Title <- x@title Documentation <- x@documentation # Fix missing matrix method for quantile(), still to do ... .quantile.matrix = function(x, probs = 0.95, ...) { apply(as.matrix(x), 2, quantile, probs = probs) } # Settings: periodLength = as.numeric(substr(period, 1, nchar(period) - 1)) periodUnit = substr(period, nchar(period), nchar(period)) N = nrow(x) Start = start(x) + (periodLength-1)*24*3600 Positions = time(x) to = Positions[Positions > Start] from = to - periodLength*24*3600 # Apply Function: ans <- applySeries(x = x, from = from, to = to, FUN = FUN, ...) ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ # OBSOLETE: .ohlcDailyPlot <- function(x, volume = TRUE, colOrder = c(1:5), units = 1e6, xlab = c("Date", "Date"), ylab = c("Price", "Volume"), main = c("O-H-L-C", "Volume"), grid.nx = 7, grid.lty = "solid", ...) { # A function implemented by Diethelm Wuertz # Description: # Plots open | high | low | close bar chart # Arguments: # x - an S4 object of class 'timeSeries' with named entries: # Open, High, Low, Close, and Volume # Reference: # Build on top of Adrian Trapletti's plotOHLC() # function from his R-package "tseries". # FUNCTION: stopifnot(is.timeSeries(x)) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Next: x.filled = alignDailySeries(x, method = "fillNA", include.weekends = TRUE) jul = as.integer(julian(time(x.filled))) X = ts(as.matrix(x.filled)[, 1:4], start = min(jul), end = max(jul)) # Plot OHLC: .plotOHLC(X, origin = "1970-01-01", xlab = xlab[1], ylab = ylab[1]) # print(axTicks(1)) # print(axTicks(2)) title(main = main[1]) grid(nx = grid.nx, ny = NULL, lty = grid.lty, ...) # Include Volume? if (volume) { Volume = x[, 5]/units plot(Volume, type = "h", xlab = xlab[2], ylab = ylab[2]) title(main = main[2]) grid(nx = grid.nx, ny = NULL, lty = grid.lty, ...) } # Return value: invisible() } # ------------------------------------------------------------------------------ .plotOHLC = function (x, xlim = NULL, ylim = NULL, xlab = "Time", ylab, col = par("col"), bg = par("bg"), axes = TRUE, frame.plot = axes, ann = par("ann"), main = NULL, date = c("calendar", "julian"), format = "%Y-%m-%d", origin = "1899-12-30", ...) { # A Copy from Contributed R Package 'tseries' # Description: # Internal called by function .ohlcDailyPlot() # FUNCTION: # Check for mts: if ((!is.mts(x)) || (colnames(x)[1] != "Open") || (colnames(x)[2] != "High") || (colnames(x)[3] != "Low") || (colnames(x)[4] != "Close")) stop("x is not a open/high/low/close time series") xlabel <- if (!missing(x)) deparse(substitute(x)) else NULL if (missing(ylab)) ylab <- xlabel date <- match.arg(date) time.x <- time(x) dt <- min(lag(time.x) - time.x)/3 if (is.null(xlim)) xlim <- range(time.x) if (is.null(ylim)) ylim <- range(x[is.finite(x)]) plot.new() plot.window(xlim, ylim, ...) segments(time.x, x[, "High"], time.x, x[, "Low"], col = col[1], bg = bg) segments(time.x - dt, x[, "Open"], time.x, x[, "Open"], col = col[1], bg = bg) segments(time.x, x[, "Close"], time.x + dt, x[, "Close"], col = col[1], bg = bg) if (ann) title(main = main, xlab = xlab, ylab = ylab, ...) if (axes) { if (date == "julian") { axis(1, ...) axis(2, ...) } else { n <- NROW(x) lab.ind <- round(seq(1, n, length = 5)) D <- as.vector(time.x[lab.ind] * 86400) + as.POSIXct(origin, tz = "GMT") DD <- format.POSIXct(D, format = format, tz = "GMT") axis(1, at = time.x[lab.ind], labels = DD, ...) axis(2, ...) } } if (frame.plot) box(...) # Return Value: invisible() } ################################################################################ timeSeries/R/utils-description.R0000644000176000001440000000252712620124746016432 0ustar ripleyusers # 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 ################################################################################ # FUNCTION: DESCRIPTION: # description Creates default description string ################################################################################ description <- function() { # A function implemented by Diethelm Wuertz # Description: # Sets default description string: # FUNCTION: # Get Description String: ans <- paste(as.character(date()), "by user:", Sys.getenv("USERNAME")) # Return Value: ans } ################################################################################ timeSeries/R/base-subsetting.R0000644000176000001440000005375612620124746016062 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: SUBSETTING METHODS ON DATA: # .subset_timeSeries # .findIndex # $,timeSeries Subsets a time series by column names # $<-,timeSeries Replaces subset by column names # [,timeSeries Subsets a time series object # [<-,timeSeries Assigns value to subsets of a time series ################################################################################ ################################################################################ # index ################################################################################ # Note : no "character" -> because needs to be coerced to timeDate object. setClassUnion("index_timeSeries", members = c("numeric", "logical")) setClassUnion("time_timeSeries", members = c("POSIXt", "Date")) # ------------------------------------------------------------------------------ .subset_timeSeries <- function(x, i, j) { stopifnot(inherits(x, "timeSeries")) stopifnot(is(i, "index_timeSeries")) stopifnot(is(j, "index_timeSeries")) # subset data and positions t <- try(data <- .subset(x, i, j, drop = FALSE), silent = TRUE) if (inherits(t, "try-error")) { # cast error and remove calling function msg <- sub("Error in.*: \n *", "", t) stop(msg, call. = FALSE) } pos <- if (length(x@positions)>0) .subset(x@positions, i) else numeric(0) units <- .subset(x@units, j) # Record IDs: df <- x@recordIDs if (prod(dim(df))) df <- df[i, , drop = FALSE] # Result new("timeSeries", .Data = data, title = x@title, documentation = x@documentation, format = x@format, FinCenter = x@FinCenter, units = units, recordIDs = df, positions = pos) } # ------------------------------------------------------------------------------ .findIndex <- function(ipos, pos) { attributes(ipos) <- NULL if (unsorted <- is.unsorted(pos)) { or <- order(pos) pos <- pos[or] } i <- findInterval(ipos, pos) if (!identical(ipos, pos[i])) stop("subscript out of bounds", call. = FALSE) if (unsorted) i <- or[i] i } ################################################################################ # [,timeSeries Subsets of a 'timeSeries' object ################################################################################ ## i <- c("index_timeSeries", "character", "timeDate", ## "timeSeries", "missing", "ANY") ## j <- c("index_timeSeries", "character", "timeSeries", ## "missing", "ANY") ## expand.grid(i = i, j = j) ## > i j ## 1 index_timeSeries index_timeSeries ## 2 character index_timeSeries ## 3 timeDate index_timeSeries ## 4 timeSeries index_timeSeries ## 5 missing index_timeSeries ## 6 ANY index_timeSeries ## 7 index_timeSeries character ## 8 character character ## 9 timeDate character ## 10 timeSeries character ## 11 missing character ## 12 ANY character ## 13 index_timeSeries timeSeries ## 14 character timeSeries ## 15 timeDate timeSeries ## 16 timeSeries timeSeries ## 17 missing timeSeries ## 18 ANY timeSeries ## 19 index_timeSeries missing ## 20 character missing ## 21 timeDate missing ## 22 timeSeries missing ## 23 missing missing ## 24 ANY missing ## 25 index_timeSeries ANY ## 26 character ANY ## 27 timeDate ANY ## 28 timeSeries ANY ## 29 missing ANY ## 30 ANY ANY ## YC : Added i=time_timeSeries i <- "time_timeSeries" j <- c("index_timeSeries", "character", "timeSeries", "missing", "ANY") expand.grid(i = i, j = j) ## 1 time_timeSeries index_timeSeries ## 2 time_timeSeries character ## 3 time_timeSeries timeSeries ## 4 time_timeSeries missing ## 5 time_timeSeries ANY # ------------------------------------------------------------------------------ ## FIXME : deal with signal series # ------------------------------------------------------------------------------ ## 1 index_timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) .subset_timeSeries(x, i, j)) # ------------------------------------------------------------------------------ ## 2 character index_timeSeries setMethod("[", signature(x = "timeSeries", i = "character", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { td <- timeDate(i) if (any(is.na(td))) return(as.vector(NA)) # bad to use directly @Data but more efficient in this case i <- .findIndex(td@Data, x@positions) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 3 timeDate index_timeSeries setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 4 timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "timeSeries", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) { if (x@format != "counts" && i@format != "counts" && finCenter(x) != finCenter(i)) stop("FinCenter of timeSeries and subset do not match") .subset_timeSeries(x, as.vector(i), j) }) # ------------------------------------------------------------------------------ ## 5 missing index_timeSeries setMethod("[", signature(x = "timeSeries", i = "missing", j = "index_timeSeries"), function(x, i, j, ..., drop = FALSE) .subset_timeSeries(x, TRUE, j)) # ------------------------------------------------------------------------------ ## 6 ANY index_timeSeries setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 7 index_timeSeries character setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 8 character character setMethod("[", signature(x = "timeSeries", i = "character", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 9 timeDate character setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "character"), function(x, i, j, ..., drop = FALSE) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, i, j) }) # ------------------------------------------------------------------------------ ## 10 timeSeries character # inherited method works fine # ------------------------------------------------------------------------------ ## 11 missing character setMethod("[", signature(x = "timeSeries", i = "missing", j = "character"), function(x, i, j, ..., drop = FALSE) { j <- pmatch(j, slot(x, "units"), duplicates.ok = TRUE) if (any(is.na(j))) stop("subscript out of bounds", call. = FALSE) .subset_timeSeries(x, TRUE, j) }) # ------------------------------------------------------------------------------ ## 12 ANY character setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 13 index_timeSeries timeSeries ## 14 character timeSeries ## 15 timeDate timeSeries ## 16 timeSeries timeSeries ## 17 missing timeSeries ## 18 ANY timeSeries ## rely on inherited methods # ------------------------------------------------------------------------------ ## 19 index_timeSeries missing setMethod("[", signature(x = "timeSeries", i = "index_timeSeries", j = "missing"), function(x, i, j, ..., drop = FALSE) { if(nargs() == 2) { # same sub-setting as matrix if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[i] } else { .subset_timeSeries(x, i, TRUE) } }) # ------------------------------------------------------------------------------ ## 20 character missing setMethod("[", signature(x = "timeSeries", i = "character", j = "missing"), function(x, i, j, ..., drop = FALSE) { if (nargs() == 2) as.numeric(NA) #-> return NA if comma missing else callGeneric(x=x, i=i, j=TRUE) }) # ------------------------------------------------------------------------------ ## 21 timeDate missing setMethod("[", signature(x = "timeSeries", i = "timeDate", j = "missing"), function(x, i, j, ..., drop = FALSE) { # do not return NA if comma missing because timeDate index # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) .subset_timeSeries(x, i, TRUE) }) # ------------------------------------------------------------------------------ ## 22 timeSeries missing setMethod("[", signature(x = "timeSeries", i = "timeSeries", j = "missing"), function(x, i, j, ..., drop = FALSE) { if (x@format != "counts" && i@format != "counts" && finCenter(x) != finCenter(i)) stop("FinCenter of timeSeries and subset do not match") if(nargs() == 2) { if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[as.vector(i)] } else { .subset_timeSeries(x, as.vector(i), TRUE) } }) # ------------------------------------------------------------------------------ ## workaround i <- matrix. setMethod("[", signature(x = "timeSeries", i = "matrix", j = "missing"), function(x, i, j, ..., drop = FALSE) { if(nargs() == 2) { # same sub-setting as matrix if(any(as.logical(i)) || prod(dim(x)) == 0) as.vector(x)[i] } else { .subset_timeSeries(x, as.vector(i), TRUE) } }) # ------------------------------------------------------------------------------ ## 23 missing missing setMethod("[", signature(x = "timeSeries", i = "missing", j = "missing"), function(x, i, j, ..., drop = FALSE) x) # ------------------------------------------------------------------------------ ## 24 ANY missing setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 25 index_timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 26 character ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 27 timeDate ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 28 timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 29 missing ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 30 ANY ANY setMethod("[", signature(x = "timeSeries", i = "ANY", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) stop("invalid or not-yet-implemented 'timeSeries' subsetting")) # ------------------------------------------------------------------------------ ## 1 time_timeSeries index_timeSeries setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "index_timeSeries"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 2 time_timeSeries character setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "character"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) # ------------------------------------------------------------------------------ ## 4 time_timeSeries missing setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "missing"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, drop=drop) }) # ------------------------------------------------------------------------------ ## 5 time_timeSeries ANY setMethod("[", signature(x = "timeSeries", i = "time_timeSeries", j = "ANY"), function(x,i,j, ..., drop = FALSE) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, drop=drop) }) ################################################################################ # $,timeSeries Subset by column names ################################################################################ # should behave the same way as $,data.frame setMethod("$", signature(x = "timeSeries"), function (x, name) { nc <- colnames(x) nr <- names(x@recordIDs) dataIdx <- pmatch(name, nc) recordIDsIdx <- pmatch(name, nr) # if none or more than one match returns NULL if ((is.na(dataIdx) && is.na(recordIDsIdx)) || (!is.na(dataIdx) && !is.na(recordIDsIdx))) return(NULL) if (!is.na(dataIdx)) return(.subset(x, TRUE, dataIdx)) if (!is.na(recordIDsIdx)) return(x@recordIDs[[recordIDsIdx]]) NULL }) # methods to generate completion after $ .DollarNames.timeSeries <- function(x, pattern) grep(pattern, names(x), value = TRUE) ################################################################################ # $<-,timeSeries Subset by column names ################################################################################ .dollar_assign <- function(x, name, value) { stopifnot(inherits(x, "timeSeries")) # check size of value if (NROW(value) < nrow(x)) { value <- rep(value, length.out = nrow(x)) } else if (NROW(value) > nrow(x)) { stop(gettextf("replacement has %i rows, time series has %i", NROW(value), nrow(x))) #, call. = FALSE) } # assign value to recordIDs if (length(x@recordIDs)) { x@recordIDs[[name]] <- value } else { x@recordIDs <- as.data.frame(value) colnames(x@recordIDs) <- name } # check if object is valid validObject(x) x } setReplaceMethod("$", signature(x = "timeSeries", value = "numeric"), function(x, name, value) { # check size of value if (NROW(value) < nrow(x)) { value <- rep(value, length.out = nrow(x)) } else if (NROW(value) > nrow(x)) { stop(gettextf("replacement has %i rows, time series has %i", NROW(value), nrow(x))) #, call. = FALSE) } # get data part data <- getDataPart(x) # coerce value to matrix ncol <- NCOL(value) value <- matrix(value, ncol = NCOL(value), dimnames = NULL) # set up colnames cn <- colnames(value) if (any(is.null(cn))) cn <- if (ncol > 1) paste(name, ".", seq.int(ncol), sep = "") else name colnames(value) <- cn # if name already present - subsitute ... if (any(cdata <- (colnames(data) %in% cn))) { cvalue <- cn %in% colnames(data) data[,cdata] <- value[,cvalue] value <- cbind(data, value[,!cvalue]) ans <- setDataPart(x, value) } else { ans <- .dollar_assign(x, name, as.vector(value)) } # return ans }) setReplaceMethod("$", signature(x = "timeSeries", value = "factor"), function(x, name, value) .dollar_assign(x, name, value)) setReplaceMethod("$", signature(x = "timeSeries", value = "ANY"), function(x, name, value) .dollar_assign(x, name, value)) ################################################################################ # [<-,timeSeries Assign value to subsets of a 'timeSeries' object ################################################################################ # Note that most of the generic function works by default with [<-,timeDate # only need to deal with special cases that are i <- ("timeDate", "character") # ------------------------------------------------------------------------------ # timeDate setReplaceMethod("[", signature(x = "timeSeries", i = "timeDate", j = "ANY"), function(x, i, j, value) { # bad to use directly @Data but more efficient in this case i <- .findIndex(i@Data, x@positions) callGeneric(x=x, i=i, j=j, value=value) }) setReplaceMethod("[", signature(x = "timeSeries", i = "timeDate", j = "missing"), function(x, i, j, value) callGeneric(x=x, i=i, j=TRUE, value=value)) # ------------------------------------------------------------------------------ # character setReplaceMethod("[", signature(x = "timeSeries", i = "character", j = "ANY"), function(x, i, j, value) { i <- timeDate(i) callGeneric(x=x, i=i, j=j, value=value) }) setReplaceMethod("[", signature(x = "timeSeries", i = "character", j = "missing"), function(x, i, j, value) { i <- timeDate(i) callGeneric(x=x, i=i, j=TRUE, value=value) }) ################################################################################ timeSeries/R/timeSeries-slotTime.R0000644000176000001440000001056012620124746016654 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # time,timeSeries Extracs time positions from a 'timeSeries' # time<- Defines S3 UseMethod # time<-.timeSeries ... to avoid problems with zoo # FUNCTION: DESCRIPTION: # getTime Get time slot from a 'timeSeries' # setTime<- Set new time slot to a 'timeSeries' ################################################################################ # DEPRECATED: DESCRIPTION: # seriesPositions Deprecated, use time # newPositions<- Deprecated, use time<- ################################################################################ .time.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Extracs time positions from a 'timeSeries' # Arguments: # x - a 'timeSeries' object. # Value: # Returns a time resampled object of class 'timeSeries'. # FUNCTION: if (length(x@positions)>0) timeDate(x@positions, zone = "GMT", FinCenter = x@FinCenter) else seq.int(NROW(x)) } setMethod("time", "timeSeries", function(x, ...) .time.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions time.timeSeries <- function(x, ...) .time.timeSeries(x, ...) # ------------------------------------------------------------------------------ `time<-` <- function(x, value) { UseMethod("time<-") } # ------------------------------------------------------------------------------ `time<-.timeSeries` <- function(x, value) { # A function implemented by Yohan Chalabi # Note: # To avoid conflict with zoo package. # FUNCTION: # Assign Rownames: rownames(x) <- value # Return Value: x } # ------------------------------------------------------------------------------ # setMethod("time<-", "timeSeries", function(x, value) # { # rownames(x) <- value # # Return # x # }) ############################################################################### getTime <- function(x) { # Description: # Get time slot from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: time(x) } # ------------------------------------------------------------------------------ "setTime<-" <- function(x, value) { # Description: # Set time slot to a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Assign Time Slot: time(x) <- value # Return Value: x } ############################################################################### # DEPRECATED seriesPositions <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Extracts the positions of a 'timeSeries' objects and # converts them to a 'timeDate' object. # Arguments: # object - a 'timeSeries' object # Value: # Returns 'timeSeries' positions as 'timeDate' objects. # FUNCTION: # Deprecated: .Deprecated(new = "time", package = "timeSeries") # Return Value: time(object) } # ------------------------------------------------------------------------------ # Deprecated: "newPositions<-" <- function(object, value) { # A function implemented by Diethelm Wuertz # FUNCTION: # Deprecated: .Deprecated(new = "time<-", package = "timeSeries") # Assign Rownames: rownames(object) <- value # Return Value: object } ################################################################################ timeSeries/R/base-rank.R0000644000176000001440000000377012620124746014615 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: COLUMN STATISTICS IN FUTILITIES: # rank,timeSeries Returns sample ranks of a 'timeSeries' object ################################################################################ setMethod("rank", "timeSeries", function(x, na.last = TRUE, ties.method = eval(formals(rank)$ties.method)) { # Description: # Returns the sample ranks of the values in a 'timeSeries' # Arguments: # x - an object of class 'timeSeries' # ties.method - # "average", replaces them by their mean, # "first" method results in a permutation with increasing # values at each index set of ties. # "random" method puts these in random order whereas the # default, # "max" and "min" replaces them by their maximum and minimum # respectively, the latter being the typical sports ranking. # Note: # Ties (i.e., equal values) and missing values can be handled # in several ways. # FUNCION: # Return Value: apply(x, 2, rank, na.last = na.last, ties.method = ties.method) } ) ################################################################################ timeSeries/R/timeSeries-slotFinCenter.R0000644000176000001440000000610712620124746017635 0ustar ripleyusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # finCenter,timeSeries Get financial center slot from a 'timeSeries' # finCenter<-,timeSeries Set financial center slot from a 'timeSeries' # FUNCTION: DESCRIPTION: # getFinCenter Get financial center slot from a 'timeSeries' # setFinCenter<- Set new financial center slot from a 'timeSeries' ################################################################################ setMethod("finCenter", "timeSeries", function(x) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Get financial center slot from a 'timeSeries' object # Arguments: # x - an object of class 'timeSeries' # FUNCTION: # Extract financial center: ans <- x@FinCenter # Return Value: ans }) # ------------------------------------------------------------------------------ setMethod("finCenter<-", "timeSeries", function(x, value) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Set financial center slot from a 'timeSeries' object # Arguments: # x - an object of class 'timeSeries' # value - a character string, setting the name of the financial # center. # FUNCTION: # Check: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Convert to user financial centre: positions <- timeDate(charvec = time(x), zone = finCenter(x), FinCenter = value) # Assign Positions to Time Stamps: time(x) <- positions # Return Value: x }) ################################################################################ getFinCenter <- function(x) { # Description: # Get financial center slot from a 'timeSeries' object # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: finCenter(x) } # ------------------------------------------------------------------------------ "setFinCenter<-" <- function(x, value) { # Description: # Set new financial center slot from a 'timeSeries' object # FUNCTION: # Arguments: # x - a 'timeSeries' object # Assign Financial Center Slot: finCenter(x) <- value # Return Value: x } ################################################################################ timeSeries/R/methods-comment.R0000644000176000001440000000266612620124746016060 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # comment, timeSeries Get documentation slot of a timeSeries object # comment<-,timeSeries Set documentation slot of a timeSeries object ################################################################################ setMethod("comment", "timeSeries", function(x) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Return Value: x@documentation } ) # ------------------------------------------------------------------------------ setMethod("comment<-", "timeSeries", function(x, value) { x@documentation <- paste(value, collapse = " ") # Return Value: x } ) ################################################################################ timeSeries/R/fin-runlengths.R0000644000176000001440000000422012620124746015704 0ustar ripleyusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # runlengths Returns 'timeSeries' object of runlengths ################################################################################ runlengths <- function(x, ...) { # A function implemetned by Diethelm Wuertz # Description: # Returns 'timeSeries' object of runlengths # Arguments: # x - an univariate 'timeSeries' object of financial returns # ... - arguments passed to the function na.omit() # Value: # runlengths an object of class 'timeSeries'. # Note: # Zeroes are handled as NA. # Example: # set.seed(4711) # x.tS = timeSeries(data=rnorm(12), charvec=timeCalendar(), units="x") # runlengths(x.tS) # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x[x == 0] <- NA x.vec = sign(as.vector(na.omit(x, ...))) # Compute Run Lengths: n <- length(x.vec) y <- x.vec[-1L] != x.vec[-n] Index <- c(which(y | is.na(y)), n) X = x[Index, ] series(X) <- matrix(diff(c(0L, Index)), ncol = 1) # Reset recordIDs: X@recordIDs <- data.frame() # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } ################################################################################ timeSeries/R/fin-align.R0000644000176000001440000001077112620124746014615 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # align Aligns a 'timeSeries object' to time stamps # .align.timeSeries Aligns a 'timeSeries object' to time stamps ################################################################################ # DW: See also ... # in package timeDate # setMethod("align", "ANY", # setMethod("align", "timeDate", # ------------------------------------------------------------------------------ .align.timeSeries <- function(x, by = "1d", offset = "0s", method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Aligns a 'timeSeries' object to equidistant time stamps # Arguments: # x - an object of class "timeSeries". # by - # offset - # method - # "before" - use the data from the row whose position is # just before the unmatched position; # "after" - use the data from the row whose position is # just after the unmatched position; # "linear" - interpolate linearly between "before" and # "after". # "fillNA" - fill missing days with NA values # include.weekends - a logical value. Should weekend dates be # included or removed? # Example: # data(usdthb) # data = matrix(usdthb[, "BID"]) # charvec = as.character(usdthb[, "XDATE"]) # USDTHB = timeSeries(data, charvec, format = "%Y%M%d%H%M") # align(USDTHB, by = "3h", offset = "92m") # MSFT = as.timeSeries(data(msft.dat)) # align(MSFT) # See also: # in package timeDate # setMethod("align", "ANY", # setMethod("align", "timeDate", # FUNCTION: # Settings: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # check if series sorted if (is.unsorted(x)) x <- sort(x) # Adjustment: Method <- match.arg(method) fun <- switch(Method, before = function(x, u, v) approxfun(x = u, y = v, method = "constant", f = 0, ...)(x), after = function(x, u, v) approxfun(x = u, y = v, method = "constant", f = 1, ...)(x), interp = , fillNA = function(x, u, v) approxfun(x = u, y = v, method = "linear", f = 0.5, ...)(x), fmm = , periodic = , natural = , monoH.FC = function(x, u, v) splinefun(x = u, y = v, method = Method, ...)(x)) # Align timeDate stamps td1 <- time(x) td2 <- align(td1, by = by, offset = offset) # extract numerical values u <- as.numeric(td1, units = "secs") xout <- as.numeric(td2, units = "secs") N = NCOL(x) data <- matrix(ncol = N, nrow = length(td2)) xx <- getDataPart(x) for (i in 1:N) { v <- as.vector(xx[, i]) # New Positions and approximated values: yout <- fun(xout, u, v) if (Method == "fillNA") yout[!(xout %in% u)] = NA # Compose data: data[, i] = yout } # build time series ans <- timeSeries(data, td2, units = colnames(x)) # Remove Weekends: if(!include.weekends) ans <- ans[isWeekday(td2), ] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ setMethod("align", "timeSeries", .align.timeSeries) ################################################################################ timeSeries/R/base-cbind.R0000644000176000001440000002575312620124746014746 0ustar ripleyusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # Generic functions defined in {base} # cbind.timeSeries Combines two 'timeSeries' objects by column # rbind.timeSeries Combines two 'timeSeries' objects by row ################################################################################ # Generic functions defined in {methods} # cbind2.timeSeries Combines two objects by column # rbind2.timeSeries Combines two objects by row ################################################################################ cbind.timeSeries <- function(..., deparse.level = 1) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Binds columns of two 'timeSeries' objects # Arguments: # ... # deparse.level # FUNCTION: # Columnwise bind: dots <- list(...) # Preserve the title of the first ... element # counter <- NULL # for (i in 1:length(dots)) counter <- c(counter, is.timeSeries(dots[[i]])) # Index <- which(counter[1]) Title <- "" #dots[[Index]]@title # Compose Attributes - Documentation : Attributes <- list() for (i in 1:length(dots)) { if (class(dots[[i]]) == "timeSeries") { nextAttributes <- getAttributes(dots[[i]]) Attributes <- .appendList(Attributes, nextAttributes) } } Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # remove NULL from dots args if (any(t <- unlist(lapply(dots, is.null)))) dots[t] <- NULL # deal with numeric values vecIdx <- sapply(dots, function(obj) (!inherits(obj, "timeSeries") && prod(dim(obj)) == 1)) if (any(vecIdx)) dots[vecIdx] <- lapply( dots[vecIdx], function(vec) as.timeSeries(rep(as.vector(vec), len = NROW(dots[[1]])))) # coerce to timeSeries object if not a timeSeries if (any(t <- !unlist(lapply(dots, inherits, "timeSeries")))) dots[t] <- lapply(dots[t], as.timeSeries) # note that new timeSeries get FinCenter of first entry of args FinCenter = finCenter(dots[[1]]) # get names of arguments if any units <- unlist(lapply(dots, colnames)) if (length(t <- as.logical((nchar(nm <- names(units)))))) units[t] <- nm[t] # change colnames if they are the same if (length(unique(units)) != length(units)) { for (name in unique(units)) { pos <- grep(name, units) if (length(pos) != 1) units[pos] <- paste(units[pos], seq(pos), sep = ".") } } # ensure that data is sorted dots <- lapply(dots, sort) # get list of timestamps and recordIDs tds <- lapply(dots, slot, "positions") rec <- lapply(dots, slot, "recordIDs") # Fast version when timeSeries have identical timestamps # or with signal series if (any(co <- unlist(lapply(dots, function(ts) ts@format == "counts"))) || (any(!co) & all(sapply(tds[!co], identical, tds[!co][[1]])))) { # check if all have same number of rows if (diff(range((unlist(lapply(dots, NROW)))))) stop("number of rows must match") td <- if (any(!co)) tds[!co][[1]] else NULL data <- array(unlist(dots), dim=c(NROW(dots[[1]]), sum(sapply(dots, ncol)))) recordIDs <- if (sum(recIdx <- sapply(rec, length))) do.call(cbind, rec[recIdx]) else data.frame() ans <- timeSeries(data = data, charvec = td, units = units, zone = "GMT", FinCenter = FinCenter, recordIDs = recordIDs) } else { # Aligned timestamps: td <- sort(unique(unlist(tds))) fun <- function(ts, td, ref) { mm <- matrix(NA, ncol = ncol(ts), nrow = length(ref)) mm[findInterval(td, ref),] <- getDataPart(ts) mm} data <- mapply(fun, ts = dots, td = tds, MoreArgs = list(ref=td), SIMPLIFY = FALSE) data <- array(unlist(data), dim=c(length(td), sum(sapply(dots, ncol)))) # Note that recordIDs are not preserved when time stamps are # not equal because don't know what value we should use for # missing entries if (sum(sapply(rec, length))) { msg <- "@recordIDs cannot be binded when timestamps are not identical" warning(msg, call. = FALSE) } # note that new timeSeries get FinCenter of first entry of args ans <- timeSeries(data = data, charvec = td, units = units, zone = FinCenter, FinCenter = FinCenter) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ ## # YC: ## # Note that since 2.9.0 S3 methods can not be defined for S4 classes ## # which extends an object like matrix. Therefore we turn all S3 ## # generics to S4 generics for backward compatibility ## # Note that since 2.8.0 it is possible to define methods for functions ## # with dots ... ## if (getRversion() < "2.9.0") { ## cbind.timeSeries <- ## function(..., deparse.level = 1) ## .cbind.timeSeries(..., deparse.level = deparse.level) ## } else { ## setGeneric("cbind", signature = "...") #-> creates warning but ## # cannot avoid it with ## # current dotsMethods scheme ## setMethod("cbind", "timeSeries", function(..., deparse.level = 1) ## .cbind.timeSeries(..., deparse.level = deparse.level)) ## } # ------------------------------------------------------------------------------ setMethod("cbind2", c("timeSeries", "timeSeries"), function(x, y) cbind(x, y)) setMethod("cbind2", c("timeSeries", "ANY"), function(x,y) callGeneric(x, as(y, "timeSeries"))) setMethod("cbind2", c("ANY", "timeSeries"), function(x,y) callGeneric(as(x, "timeSeries"), y)) setMethod("cbind2", c("timeSeries", "missing"), function(x,y) x) # ------------------------------------------------------------------------------ rbind.timeSeries <- function(..., deparse.level = 1) { # A function implemented by Yohan Chalabi and Diethelm Wuertz # Description: # Binds rows of two 'timeSeries' objects # Arguments: # ... # deparse.level # FUNCTION: # Row bind: dots <- list(...) # Preserve the title of the first ... element # counter <- NULL # for (i in 1:length(dots)) counter <- c(counter, is.timeSeries(dots[[i]])) # Index <- which(counter[1]) Title <- "" # dots[[Index]]@title # Compose Attributes - Documentation : Attributes <- list() for (i in 1:length(dots)) { if (class(dots[[i]]) == "timeSeries") { nextAttributes <- getAttributes(dots[[i]]) Attributes <- .appendList(Attributes, nextAttributes) } } Documentation <- as.character(date()) attr(Documentation, "Attributes") <- Attributes # Remove NULL from dots args if (any(t <- unlist(lapply(dots, is.null)))) dots[t] <- NULL # Coerce to timeSeries object if not a timeSeries if (any(t <- !unlist(lapply(dots, inherits, "timeSeries")))) dots[t] <- lapply(dots[t], as.timeSeries) if (diff(range((unlist(lapply(dots, ncol)))))) stop("number of columns must match") # get names of arguments if any otherwise use colnames units <- unlist(lapply(dots, colnames)) if (length(t <- as.logical((nchar(nm <- names(units)))))) units[t] <- nm[t] units <- structure(units, dim = c(ncol(dots[[1]]), length(dots))) units <- apply(units, 1, paste, collapse = "_") # Bind: # data <- base::rbind(...) # no because S3 method dispatch done in C level data <- do.call(base::rbind, lapply(dots, getDataPart)) if (any(unlist(lapply(dots, function(ts) ts@format == "counts")))) { return(timeSeries(data=data, units = units)) } # recordIDs part if (length(dots) > 1) recordIDs <- tryCatch(do.call(rbind, lapply(dots, slot, "recordIDs")), error = function(e) { msg <- paste("@recordIDs cannot be binded :", conditionMessage(e)) warning(msg, call. = FALSE) data.frame()}) else recordIDs <- slot(dots[[1]], "recordIDs") tds <- unlist(lapply(dots, slot, "positions")) ans <- timeSeries(data = data, charvec = tds, zone = "GMT", FinCenter = finCenter(dots[[1]]), units = units, recordIDs = recordIDs) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ ## # YC: ## # Note that since 2.9.0 S3 methods can not be defined for S4 classes ## # which extends an object like matrix. Therefore we turn all S3 ## # generics to S4 generics for backward compatibility ## # Note that since 2.8.0 it is possible to define methods for functions ## # with dots ... ## if (getRversion() < "2.9.0") { ## rbind.timeSeries <- ## function(..., deparse.level = 1) ## .rbind.timeSeries(..., deparse.level = deparse.level) ## } else { ## setGeneric("rbind", signature = "...") #-> creates warning but ## # cannot avoid it with ## # current dotsMethods scheme ## setMethod("rbind", "timeSeries", function(..., deparse.level = 1) ## .rbind.timeSeries(..., deparse.level = deparse.level)) ## } # ------------------------------------------------------------------------------ setMethod("rbind2", c("timeSeries", "timeSeries"), function(x, y) rbind(x, y)) setMethod("rbind2", c("timeSeries", "ANY"), function(x,y) callGeneric(x, as(y, "timeSeries"))) setMethod("rbind2", c("ANY", "timeSeries"), function(x,y) callGeneric(as(x, "timeSeries"), y)) setMethod("rbind2", c("timeSeries", "missing"), function(x,y) x) ################################################################################ timeSeries/R/stats-window.R0000644000176000001440000000750212620124746015412 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # window,timeSeries Extracts a piece from a 'timeSeries' object # DEPRECATED: DESCRIPTION: # cut,timeSeries Extracsts a piece from a 'timeSeries' object ################################################################################ .window.timeSeries <- function(x, start, end, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Windows a piece from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # from, to - two 'timeDate' position vectors which size the # blocks # Details: # from and to, are both included in the window. # Value: # Returns a S4 object of class 'timeSeries'. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # check if all argument names are used if (length(dot <- list(...))) { if (any(names(dot) %in% c("from", "to"))) { if (!is.null(from <- dot$from)) start <- from if (!is.null(to <- dot$to)) end <- to warning("Arguments 'from/to' are deprecated.\nUse instead 'start/end'.", call. = FALSE) } } start <- timeDate(start) end <- timeDate(end) Positions <- time(x) test <- (Positions >= start & Positions <= end) ans <- x[test,] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } setMethod("window", "timeSeries", function(x, start, end, ...) .window.timeSeries(x, start, end, ...)) # until UseMethod dispatches S4 methods in 'base' functions window.timeSeries <- function(x, ...) .window.timeSeries(x, ...) ############################################################################### .cut.timeSeries <- function (x, from, to, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Cuts out a piece from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # from, to - two 'timeDate' position vectors which size the # blocks # Value: # Returns a S4 object of class 'timeSeries'. # FUNCTION: # .Deprecated("window", "timeSeries") stopifnot(is.timeSeries(x)) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") from = timeDate(from) to = timeDate(to) Positions = time(x) test = (Positions >= from & Positions <= to) ans <- x[test,] # Return value: ans } setMethod("cut", "timeSeries", function (x, from, to, ...) .cut.timeSeries(x, from, to, ...)) # until UseMethod dispatches S4 methods in 'base' functions cut.timeSeries <- function(x, ...) .cut.timeSeries(x, ...) ################################################################################ timeSeries/R/fin-spreads.R0000644000176000001440000000513412620124746015161 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # spreads Computes spreads from a 'timeSeries' object # midquotes Computes mid quotes from a 'timeSeries' object ################################################################################ # DW: # Setting bid and ask for column names is maybe the best choice. Examples # are the TED spread or the Libo OIS spread. The spread between High and Low # is the range. # ------------------------------------------------------------------------------ spreads <- function(x, which = c("Bid", "Ask"), tickSize = NULL) { # A function implemented by Diethelm Wuertz # Description: # Computes spreads from a 'timeSeries' object # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Compute Spread: spread <- x[, which[2]] - x[, which[1]] if (!is.null(tickSize)) series(spread) <- round(series(spread)/tickSize) # Preserve Title and Documentation: spread@title <- Title spread@documentation <- Documentation # Return Value: spread } # ------------------------------------------------------------------------------ midquotes = function(x, which = c("Bid", "Ask")) { # A function implemented by Diethelm Wuertz # Description: # Computes mid quotes from a 'timeSeries' object # FUNCTION: # Check arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Compute Mid Quotes: midquotes = 0.5 * ( x[, which[1]] + x[, which[2]] ) # Preserve Title and Documentation: midquotes@title <- Title midquotes@documentation <- Documentation # Return Value: midquotes } ################################################################################ timeSeries/R/fin-monthly.R0000644000176000001440000001316512620124746015215 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: FOR MONTHLY OPERATIONS: # countMonthlyRecords Returns a series with monthly counts of records # rollMonthlyWindows Returns start/end dates for rolling time windows # rollMonthlySeries Rolls Monthly a 'timeSeries' on a given period ################################################################################ # DW: # I think we should call these functions: # countRecordsMonthly, rollWindowsMonthly, rollSeriesMonthly, ... # ------------------------------------------------------------------------------ countMonthlyRecords <- function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns a series with monthly counts of records # Example: # x = as.timeSeries(data(msft.dat)); countMonthlyRecords(x) # x = as.timeSeries(data(edhec)); countMonthlyRecords(x) # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Count: ans <- rollMonthlySeries(x[, 1], period = "1m", by = "1m", FUN = NROW) colnames(ans) <- "Counts" # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } # ------------------------------------------------------------------------------ rollMonthlyWindows <- function(x, period = "12m", by = "1m") { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns start and end dates for rolling time windows # Arguments: # x - a 'timeSerie's object of asset returns # period - a character string denoting the length of the rolling # window, e.g. "24m" means 24 months # by - a character string denoting the shift of the rolling window, # e.g. "3m" means one quarter # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Get Window Parameter: periodLength <- as.numeric(substr(period, 1, nchar(period)-1)) periodUnit <- substr(period, nchar(period), nchar(period)) byLength <- as.numeric(substr(by, 1, nchar(by)-1)) byUnit <- substr(by, nchar(by), nchar(by)) stopifnot(periodUnit == "m") stopifnot(byUnit == "m") # Get Window Parameter: periodLength <- as.numeric(substr(period, 1, nchar(period)-1)) periodUnit <- substr(period, nchar(period), nchar(period)) byLength <- as.numeric(substr(by, 1, nchar(by)-1)) byUnit <- substr(by, nchar(by), nchar(by)) stopifnot(periodUnit == "m") stopifnot(byUnit == "m") # Make Windows - We expect monthly data records ... positions <- time(x) Positions <- unique(timeFirstDayInMonth(positions)) numberOfPositions <- length(Positions) startDates <- Positions[1:(numberOfPositions-periodLength)] endDates <- Positions[(periodLength+1):numberOfPositions]-24*3600 # Windows: windows <- list(from = startDates, to = endDates) attr(windows, "control") = c(start = start(positions), end = end(positions)) # Return Value: windows } # ------------------------------------------------------------------------------ rollMonthlySeries <- function(x, period = "12m", by = "1m", FUN, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Rolls monthly a 'timeSeries' on a given period # Arguments: # x - a 'timeSerie's object of asset returns # period - a character string denoting the length of the rolling # window, e.g. "24m" means 24 months # by - a character string denoting the shift of the rolling window, # e.g. "3m" means one quarter # FUN - function to be applied # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: stopifnot(is(x, "timeSeries")) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Settings: windows <- rollMonthlyWindows(x = x[, 1], period = period, by = by) # Apply Function: ans <- applySeries(x = x, from = windows$from, to = windows$to, FUN = FUN, ...) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ timeSeries/R/base-t.R0000644000176000001440000000177312620124746014126 0ustar ripleyusers # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # t,timeSeries Returns the transpose of timeSeries object ################################################################################ setMethod("t", "timeSeries", function(x) callGeneric(getDataPart(x))) ################################################################################ timeSeries/R/base-rev.R0000644000176000001440000000221112620124746014443 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rev,timeSeries Reverts a 'timeSeries' object in time ################################################################################ .rev.timeSeries <- function(x) x[NROW(x):1,] setMethod("rev", "timeSeries", function(x) .rev.timeSeries(x)) # until UseMethod dispatches S4 methods in 'base' functions rev.timeSeries <- function(x) .rev.timeSeries(x) ################################################################################ timeSeries/R/stats-filter.R0000644000176000001440000000572612620124746015376 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # filter,timeSeries Applies linear filtering to a 'timeSeries' object ################################################################################ setMethod("filter", "timeSeries", function(x, filter, method = c("convolution", "recursive"), sides = 2, circular = FALSE, init = NULL) { # Description: # Applies linear filtering to a 'timeSeries' object # Arguments: # x - a univariate or multivariate time series. # filter - a vector of filter coefficients in reverse time order (as # for AR or MA coefficients). # method - Either "convolution" or "recursive" (and can be # abbreviated). If "convolution" a moving average is used: # if "recursive" an autoregression is used. # sides - for convolution filters only. If sides=1 the filter # coefficients are for past values only; if sides=2 they are # centred around lag 0. In this case the length of the filter # should be odd, but if it is even, more of the filter is # forward in time than backward. # circular - for convolution filters only. If TRUE, wrap the filter # around the ends of the series, otherwise assume external # values are missing (NA). # init - for recursive filters only. Specifies the initial values # of the time series just prior to the start value, in reverse # time order. The default is a set of zeros. # Value: # Returns a 'timeSeries' object. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Filter: ans <- filter(getDataPart(x), filter = filter, method = method, sides = sides, circular = circular, init = init) # Note: do not use as.matrix because ts objects might # not be coerced properly ans <- as(ans, "matrix") # Add Column Names: colnames(ans) <- colnames(x) ans <- setDataPart(x, ans) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans }) ################################################################################ timeSeries/R/utils-structure.R0000644000176000001440000000473712620124746016154 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # str,timeSeries Displays the structure of a 'timeSeries' object ################################################################################ .str.timeSeries <- function(object, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Displays the structure of a 'timeSeries' object. # Arguments: # object - an object of class 'timeSeries'. # ... - # FUNCTION: # Series Name: cat("Time Series: ") cat("\n Name: ", as.character(c(substitute(object)))) # YC : as.character(c( important to handle str(timeSeries()) # Data Matrix: Dim = dim(object) cat("\nData Matrix: ") cat("\n Dimension: ", Dim) cat("\n Column Names: ", colnames(object) ) firstName = rownames(object)[1] lastName = rownames(object)[Dim[1]] cat("\n Row Names: ", firstName, " ... ", lastName) # Date/Time Positions: cat("\nPositions: ") cat("\n Start: ", as.character(start(object))) cat("\n End: ", as.character(end(object))) # Other Attributes: cat("\nWith: ") cat("\n Format: ", object@format) cat("\n FinCenter: ", object@FinCenter) cat("\n Units: ", object@units) cat("\n Title: ", object@title) cat("\n Documentation: ", object@documentation) cat("\n") # Return Value: invisible() } setMethod("str", "timeSeries", function(object, ...) .str.timeSeries(object, ...)) # until UseMethod dispatches S4 methods in 'base' functions str.timeSeries <- function (object, ...) .str.timeSeries(object, ...) ################################################################################ timeSeries/R/methods-as.R0000644000176000001440000002564512620124746015023 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: CREATE A TIMESERIES FROM OTHER OBJECTS: # as.timeSeries Defines method for a 'timeSeries' object # as.timeSeries.default Returns the input # as.timeSeries.ts Transforms a 'data.frame' into a 'timeSeries' # as.timeSeries.data.frame Transforms a 'data.frame' into a 'timeSeries' # as.timeSeries.character Loads and transformas from a demo file # as.timeSeries.zoo Transforms a 'zoo' object into a 'timeSeries' # METHOD: TRANSFORM A TIMESERIES INTO OTHER OBJECTS: # as.vector.timeSeries Converts a univariate 'timeSeries' to a vector # as.matrix.timeSeries Converts a 'timeSeries' to a 'matrix' # as.numeric.timeSeries Converts a 'timeSeries' to a 'numeric' # as.data.frame.timeSeries Converts a 'timeSeries' to a 'data.frame' # as.ts.timeSeries Converts a 'timeSeries' to a 'ts' # as.ts.logical Converts a 'timeSeries' to 'logical' # as.list.timeSeries Converts a 'timeSeries' to 'list' ################################################################################ # YC: # here keep S3 methods because it should expect an oldClass object as argument # ------------------------------------------------------------------------------ as.timeSeries <- function(x, ...) { UseMethod("as.timeSeries") } # ------------------------------------------------------------------------------ as.timeSeries.default <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # FUNCTION: # Return Value: ans <- timeSeries(x, ...) ans } setAs("ANY", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.ts <- function(x, ...) { asTime <- unclass(time(x)) yearPart <- trunc(asTime) decimalPart <- asTime - yearPart leapYears <- yearPart%%4 == 0 & (yearPart%%100 != 0 | yearPart%%400 == 0) days <- trunc(decimalPart * (365 + leapYears)) + 1 freq <- frequency(x) charvec <- if (freq == 4) { # Quarterly Data: days <- days + 1 ans <- timeDate(format(strptime(paste(yearPart, days), format = "%Y %j")), zone = "GMT", FinCenter = "GMT") timeLastDayInQuarter(ans) } else if (freq == 12) { # Monthly Data: days <- days + 1 ans <- timeDate(format(strptime(paste(yearPart, days), format = "%Y %j")), zone = "GMT", FinCenter = "GMT") timeLastDayInMonth(ans) } else { NA } # Result: tS = timeSeries(x, charvec, ...) attr(tS, "ts") <- c(start = round(start(x)), frequency = round(frequency(x)), deltat = deltat(x)) # Return Value: tS } setAs("ts", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.data.frame <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Converts a data.frame into a timeSeries object # Notes: # The first column must contain the dates. # Examples: # data(bmwRet); head(as.timeSeries(data(bmwRet))) # FUNCTION: if (all(!(num <- unlist(lapply(x, is.numeric))))) stop("x contains no numeric columns") # Check if rownames(x) or the first column has a valid ISO-format: if (num[1]) # is.numeric() is better than format == "unkown" # which can give wrong result. i.e. whichFormat(0.1253328600) suppressWarnings(charvec <- timeDate(rownames(x))) else suppressWarnings(charvec <- timeDate(as.vector(x[,1]))) data <- as.matrix(x[, num]) units <- names(x)[num] if (any(!(cl <- num[-1]))) { recordIDs <- as.data.frame(x[, !c(TRUE, cl)]) # do not take first column names(recordIDs) <- names(x)[!c(TRUE, cl)] } else { recordIDs <- data.frame() } # Create Time Series Object: timeSeries(data = data, charvec = charvec, units = units, recordIDs = recordIDs, ...) } setAs("data.frame", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.character <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Example: # as.timeSeries(data(nyse)) # FUNCTION: # Load Demo File - Returns a data frame: x <- eval(parse(text = eval(x))) # timeSeries: ans <- as.timeSeries(x, ...) # Return Value: ans } setAs("character", "timeSeries", function(from) as.timeSeries(from)) # ------------------------------------------------------------------------------ as.timeSeries.zoo <- function(x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # FUNCTION: # as. timeSeries: ans <- timeSeries(data = as.matrix(x), charvec = as.character(attr(x, "index")), ...) # Return Value: ans } ################################################################################ # YC: # Since 2.9.0 must define proper S4 methods .as.matrix.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a multivariate "timeSeries" to a matrix # Arguments: # x - a 'timeSeries' object # Value: # Returns the data slot of a 'timesSeries' object as a vector. # FUNCTION: # Check: if (!inherits(x, "timeSeries")) stop("x is not a timeSeries object!") # Convert: ans <- getDataPart(x) # is matrix dimnames(ans) <- dimnames(x) # Results ans } setMethod("as.matrix", "timeSeries", function(x, ...) .as.matrix.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.matrix.timeSeries <- function(x, ...) .as.matrix.timeSeries(x, ...) setAs("timeSeries", "matrix", function(from) as.matrix(from)) # ------------------------------------------------------------------------------ .as.data.frame.timeSeries <- function(x, row.names = NULL, optional = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a multivariate "timeSeries" to a data.frame # Arguments: # x - a 'timeSeries' object # row.names, optional - not used # Value: # Returns the data slot of a 'timesSeries' object as a data frame. # FUNCTION: # get rownames from timeSeries if (is.null(row.names)) row.names <- rownames(x) if (any(duplicated(row.names))) stop("cannot convert to data.frame with duplicate timestamps") ans <- if (!length(x@recordIDs)) data.frame(as.list(x), row.names = row.names, ...) else data.frame(as.list(x), x@recordIDs, row.names = row.names, ...) # Return Value: ans } setMethod("as.data.frame", "timeSeries", function(x, row.names = NULL, optional = FALSE, ...) .as.data.frame.timeSeries(x, row.names = row.names, optional = optional, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.data.frame.timeSeries <- function(x, ...) .as.data.frame.timeSeries(x, ...) setAs("timeSeries", "data.frame", function(from) as.data.frame(from)) # ------------------------------------------------------------------------------ .as.ts.timeSeries <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Converts a colum from a 'timeSeries' object into an object # of class 'ts'. # Example: # # x = dummySeries(); as.ts(x) # # x = timeSeries(seq(12), timeSequence(by = "month", length.out = 12)) # as.ts(x) # # x = dummySeries()[c(3,6,9,12),]; as.ts(x) # x = dummySeries()[c(2,5,8,11),]; as.ts(x) # x = dummySeries()[c(1,4,7,10),]; as.ts(x) # # x = dummySeries()[c(4,7,10,1),]; as.ts(x) # Changes: # # FUNCTION: # check if monthly or quarterly data td <- time(x) m <- c(timeDate::months(td)) #-> c() to remove attributes # (m[1] -1) -> shift vector to match first entry in m monthly <- seq(from = m[1]-1, length.out=length(m)) %% 12 + 1 quarterly <- seq(from = m[1]-1, by = 3, length=length(m)) %% 12 + 1 # get year of first entry y1 <- as.numeric(format(td[1], "%Y")) # important to use vector/matrix to avoid troubles with ts() data <- if (isUnivariate(x)) as.vector(x) else matrix(x, ncol = ncol(x)) if (identical(monthly, m)) # Monthly data return(ts(data, start = c(y1, m[1]), frequency = 12)) if (identical(quarterly, m)) # Quarterly data return(ts(data, start = c(y1, m[1]%/%4+1), frequency = 4)) # other frequencies not implemented yet; return default value ans <- ts(data, names = colnames(x)) attr(ans, "positions") <- time(x) ans } setMethod("as.ts", "timeSeries", function(x, ...) .as.ts.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.ts.timeSeries <- function(x, ...) .as.ts.timeSeries(x, ...) setAs("timeSeries", "ts", function(from) as.ts(from)) # ------------------------------------------------------------------------------ # YC: # Unneeded since timeSeries inherits from the structure class # as.logical.timeSeries <- function(x, ...) as.logical(series(x), ...) # ------------------------------------------------------------------------------ # YC: # Important for functions like lapply and sapply to work properly .as.list.timeSeries <- function(x, ...) { data <- getDataPart(x) ncols <- NCOL(data) value <- vector("list", ncols) for (i in seq.int(ncols)) value[[i]] <- as.vector(data[, i]) names(value) <- colnames(x) value } setMethod("as.list", "timeSeries", function(x, ...) .as.list.timeSeries(x, ...)) # until UseMethod dispatches S4 methods in 'base' functions as.list.timeSeries <- function(x, ...) .as.list.timeSeries(x, ...) setAs("timeSeries", "list", function(from) as.list(from)) ################################################################################ timeSeries/R/base-apply.R0000644000176000001440000000570112620124746015003 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # apply Applies a function to blocks of a 'timeSeries' ################################################################################ setMethod("apply", "timeSeries", function(X, MARGIN, FUN, ...) { # A function implemented by Siethelm Wuertz and Yohan Chalabi # Description: # Apply Functions Over 'Array'timeSeries' Margins # Arguments: # X - an array, including a matrix. # MARGIN - a vector giving the subscripts which the function # will be applied over. E.g., for a matrix 1 indicates rows, # 2 indicates columns, c(1, 2) indicates rows and columns. # Where X has named dimnames, it can be a character vector # selecting dimension names. # FUN - the function to be applied: see ???Details???. In the case # of functions like +, %*%, etc., the function name must be # backquoted or quoted. # ... - optional arguments to FUN. # Value: # Returns a vector or array or list of values obtained by # applying a function to margins of a 'timeSeries'. If the # returned value is a matrix, and if the input argument X and # the returned value have the same number of rows, then the # returned value will be transformed into a 'timeSeries' object. # FUNCTION # Check arguments: stopifnot(is.timeSeries(X)) # Extract Title and Documentation: Title <- X@title Documentation <- X@documentation # Settings: pos <- X@positions rec <- X@recordIDs FinCenter <- finCenter(X) X <- getDataPart(X) ans <- callGeneric() # Manage when univariate timeSeries drops the apply to vector: if( is(ans, "vector") && identical(length(ans), NROW(X)) ) { ans <- matrix(ans, ncol=1) } # Result: if (is(ans, "matrix") && identical(NROW(ans), NROW(X))) { # Compose timeSeries ans <- timeSeries( data = ans, charvec = pos, one = FinCenter, FinCenter = FinCenter, recordIDs = rec) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation } # Return Value: ans }) ############################################################################### timeSeries/R/timeSeries-signalCounts.R0000644000176000001440000000267612620124746017536 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .signalCounts Creates charvec for integer indexed time stamps ################################################################################ .signalCounts <- function(int) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Descriptions: # Creates the charvec for integer indexed time stamps # Arguments: # int - a vector of integers, the counts. # FUNCTION: # Check that int is an integer # ... # Check that all int's are positive ... # ... # Format: cint <- as.character(int) ans <- format(cint, width = max(nchar(cint)), justify = "right") # Return Value: ans } ################################################################################ timeSeries/R/timeSeries-isRegular.R0000644000176000001440000000336512620124746017016 0ustar ripleyusers # This R package 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 R package 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 R package; if not, write to the # Free Foundation, Inc., 59 Temple Place, Suite 330, Boston, # MA 02111-1307 USA ################################################################################ # FUNCTION: DESCRIPTION: # isDaily,timeSeries-method Tests if a time series is a daily series # isMonthly,timeSeries-method Tests if a time series is a monthly series # isQuarterly,timeSeries-method Tests if a time series is a quarterly series # isRegular,timeSeries-method Tests if a time series is a regular series # frequency,timeSeries-method Returns the frequency of a regular time series ################################################################################ setMethod("isDaily", "timeSeries", function(x) callGeneric(time(x))) setMethod("isQuarterly", "timeSeries", function(x) callGeneric(time(x))) setMethod("isMonthly", "timeSeries", function(x) callGeneric(time(x))) setMethod("isRegular", "timeSeries", function(x) callGeneric(time(x))) setMethod("frequency", "timeSeries", function(x, ...) callGeneric(time(x), ...)) ################################################################################ timeSeries/R/base-scale.R0000644000176000001440000000311312620124746014740 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # scale,timeSeries Centers and/or scales a 'timeSeries' object ################################################################################ .scale.timeSeries <- function(x, center = TRUE, scale = TRUE) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Centers and/or scales a 'timeSeries' object. # Arguments: # FUNCTION: # Scale: setDataPart(x, scale(x = getDataPart(x), center = center, scale = scale)) } setMethod("scale", "timeSeries", function(x, center = TRUE, scale = TRUE) .scale.timeSeries(x, center = center, scale = scale)) # until UseMethod dispatches S4 methods in 'base' functions scale.timeSeries <- function (x, center = TRUE, scale = TRUE) .scale.timeSeries(x, center = center, scale = scale) ################################################################################ timeSeries/R/fin-periodical.R0000644000176000001440000001412312620124746015631 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # endOfPeriodSeries Returns series back to a given period # endOfPeriodStats Returns statistics back to a given period # endOfPeriodBenchmarks Returns benchmarks back to a given period ################################################################################ endOfPeriodSeries <- function(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns series back to a given period # Arguments: # x - a monthly 'timeSeries' object of returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Check: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Settings: if (nYearsBack == "YTD") monthsBack = atoms(end(x))$m else if (nYearsBack == "1y") monthsBack = 12 else if (nYearsBack == "2y") monthsBack = 24 else if (nYearsBack == "3y") monthsBack = 36 else if (nYearsBack == "5y") monthsBack = 60 else if (nYearsBack == "10y") monthsBack = 120 stopifnot( nrow(x) >= monthsBack ) # ReturnValue: rev(rev(x)[1:monthsBack, ]) } # ------------------------------------------------------------------------------ endOfPeriodStats <- function(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns series statistics back to a given period # Arguments: # x - a monthly 'timeSeries' object of returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Check: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Series: Series <- endOfPeriodSeries(x, nYearsBack = nYearsBack) # Internal Function: .cl.vals <- function(x, ci) { x = x[!is.na(x)] n = length(x) if (n <= 1) return(c(NA, NA)) se.mean = sqrt(var(x)/n) t.val = qt((1 - ci)/2, n - 1) mn = mean(x) lcl = mn + se.mean * t.val ucl = mn - se.mean * t.val c(lcl, ucl) } # Statistics: for (i in 1:ncol(Series)) { # Basic Statistics: X = as.vector(Series[, i]) X.length = length(X) X = X[!is.na(X)] X.na = X.length - length(X) ci = 0.95 z = c(X.length, X.na, min(X), max(X), as.numeric(quantile(X, prob = 0.25, na.rm = TRUE)), as.numeric(quantile(X, prob = 0.75, na.rm = TRUE)), mean(X), median(X), sum(x), sqrt(var(X)/length(X)), .cl.vals(X, ci)[1], .cl.vals(X, ci)[2], var(X), sqrt(var(X)), skewness(X), kurtosis(X)) znames = c("nobs", "NAs", "Minimum", "Maximum", "1. Quartile", "3. Quartile", "Mean", "Median", "Sum", "SE Mean", "LCL Mean", "UCL Mean", "Variance", "Stdev", "Skewness", "Kurtosis") stats1 <- matrix(z, ncol = 1) row.names(stats1) <- znames # Monthly Return Statistics: xData <- as.vector(x) noNegativePeriods <- length(xData[xData < 0 ]) noPositivePeriods <- length(xData[xData > 0 ]) stats1 = rbind(stats1, worstPeriod = min(xData), negativeValues = noNegativePeriods, positiveValues = noPositivePeriods) MaximumDrawdown = NA TimeUnderWater = NA AnnualizedVolatility = NA SharpeRatio = NA InformationRatio = NA ValueAtRisk = NA ExpectedShortfall = NA # Bind: if (i > 1) { stats <- cbind.data.frame(stats, stats1) } else { stats <- stats1 } } colnames(stats) <- colnames(x) # Return Value: stats } # ------------------------------------------------------------------------------ endOfPeriodBenchmarks <- function(x, benchmark = ncol(x), nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) { # A function implemented by Diethelm Wuertz # Description: # Returns benchmarks back to a given period # Arguments: # x - a monthly 'timeSeries' object of financial returns # nYearsBack - a period string. How long back should the series # be extracted? Options include values from 1 year to 10 years. # and year to date: "1y", "2y", "3y", "5y", "10y", "YTD". # FUNCTION: # Checks: stopifnot(is.timeSeries(x)) # Match Arguments: nYearsBack <- match.arg(nYearsBack) # Series: Series <- endOfPeriodSeries(x[, -benchmark], nYearsBack = nYearsBack) y <- Benchmark <- endOfPeriodSeries(x[, benchmark], nYearsBack = nYearsBack) stats <- NULL for (i in 1:ncol(Series)) { # Gdet Series: x <- Series[, i] # Compute Statistics: stats1 <- c( TrackingError = NA, Alpha = NA, Beta = NA, CorrelationToBenchmark = NA) # Bind Results: stats <- rbind(stats, stats1) } # Return Value: invisible() } ################################################################################ timeSeries/R/timeSeries.R0000644000176000001440000002551112620124746015060 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .signalSeries Creates a signal series from scratch # .timeSeries Creates a time series from scratch # METHODS: # timeSeries,ANY,ANY # timeSeries,matrix,missing # timeSeries,matrix,timeDate # timeSeries,matrix,numeric # timeSeries,matrix,ANY ################################################################################ ## .signalSeries : generate units, title, documentation if NULL ## data must be a matrix .signalSeries <- function(data, charvec, units = NULL, format, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # Description: # Arguments: # Note: # it is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check # FUNCTION: if (!is(data, "matrix")) data <- as(data, "matrix") # Add units, title and Documentation: if (is.null(units)) units <- colnames(data) if (is.null(units)) units <- paste("SS.", seq.int(dim(data)[2]), sep = "") if (is.null(title)) title = "Signal Series Object" if (is.null(documentation)) documentation = as.character(date()) # remove rownames of data but keep colnames for # functions like var, cov ... # Note that if it fails, new("timeSeries" should fail to - normal try(dimnames(data) <- list(NULL, units), silent = TRUE) ### new("signalSeries", ### .Data = data, ### units = units, ### recordIDs = recordIDs, ### title = title, ### documentation = documentation) new("timeSeries", .Data = data, units = units, positions = numeric(0), FinCenter = "", format = "counts", recordIDs = recordIDs, title = title, documentation = documentation) } # ------------------------------------------------------------------------------ ## .timeSeries : generate units, title, documentation if NULL ## data must be a matrix and charvec a timeDate object .timeSeries <- SERIES <- function(data, charvec, units = NULL, format, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # Description: # Creates a time series from scratch # Arguments: # Note: # it is possible that a ts object is considered as a # matrix when timeSeries method as dispatched. Hence this check # FUNCTION: if (!is(data, "matrix")) data <- as(data, "matrix") stopifnot(is(charvec, "numeric")) # Add units, title and Documentation: if (is.null(units)) units <- colnames(data) if (is.null(units)) units <- paste("TS.", seq.int(dim(data)[2]), sep = "") if (is.null(title)) title <- "Time Series Object" if (is.null(documentation)) documentation <- as.character(date()) if (missing(format)) format <- "%Y-%m-%d" if (identical("", FinCenter)) FinCenter <- "GMT" # Remove rownames of data but keep colnames for # functions like var, cov ... # Note that if it fails, new("timeSeries" should fail to - normal try(dimnames(data) <- list(NULL, units), silent = TRUE) positions <- charvec # as.numeric(charvec, "sec") attributes(positions) <- NULL new("timeSeries", .Data = data, positions = positions, units = units, format = format, # charvec@format, FinCenter = FinCenter, # charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation) } # ------------------------------------------------------------------------------ ## missing ANY setMethod("timeSeries", signature(data = "missing", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { .signalSeries(data = matrix(NA), units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## missing missing setMethod("timeSeries", signature(data = "missing", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { .signalSeries(data = matrix(NA), units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## ANY ANY setMethod("timeSeries", signature(data = "ANY", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { data <- as(data, "matrix") if (!is(data, "matrix")) stop("Could not coerce 'data' to a matrix") callGeneric(data = data, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## ANY missing setMethod("timeSeries", signature(data = "ANY", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { data <- as(data, "matrix") if (!is(data, "matrix")) stop("Could not coerce 'data' to a matrix") callGeneric(data = data, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) }) # ------------------------------------------------------------------------------ ## matrix missing setMethod("timeSeries", signature(data = "matrix", charvec = "missing"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { charvec <- rownames(data) if (is.null(charvec)) { .signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) } else { callGeneric(data = data, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } } ) # ------------------------------------------------------------------------------ ## matrix timeDate setMethod("timeSeries", signature(data = "matrix", charvec = "timeDate"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { if (any(is.na(charvec))) return(.signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...)) if (any(!c(zone, FinCenter) %in% "")) charvec <- timeDate(charvec, format = format, zone = zone, FinCenter = FinCenter) .timeSeries(data = data, charvec = as.numeric(charvec, "sec"), units = units, format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) # ------------------------------------------------------------------------------ ## matrix numeric setMethod("timeSeries", signature(data = "matrix", charvec = "numeric"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { if (any(!c(zone, FinCenter) %in% "")) { td <- timeDate(charvec, zone = zone, FinCenter = FinCenter) charvec <- as.numeric(td, "sec") FinCenter <- finCenter(td) } .timeSeries(data = data, charvec = charvec, units = units, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) # ------------------------------------------------------------------------------ ## matrix ANY setMethod("timeSeries", signature(data = "matrix", charvec = "ANY"), function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) { # if charvec NULL returns a signal series if (is.null(charvec)) return(.signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...)) # coerce charvec to timeDate charvec <- timeDate(charvec = charvec, format = format, zone = zone, FinCenter = FinCenter) if (any(is.na(charvec))) # Note : there is already a warning in timeDate if there are NA's .signalSeries(data = data, units = units, recordIDs = recordIDs, title = title, documentation = documentation, ...) else .timeSeries(data = data, charvec = as.numeric(charvec, "sec"), units = units, format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } ) ################################################################################ timeSeries/R/methods-mathOps.R0000644000176000001440000001342312620124746016022 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # Ops,timeSeries Returns group 'Ops' for a 'timeSeries' object # cummax Returns cumulated maxima # cummin Returns cumulated minima # cumprod Returns cumulated products # cumsum Returns cumulated sums # ##diff,timeSeries Differences a timeSeries object # ##scale,timeSeries Scales a timeSeries object # quantile,timeSeries Samples qunatiles of a timeSeries object ################################################################################ setMethod("Ops", c("vector", "timeSeries"), function(e1, e2) { lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("array", "timeSeries"), function(e1, e2) { e1 <- as.vector(e1) lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("ts", "timeSeries"), function(e1, e2) { e1 <- as(e1, "matrix") lattrs <- attributes(e2) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e2))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "vector"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "array"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "ts"), function(e1, e2) { lattrs <- attributes(e1) e1 <- getDataPart(e1) e2 <- as(e2, "matrix") value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("Ops", c("timeSeries", "timeSeries"), function(e1, e2) { # Note keep recordIDs of e1 only # check if conformable arrays if (!identical(dim(e1), dim(e2))) stop("non-conformable arrays") # check if positions are identical if (!identical(e1@positions, e2@positions)) stop("positions slot do not match") lattrs <- attributes(e1) e1 <- getDataPart(e1) e2 <- getDataPart(e2) value <- callGeneric(e1, e2) if (identical(dim(value), dim(e1))) { attributes(value) <- lattrs value <- asS4(value, TRUE) } value } ) # ------------------------------------------------------------------------------ setMethod("cummax", "timeSeries", function(x) callGeneric(getDataPart(x))) # ------------------------------------------------------------------------------ setMethod("cummin", "timeSeries", function(x) callGeneric(getDataPart(x))) # ------------------------------------------------------------------------------ setMethod("cumprod", "timeSeries", function(x) callGeneric(getDataPart(x))) # ------------------------------------------------------------------------------ setMethod("cumsum", "timeSeries", function(x) callGeneric(getDataPart(x))) # ------------------------------------------------------------------------------ ## setMethod("diff", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ ## setMethod("scale", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ setMethod("quantile", "timeSeries", function(x, ...) { x <- getDataPart(x) callGeneric() }) ################################################################################ timeSeries/R/methods-show.R0000644000176000001440000001162612620124746015372 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # show,timeSeries Prints a 'timeSeries' object # print,timeSeries Prints a 'timeSeries' object # .print.timeSeries Called by function print,timeSerie ################################################################################ setMethod("show", "timeSeries", function(object) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Print method for an S4 object of class "timeSeries" # FUNCTION: # Check records to get printed: maxRmetrics <- as.numeric(getRmetricsOptions("max.print")) maxR <- as.numeric(getOption("max.print")) maxR <- floor(maxR / (NCOL(object) + NCOL(object@recordIDs))) max <- min(na.omit(c(maxRmetrics, maxR, Inf))) #-> Inf to cast case when maxRmetrics and maxR are NULL if (ptest <- ((omitted <- NROW(object) - max) > 0)) object <- object[seq.int(max),] data <- as(object, "matrix") recordIDs <- object@recordIDs FinCenter <- finCenter(object) # Series: cat(FinCenter, "\n", sep = "") if (prod(dim(recordIDs)) & (nrow(data) == NROW(recordIDs))) { dataIDs <- as.matrix(recordIDs) colnames(dataIDs) <- paste(colnames(dataIDs), "*", sep = "") #-> use format(data) to have same number of digits when timeSeries # is printed without @recordIDs print(cbind(format(data), dataIDs), quote = FALSE, right = TRUE) } else { print(data, quote = FALSE) #-> to be consistent with @recordIDs print } # print message if (ptest) cat(gettextf("...\n [ reached getRmetricsOption('max.print') | getOption('max.print') -- omitted %i rows ]]\n", omitted)) # Return Value: invisible(NULL) # as specified in ?show } ) # ------------------------------------------------------------------------------ .print.timeSeries <- function(x, FinCenter = NULL, format = NULL, style = c("tS", "h", "ts"), by = c("month", "quarter"), ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Allows for horizontal and ts like print output. # Arguments: # x - an object of class timeSeries # FinCenter - print with time stamps according to FinCenter # format - use specified format for printing # style - a character value specifying how to print: # "tS" Rmetrics' default vertical print style # "h" horizontal print style, # "ts" R's base style for regular time series # by - specifies the period for a regular time serie, # note only active for style="ts". # Example: # x = timeSeries(); print(x, format = "%d %b %Y"); x # FUNCTION: # Change Financial Center: if (!is.null(FinCenter)) finCenter(x) <- FinCenter # Match Arguments: style = match.arg(style) by = match.arg(by) # Change Format: if (is.null(format)) { charvec = rownames(x) } else { ans = timeDate(charvec = rownames(x), zone = "GMT", FinCenter = finCenter(x)) if (format == "%Q") { Quarters = rep(paste("Q", 1:4, sep = ""), each = 3) Y = atoms(ans)[, 1] Q = Quarters[atoms(ans)[, 2]] charvec = paste(Y, Q) } else { charvec = format(ans, format) } } # Styles: if (style == "tS") { cat(finCenter(x), "\n") X <- getDataPart(x) rownames(X) = charvec print(X) } else if (style == "h") { stopifnot(isUnivariate(x)) # print(as.vector(x)) ans = as.matrix(x)[,1] names(ans) = charvec print(ans) } else if (style == "ts") { freq = c(month = 12, quarter = 4) start(x) start = unlist(atoms(start(x))) end = unlist(atoms(end(x))) ts = ts(as.vector(x), start[1:2], end[1:2], freq[by]) print(ts) } # Return Value: invisible(x) } # ------------------------------------------------------------------------------ setMethod("print", "timeSeries", .print.timeSeries) ################################################################################ timeSeries/R/timeSeries-getDataPart.R0000644000176000001440000000527712620124746017265 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHODS: DESCRIPTION: # getDataPart,timeSeries Get data slot of an object of class 'timeSeries' # setDataPart,timeSeries Set data slot of an object of class 'timeSeries' ################################################################################ # YC: # This makes getDataPart a bit faster than default function setMethod("getDataPart", "timeSeries", #"signalSeries", function(object) { value <- object attributes(value) <- NULL attr(value, "dim") <- attr(object, "dim") attr(value, "dimnames") <- attr(object, "dimnames") value } ) # ------------------------------------------------------------------------------ # YC: # This makes setDataPart a bit faster than default function if (getRversion() < "2.8.0") { setMethod("setDataPart", "timeSeries", function(object, value) { #-> Note : do not use as.matrix because ts objects might #-> not be coerced properly value <- as(value, "matrix") supplied <- attributes(object) valueAttrs <- attributes(value) supplied[names(valueAttrs)] <- valueAttrs # YC: force @unit to be identical to colnames(value) supplied[["units"]] <- colnames(value) attributes(value) <- supplied asS4(value, TRUE) } ) } else { setMethod("setDataPart", "timeSeries", function(object, value, check = TRUE) { #-> Note : do not use as.matrix because ts objects might #-> not be coerced properly if (check) value <- as(value, "matrix") supplied <- attributes(object) valueAttrs <- attributes(value) supplied[names(valueAttrs)] <- valueAttrs # YC: force @unit to be identical to colnames(value) supplied[["units"]] <- colnames(value) attributes(value) <- supplied asS4(value, TRUE) } ) } ################################################################################ timeSeries/R/stats-lag.R0000644000176000001440000000754512620124746014655 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # lag,timeSeries Lags a 'timeSeries' object ################################################################################ setMethod("lag" , "timeSeries", function(x, k = 1, trim = FALSE, units = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Lags a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object. # k - an integer indicating which lag to use. By default 1. # Note, negative lags are to data in the future. # trim - a logical. Should NAs at the beginning of the # series be removed? By default FALSE. # units - # ... - # Details: # The arguments differ in the following way from the function # stats::lag - lag(x, k = 1, ...) # Value: # Returns a lagged object of class 'timeSeries'. # Example: # SPI = 100* as.timeSeries(data(LPP2005REC))[1:20, "SPI"] # Note negative lags are to data in the future ! # lag(SPI, k = -2:2) # lag(SPI, k = 0:2 , trim = TRUE) # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Internal Function: tslagMat <- function(x, k = 1) { # Internal Function: tslag1 = function(x, k) { y = x if (k > 0) y = c(rep(NA, times = k), x[1:(length(x)-k)]) if (k < 0) y = c(x[(-k+1):length(x)], rep(NA, times = -k)) y } # Bind: ans <- NULL for (i in k) { ans <- cbind(ans, tslag1(x, i)) } # As Vector: if (length(k) == 1) ans <- as.vector(ans) # Return Value: ans } # Convert: y <- getDataPart(x) Dim <- dim(y)[2] # Lag on each Column: z <- NULL for (i in 1:Dim) { ts <- tslagMat( y[, i], k = k) #, trim = FALSE) z <- cbind(z, ts) } # Positions pos <- x@positions # Record IDs: df <- x@recordIDs # Trim: if (trim){ idx <- !is.na(apply(z, 1, sum)) z <- z[idx, , drop = FALSE] pos <- pos[idx] if (sum(dim(df)) > 0) { df <- df[idx, , drop = FALSE] rownames(df) <- seq.int(sum(idx)) } } # Augment Colnames: cn <- colnames(x) a <- if (is.null(units)) # ensure that colnames is replicated according to the length # of lag indexes. as.vector(matrix(cn, nrow = length(k), ncol = length(cn), byrow = TRUE)) else units kcols <- rep(k, times = ncol(y)) b <- paste("[", kcols, "]", sep="") ab <- paste(a, b, sep = "") units <- ab # Result: ans <- timeSeries(data = z, charvec = pos, units = units, format = x@format, FinCenter = x@FinCenter, recordIDs = df) # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans }) # until UseMethod dispatches S4 methods in 'base' functions lag.timeSeries <- function(x, ...) timeSeries::lag(x, ...) ################################################################################ timeSeries/R/fin-drawdowns.R0000644000176000001440000001525012620124746015530 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # drawdowns Generate 'timeSeries' object of drawdown levels # drawdownsStats Compute drawdown stats for univariate time series # FUNCTION: DESCRIPTION: # .drawdownsHistPlot Displays a histogram plot ################################################################################ drawdowns <- function(x, ...) { # A function implemented by Diethelm Wuertz and Tobias Setz # Description: # Generate 'timeSeries' object of drawdown levels # Arguments: # x - an uni- or multivariate 'timeSeries' object of financial # returns # ... - arguments passed to the function na.omit() # Value: # returns an object of class 'timeSeries'. # FUNCTION: # Check Arguments stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: r <- na.omit(x, ...) # The starting point of every time series is set to zero. # This ensures that the starting value for cumprodReturns below # (which is the wealth index) is 1. startup <- timeSeries(data=t(rep(0, ncol(r))), charvec=time(r)[1]) # Preset Drawdowns: nms <- colnames(r) drawdowns <- r <- rbind(startup,r) colnames(drawdowns) <- colnames(r) <- nms # Compute multivariate 'timeSeries' of Drawdowns: cumprodReturns <- colCumprods(1 + r) cummaxReturns <- colCummaxs(cumprodReturns) series(drawdowns) <- series(cumprodReturns)/series(cummaxReturns) - 1 drawdowns <- drawdowns[-1, ] # Preserve Title and Documentation: drawdowns@title <- Title drawdowns@documentation <- Documentation # Return Value: drawdowns } # ------------------------------------------------------------------------------ drawdownsStats = function(x, ...) { # A function implemented by Diethelm Wuertz and Tobias Setz # Description: # Finds the drawdowns in an univariate 'timeSeries' object # Arguments: # x - an uni- or multivariate 'timeSeries' object of financial # returns # ... - arguments passed to the function drawdowns() # Value: # returns an object of class 'data.frame' returning # drawdown - the depth of the drawdown # from - the start date # trough - the trough period # to - the end date # length - the length in number of records # peaktrough - the peak trough # recovery - the recovery length in number of records # Author: # Based on Peter Carl, # partly from contributed R package Performance Analytics # Note: # modified with permission from function by Sankalp Upadhyay # # Examples: # x = drawdownsStats(as.timeSeries(data(edhec))[,1]) # FUNCTION: # Check Arguments: stopifnot(isUnivariate(x)) # Compute Drawdowns: drawdowns <- as.vector(drawdowns(x, ...)) time <- time(x) # Find Drawdowns from a Numeric Vector of Returns: draw <- begin <- end <- length <- trough <- c() index <- 1 if (drawdowns[1] >= 0) { priorSign <- 1 } else { priorSign <- 0 } from <- 1 sofar <- drawdowns[1] to <- 1 dmin <- 1 for (i in 2:length(drawdowns)) { thisSign <- ifelse(drawdowns[i] < 0, 0, 1) if (thisSign == priorSign) { if(drawdowns[i]< sofar) { sofar <- drawdowns[i] dmin <- i } to <- i + 1 } else { # @todo: recovery time (in days) draw[index] <- sofar begin[index] <- from trough[index] <- dmin end[index] <- to #cat(sofar, from, to) from <- i sofar <- drawdowns[i] to <- i + 1 dmin <- i index <- index + 1 priorSign <- thisSign } } draw[index] <- sofar begin[index] <- from trough[index] <- dmin end[index] <- to ## length: as.timeDate(pos[x$to])-as.timeDate(pos[x$from]) # If the time series ends in the middle of a drawdown, return the last # date of the time series and set the recovery time to NA endt <- end; endr <- end; if(to > length(time)) { endt[index] <- to - 1 endr[index] <- NA } # Result - an index list with all drawdowns ... ans <- data.frame(from = as.vector(as.character(time[begin])), trough = as.vector(as.character(time[trough])), to = as.vector(as.character(time[endt])), drawdown = as.vector(draw), length = (end - begin + 1), peaktotrough = (trough - begin + 1), recovery = (endr - trough), stringsAsFactors = FALSE) attr(ans, "series") <- x attr(ans, "names") <- c("From", "Trough", "To", "Depth", "Length", "ToTrough", "Recovery") ans <- ans[ans[, "Depth"] < 0, ] # Order Drawdowns: ans <- ans[order(ans[, "Depth"]), ] rownames(ans) <- 1:dim(ans)[1] # Return Value: ans } # ------------------------------------------------------------------------------ if (FALSE) { .drawdownsHistPlot <- function(x, labels = TRUE, col = "steelblue", add.fit = TRUE, rug = TRUE, skipZeros = TRUE, ...) { # Note: # We require(fExtremes) move this function to fAssets # Check Arguments: stopifnot(isUnivariate(x)) # Plot Drawdowns Histogram: X = drawdowns(x, ...) histPlot(X, labels = labels, col = col, add.fit = FALSE, rug = rug, skipZeros = skipZeros, ...) # Add GPD Fit: if (add.fit) { z = -as.vector(X) par = gpdFit(z, u = 0)@fit$par.ests u = seq(0, max(abs(z)), length = 200) v = dgpd(u, xi = par[1], mu = 0, beta = par[2]) lines(-u, v, col = "brown", lwd = 2) } # return Value: invisible() } } ################################################################################ timeSeries/R/statistics-orderColnames.R0000644000176000001440000001304112620124746017727 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # statsColnames Returns statistically rearranged column names # orderColnames Returns ordered column names of a time Series # sortColnames Returns sorted column names of a time Series # sampleColnames Returns sampled column names of a time Series # pcaColnames Returns PCA correlation ordered column names # hclustColnames Returns hierarchical clustered column names ################################################################################ statsColnames = function(x, FUN = colMeans, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns statistically rearranged column names # Arguments: # x - an object of class timeSeries # FUN - a character string, the name of the function to be used # ... - optional arguments to be passed to FUN # Note: # Example of function Candidates: # colStats calculates column statistics, # colSums calculates column sums, # colMeans calculates column means, # colSds calculates column standard deviations, # colVars calculates column variances, # colSkewness calculates column skewness, # colKurtosis calculates column kurtosis, # colMaxs calculates maximum values in each column, # colMins calculates minimum values in each column, # colProds computes product of all values in each column, # colQuantiles computes quantiles of each column. # FUNCTION: # Apply colStats Function: fun = match.fun(FUN) Sort = sort(fun(x, ...)) Order = names(Sort) ans = colnames(as.matrix(x)[, Order]) attr(ans, "control") <- Sort # Return Value: ans } # ------------------------------------------------------------------------------ orderColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns ordered column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Order: ans = order(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ sortColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns sorted column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Sort: ans = sort(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ sampleColnames = function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns sampled column names of a time Series # Arguments: # x - an object of class timeSeries # FUNCTION: # Sample: ans = sample(colnames(as.matrix(x)), ...) # Return Value: ans } # ------------------------------------------------------------------------------ pcaColnames = function(x, robust = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns PCA correlation ordered column names # Arguments: # x - an object of class timeSeries # robust - a logical, should we use robust covariance estimation? # FUNCTION: # Order: if (robust) { x.cor = robustbase::covMcd(as.matrix(x), cor = TRUE, ...)$cor } else { x.cor = cor(as.matrix(x), ...) } x.eigen = eigen(x.cor)$vectors[,1:2] e1 = x.eigen[, 1] e2 = x.eigen[, 2] Order = order(ifelse(e1 > 0, atan(e2/e1), atan(e2/e1)+pi)) ans = colnames(as.matrix(x))[Order] # Return Value: ans } # ------------------------------------------------------------------------------ hclustColnames = function(x, method = c("euclidean", "complete"), ...) { # A function implemented by Diethelm Wuertz # Description: # Returns hierarchical clustered column names # Arguments: # x - an object of class timeSeries # method - the agglomeration method to be used. This should # be (an unambiguous abbreviation of) one of "ward", "single", # "complete", "average", "mcquitty", "median" or "centroid". # ... optional arguments passed to the function hclust # FUNCTION: # Order: Order = hclust(dist(t(as.matrix(x)), method = method[1]), method = method[2], ...)$order ans = colnames(as.matrix(x))[Order] # Return Value: ans } ################################################################################ timeSeries/R/fin-durations.R0000644000176000001440000000457712620124746015542 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: FINANCIAL TIME SERIES: # durations Computes durations from a 'timeSeries' object ################################################################################ durations <- function(x, trim = FALSE, units = c("secs", "mins", "hours", "days")) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Computes durations from a financial price series # Arguments: # x - a univariate or multivariate 'timeSeries' object or a # numeric vector or matrix. # trim - a logical flag, by default TRUE, the first missing # observation in the return series will be removed. # units - a character value or vector which allows to set the # units in which the durations are measured # Value: # Returns a S4 object of class 'timeSeries'. # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Check for Signal Series: if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Match Arguments: units <- match.arg(units) # Get Positions and Durations: pos <- time(x) dur <- c(NA, diff(as.integer(difftime(pos, pos[1], units = units[1])))) # Data Matrix: ans <- timeSeries(data = dur, charvec = pos, units = "Duration") if (trim) ans <- ans[-1, ] # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Series: ans } ################################################################################ timeSeries/R/base-dim.R0000644000176000001440000002021512620124746014424 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHOD: DIM OPERATIONS ON DATA: # dim,timeSeries Returns dimension of a 'timeSeries' object # dim<-,timeSeries Assigns dimension of a 'timeSeries' object # dimnames,timeDSeries Returns dimension names of a 'timeSeries' object # dimnames<-,timeSeries Assign dimension names of a 'timeSeries' object # colnames,timeSeries Return column names to a 'timeSeries' object # rownames,timeSeries Return row names to a 'timeSeries' object # colnames<-,timeSeries Assigns column names to a 'timeSeries' object # rownames<-,timeSeries Assigns row names to a 'timeSeries' object # names,timeSeries Return column names of a 'timeSeries' object # names<.,timeSeries Assigns column names of a 'timeSeries' object ################################################################################ # Base Functions: # Generate from Matrix: # edhec.tS = timeSeries(edhec.mat, rownames(edhec.mat)) # edhec.ts = ts(edhec.mat, start = c(1997, 1), frequency = 12) # Univariate time Series: # edhec1.tS = edhec.tS[, 1] # dim # dim(edhec.tS) # 20 4 # dim(edhec1.tS) # 20 1 # DIM # DIM = function(x) {c(NROW(x), NCOL(x))} # DIM(edhec.tS) # 20 4 # DIM(edhec1.tS) # 20 1 # length # length(edhec.tS) # 1 # # LENGTH # LENGTH = function(x) NROW(x) # LENGTH(edhec.tS) # 20 # LENGTH(edhec1.tS) # 20 # # ncol / nrow # ncol(edhec.tS) # 4 # # ncol(edhec1.tS) # 1 # # NCOL / NRWO # NCOL(edhec.tS) # 4 # # NCOL(edhec1.tS) # 1 # # isUnivariate # isUnivariate = function(x) NCOL(x) == 1 # isUnivariate(edhec.tS) # isUnivariate(edhec1.tS) # # isMultivariate # Just Negation of isUnivariate # # # # ------------------------------------------------------------------------------ # length # dim # ncol # nrow # LENGTH # DIM # NCOL # NROW # ------------------------------------------------------------------------------ # Note it is faster to access attribute rather than accessing @.Data setMethod("dim", "timeSeries", function(x) attr(x, "dim")) # This should make functions like # model.response(model.frame(dummySeries() ~1)) work setReplaceMethod("dim", "timeSeries", function(x, value) { # dim(x) <- NULL returns a vector if (is.null(value)) return(as.vector(x)) else x #<< returns same object : # setting new dim # is forbidden for a timeSeries object } ) # ------------------------------------------------------------------------------ # colnames - faster to have dedicated method than relying on dimnames[[2]] setMethod("colnames", "timeSeries", # "signalSeries", function(x, do.NULL = TRUE, prefix = "col") x@units ) # ------------------------------------------------------------------------------ # rownames ## setMethod("rownames", "signalSeries", ## function (x, do.NULL = TRUE, prefix = "row") NULL) ## setMethod("rownames", "timeSeries", ## function (x, do.NULL = TRUE, prefix = "row") as.character(time(x))) setMethod("rownames", "timeSeries", function (x, do.NULL = TRUE, prefix = "row") { if (length(x@positions) > 0) as.character(time(x)) else NULL } ) # ------------------------------------------------------------------------------ setMethod("dimnames", "timeSeries", # "signalSeries", function(x) { list(rownames(x),colnames(x)) } ) # ------------------------------------------------------------------------------ setMethod("colnames<-", "timeSeries", function(x, value) { units <- as.character(value) if(!length(units)) if (x@format == "counts") units <- paste("SS", seq(NCOL(x)), sep = ".") else units <- paste("TS", seq(NCOL(x)), sep = ".") if (length(units) != NCOL(x)) stop("length of 'colnames' not equal to array extent",call.=FALSE) x@units <- units colnames(x@.Data) <- units x } ) # ------------------------------------------------------------------------------ setMethod("rownames<-", c("timeSeries", "timeDate"), #c("signalSeries", "timeDate"), function (x, value) { .timeSeries( data = getDataPart(x), charvec = as.numeric(value, "sec"), units = colnames(x), format = value@format, FinCenter = value@FinCenter, recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) } ) # ------------------------------------------------------------------------------ setMethod("rownames<-", "timeSeries", # "signalSeries", function (x, value) { # if charvec NULL returns a signal series if (is.null(value)) return(.signalSeries(data = getDataPart(x), units = colnames(x), recordIDs = x@recordIDs, title = x@title, documentation = x@documentation)) # coerce charvec to timeDate charvec <- timeDate(charvec = value) if (any(is.na(charvec))) # Note : there is already a warning in timeDate if there are NA's .signalSeries(data = getDataPart(x), units = colnames(x), recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) else .timeSeries(data = getDataPart(x), charvec = as.numeric(charvec, "sec"), units = colnames(x), format = charvec@format, FinCenter = charvec@FinCenter, recordIDs = x@recordIDs, title = x@title, documentation = x@documentation) } ) # ------------------------------------------------------------------------------ setMethod("dimnames<-", c("timeSeries", "list"), # c("signalSeries", "list"), function(x, value) { rownames(x) <- value[[1]] colnames(x) <- value[[2]] x } ) # ------------------------------------------------------------------------------ # important for completion with $ setMethod("names", "timeSeries", # "signalSeries", function(x) c(colnames(x), names(x@recordIDs))) setReplaceMethod("names", "timeSeries", # "signalSeries", function(x, value) { nc <- ncol(x) nv <- length(value) nr <- length(x@recordIDs) # Note that using [][] ensure that length of the # names are equal to array extent colnames(x) <- value[seq.int(nv)][seq.int(nc)] if (nv > nc) names(x@recordIDs) <- value[-seq.int(nc)][seq.int(nr)] x }) ################################################################################ timeSeries/R/AllGeneric.R0000644000176000001440000000653312620124746014757 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # GENERIC: DESCRIPTION # returns Computes returns # rowCumsums Computes row cumulated sums # series Extracts series data # series<- Assigns series data # coredata Extracts series data # index deprecated # index <- deprecated # outlier Returns outliers # timeSeries Returns timeSeries # colCumsums Computes column cumulated sums # colCummaxs Computes column cumulated maxima # colCummins Computes column cumulated minima # colCumprods Computes column cumulated products # colCumreturns Computes column cumulated returns ################################################################################ setGeneric("returns", function(x, ...) standardGeneric("returns"), package = "timeSeries") setGeneric("rowCumsums", function(x, na.rm = FALSE, ...) standardGeneric("rowCumsums"), package = "timeSeries") setGeneric("series", function(x) standardGeneric("series"), package = "timeSeries") setGeneric("series<-", function(x, value) standardGeneric("series<-"), package = "timeSeries") setGeneric("coredata", function(x) standardGeneric("coredata"), package = "timeSeries") setGeneric("coredata<-", function(x, value) standardGeneric("coredata<-"), package = "timeSeries") ## setGeneric("index", function(x, ...) ## standardGeneric("index"), package = "timeSeries") ## setGeneric("index<-", function(x, value) ## standardGeneric("index<-"), package = "timeSeries") setGeneric("outlier", function(x, sd = 5, complement = TRUE, ...) standardGeneric("outlier")) setGeneric("timeSeries", function (data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, ...) standardGeneric("timeSeries")) setGeneric("colCumsums", function(x, na.rm = FALSE, ...) standardGeneric("colCumsums")) setGeneric("colCummaxs", function(x, na.rm = FALSE, ...) standardGeneric("colCummaxs")) setGeneric("colCummins", function(x, na.rm = FALSE, ...) standardGeneric("colCummins")) setGeneric("colCumprods", function(x, na.rm = FALSE, ...) standardGeneric("colCumprods")) setGeneric("colCumreturns", function(x, method = c("geometric", "simple"), na.rm = FALSE, ...) standardGeneric("colCumreturns")) ################################################################################ timeSeries/R/timeSeries-isOHLC.R0000644000176000001440000000432712620124746016141 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # .isOHLC Is the series an Open-High-Low-Close series? # .isOHLCV Is the series an Open-High-Low-Close-Volume series? ################################################################################ # DW: # I think we need a better method to detect if a series is a OHLC(V) series # or not. A possible approach would be: # any High >= Open, Close, Low # any Low <= Open, Close, High # Volume >= 0 # number of columns 4(5) # ----------------------------------------------------------------------------- .isOHLC <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Is the series an Open-High-Low-Close series? # Arguments: # object - an object of class 'timeSeries' # FUNCTION: colNames <- c("Open", "High", "Low", "Close") if (colnames(object)[1:4] == colNames) { return(TRUE) } else { return(FALSE) } } # ------------------------------------------------------------------------------ .isOHLCV <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Is the series an Open-High-Low-Close-Volume series? # Arguments: # object - an object of class 'timeSeries' # FUNCTION: colNames <- c("Open", "High", "Low", "Close", "Volume") if (colnames(object) == colNames) { return(TRUE) } else { return(FALSE) } } ################################################################################ timeSeries/R/fin-wealth.R0000644000176000001440000000237212620124746015005 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # index2wealth Converts an index to a wealth series ############################################################################### index2wealth <- function(x) { # A function implemented by Diethelm Wuertz # Description: # Converts an index to a wealth series # FUNCTION: # x - index time series to be converted # FUNCTION: # Wealth Initialization: for (i in 1:ncol(x)) x[, i] <- x[, i]/as.vector(x[1, i]) # Return Value: x } ############################################################################### timeSeries/R/base-applySeries.R0000644000176000001440000002523212620124746016157 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # applySeries Applies a function to blocks of a 'timeSeries' # fapply Applies a function to 'timeSeries' windows # DEPRECATED: DESCRIPTION: # .applySeries Applies a function to blocks of a 'timeSeries' # .fapply Applies a function to 'timeSeries' windows ################################################################################ applySeries <- function(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, ...) { # A function implemented by Diethelm Wuertz # Description: # Apply a function to the margins of a 'timeSeries' object # Details: # This function can be used to aggregate and coursen a # 'timeSeries' object. # Arguments: # x - a 'timeSeries' object to be aggregated # from, to - two 'timeDate' position vectors which size the # blocks # by - calendarical block, only active when both 'from' # and 'to' are NULL # FUN - function to be applied, by default 'colMeans' # units - a character vector with column names, allows to # overwrite the column names of the input 'timeSeries' # object. # Value: # Returns a S4 object of class 'timeSeries'. # Notes: # The size of the 'moving' window and the selection of an # 'adj'-acent endpoint are not needed, all the information # is kept in the 'from' and 'to' position vectors. # FUNCTION: # .Deprecated("aggregate", "timeSeries") # Check object: if (class(x) != "timeSeries") stop("s is not a timeSeries object") ### if (x@format == "counts") ### stop(as.character(match.call())[1], ### " is for time series and not for signal series.") # Monthly and Quarterly from and to: if (is.null(from) & is.null(to)) { if (by[1] == "monthly") { # Use monthly blocks: from = unique(timeFirstDayInMonth(time(x))) to = unique(timeLastDayInMonth(time(x))) } else if (by[1] == "quarterly") { from = unique(timeFirstDayInQuarter(time(x))) to = unique(timeLastDayInQuarter(time(x))) } else { stop("by must be eiter monthly or quarterly") } from@FinCenter = to@FinCenter = FinCenter } # Column Names: colNames = units # Function: fun = match.fun(FUN) ### # Blocks: ### j.pos = as.POSIXct(time(x)) ### j.from = as.POSIXct(from) ### j.to = as.POSIXct(to) # Blocks: j.pos = time(x) if (is(j.pos, "timeDate")) { j.from = as.timeDate(from) j.to = as.timeDate(to) } else { j.from = as.integer(from) j.to = as.integer(to) } # Iterate: pos = time(x) rowNames = rownames(x) rowBind = NULL for (i in seq_len(length(from))) { test <- (j.pos >= j.from[i] & j.pos <= j.to[i]) if (!sum(test)) stop("outsite of range") # make sure that cutted is a matrix ... cutted = as.matrix(x[test, ]) # YC : *AND* make sure the matrix is not subbsetted to a vector!!! # YC : here it is fine because as.matrix of a timeSeries checks it # YC : but prefer to check it one more time at the end of the loop... ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ans = fun(cutted, ...) rowBind = rbind(rowBind, ans) } stopifnot(NCOL(rowBind) == NCOL(x)) # YC : see above # YC : length(to) might not be == NCOL(rowBind) if (length(as.character(to)) == NROW(rowBind)) rownames(rowBind) = as.character(to) if (is.null(colNames)) { units = x@units } else { units = colNames } # Return Value: timeSeries(data = rowBind, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title, documentation = documentation, ...) } # ------------------------------------------------------------------------------ fapply <- function(x, from, to, FUN, ...) { # .Deprecated("aggregate", "timeSeries") # Check x: stopifnot(is(x, "timeSeries")) if (x@format == "counts") stop(as.character(match.call())[1], " is for time series and not for signal series.") # Check for missing form/to: if(missing(from)) from = start(x) if(missing(to)) to = end(x) # Return Value: applySeries(x = x, from = from, to = to, FUN = FUN, ...) } ################################################################################ # *** OLD *** # Check if it is still used somewhere ... .applySeries <- function (x, from = NULL, to = NULL, by = c("monthly", "quarterly"), FUN = colMeans, units = NULL, ...) { # Old/Alternative Version # Chreck for 'timeSeries' Object: stopifnot(is.timeSeries(x), is(from, "timeDate") || is.null(from), is(to, "timeDate") || is.null(to)) # Allow for colMeans: if (substitute(FUN) == "colMeans") FUN = "colAvgs" # Monthly and Quarterly from and to: if (is.null(from) & is.null(to)) { by = match.arg(by) if (by == "monthly") { from = unique(timeFirstDayInMonth(time(x))) to = unique(timeLastDayInMonth(time(x))) } else if (by == "quarterly") { from = unique(timeFirstDayInQuarter(time(x))) to = unique(timeLastDayInQuarter(time(x))) } from@FinCenter = to@FinCenter = x@FinCenter } # Start Cutting Process: fun = match.fun(FUN) cutted = NULL i = 1 # Find First Interval which is not empty: while (is.null(cutted)) { cutted = cut(x, from[i], to[i]) if (!is.null(cutted)) { # Non empty Interval: ans = fun(cutted, ...) } i = i + 1 } # Continue up to the end: for (j in seq_len(length(from))) { cutted = cut(x, from[j], to[j]) if (!is.null(cutted)) { # Non empty Interval: newAns = fun(cutted, ...) ans = rbind(ans, newAns) } } # Return Value: ans } ################################################################################ # *** OLD *** # Check if it is still used somewhere ... .fapply <- function(x, from, to, FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Applies a function to 'timeSeries' windows # Details: # This function can be used to aggregate and coursen a # 'timeSeries' object. # Arguments: # x - a 'timeSeries' object to be aggregated # from, to - two 'timeDate' position vectors which size the blocks # FUN - function to be applied, by default 'colMeans' # Value: # Returns a S4 object of class 'timeSeries' if FUN returns # a time series object, otherwise a list, where the entries # for each window is the output of the function FUN. # Notes: # The size of the 'moving' window and the selection of an # 'adj'-acent endpoint are not needed, all the information # is kept in the 'from' and 'to' position vectors. # FUNCTION: # Check object: if (class(x) != "timeSeries") stop("s is not a timeSeries object") # Monthly and Quarterly from and to: if (is.null(from) & is.null(to)) { if (by[1] == "monthly") { # Use monthly blocks: from = unique(timeFirstDayInMonth(time(x))) to = unique(timeLastDayInMonth(time(x))) } else if (by[1] == "quarterly") { from = unique(timeFirstDayInQuarter(time(x))) to = unique(timeLastDayInQuarter(time(x))) } else { stop("by must be eiter monthly or quarterly") } from@FinCenter = to@FinCenter = x@FinCenter } # Column Names: colNames = units # Function: fun = match.fun(FUN) # Blocks: j.pos = as.POSIXct(time(x)) j.from = as.POSIXct(from) j.to = as.POSIXct(to) # Iterate: y = series(x) pos = time(x) rowNames = rownames(x) # Compute for the first window ... i = 1 test = (j.pos >= j.from[i] & j.pos <= j.to[i]) # make sure that cutted is a matrix ... cutted = as.matrix(y[test, ]) ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ans = fun(cutted, ...) if (is.timeSeries(ans)) { ## DW can this happen - check ? rowBind = ans for (i in 2L:length(from)) { test = (j.pos >= j.from[1] & j.pos <= j.to[1]) # make sure that cutted is a matrix ... cutted = as.matrix(y[test, ]) ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ans = fun(cutted, ...) rowBind = rbind(rowBind, ans) } rownames(rowBind) = as.character(to) if (is.null(colNames)) { units = x@units } else { units = colNames } # Return Value: ans = timeSeries(data = rowBind, charvec = as.character(to), units = units, format = format, zone = x@zone, FinCenter = x@FinCenter, recordIDs = x@recordIDs, title = x@title, documentation = x@documentation, ...) return(ans) } else { listBind = list() ## DW [] -> [[]] listBind[[1]] = ans for (i in 2L:length(from)) { test = (j.pos >= j.from[i] & j.pos <= j.to[i]) # make sure that cutted is a matrix ... cutted = as.matrix(y[test, ]) ### if (sum(test)>0) rownames(cutted) <- rowNames[test] ans = fun(cutted, ...) ## DW [] -> [[]] listBind[[i]] = ans } # Return Value: ans = listBind attr(ans, "control") <- list(x = x, from = from, to = to) return(invisible(ans)) } # Return Value: return() } ################################################################################ timeSeries/R/fin-cumulated.R0000644000176000001440000000573212620124746015507 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ############################################################################### # FUNCTION: DESCRIPTION: # cumulated Computes cumulated series from financial returns # cumulated.default Computes cumulated series, default method ############################################################################### cumulated <- function(x, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes cumulated series from financial returns # Return Value: UseMethod("cumulated") } # ------------------------------------------------------------------------------ cumulated.default <- function(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes cumulated series from financial returns # supports 'matrix' and 'timeSeries'. # Arguments: # x - data object containing ordered price observations # method - "continuous == "compound" and "discrete" == "simple" # Example: # X = as.timeSeries(data(msft.dat))[1:10, "Close"]; X = X/series(X)[1, 1] # x = returns(X, "continuous"); x; X; cumulated(x, "continuous") # x = returns(X, "discrete"); x; X; cumulated(x, "discrete") # Note: # To make it conform with PortfolioAnalytics: # "compound" == "continuous", and "simple" == "discrete" # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(x)) # Settings: method <- match.arg(method) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: # if (na.rm) x = na.omit(x, ...) # Transform data: if (percentage) x <- x/100 positions <- time(x) # Calculate Cumulates: # ... colCumsums and colCumprods are generic functions with # methods for 'matrix' and 'timeSeries'. if(method == "geometric") { ans <- colCumsums(x) } if(method == "compound" || method == "continuous") { ans <- exp(colCumsums(x)) } if(method == "simple" || method == "discrete") { ans <- colCumprods(1+x) } # Preserve Title and Documentation: ans@title <- Title ans@documentation <- Documentation # Return Value: ans } ################################################################################ timeSeries/R/statistics-rollMean.R0000644000176000001440000003030712620124746016707 0ustar ripleyusers # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # rollStats Returns rolling statistics of a 'timeSeries' object # rollMean Returns rolling mean of a 'timeSeries' object # rollMin Returns rolling minimum of a 'timeSeries' object # rollMax Returns rolling maximum of a 'timeSeries' object # rolMedian Returns rolling median of a 'timeSeries' object # DEPRECATED: DESCRIPTION: # .rollmean.timeSeries Returns rolling mean of a 'timeSeries' object # .rollmin.timeSeries Returns rolling minimum of a 'timeSeries' object # .rollmax.timeSeries Returns rolling maximum of a 'timeSeries' object # .rolmedian.timeSeries Returns rolling median of a 'timeSeries' object ################################################################################ rollStats <- function(x, k, FUN=mean, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling statistics of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # FUN - statistical function to be rolled. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to function FUN. # Note: # Internal function are borrowed from package zoo ... # Example: # x <- timeSeries(matrix(sample(1:24), ncol = 2), timeCalendar()) # cbind(x, roll(x, k=3, FUN = mean, align="right", na.pad = TRUE)) # Internal Function: .rollstats <- function(x, k, fun, na.pad = FALSE, align = c("center", "left", "right"), ...) { window <- matrix(1:(length(x)+1-k), ncol = 1) winFun <- function(i, fun, y, k, ...) { fun = match.fun(fun) from = 1:(length(y)+1-k) to = from + k -1 fun(y[i:to[i]], ...) } rval <- apply(window, 1, FUN=winFun, fun=FUN, y=x, k=k, ...) if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, FUN=.rollstats, k=k, fun=FUN, na.pad=na.pad, align=align, ...) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RSTATS", sep = "_") if(!na.pad) x = na.omit(x) # Return Value x } ############################################################################### rollMean <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling mean of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMean(x = X, k = 3); R; plot(R) # FUNCTION: # Internal Function: .rollmean.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) y <- x[k:n] - x[c(1, 1:(n-k))] y[1] <- sum(x[1:k]) rval <- cumsum(y)/k if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, .rollmean.default, k = k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMEAN", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMax <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling maximum of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMax(x = X, k = 3); plot(R) # FUNCTION: # Internal Function: .rollmax.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) rval <- rep(0, n) a <- 0 for (i in k:n) { rval[i] <- if (is.na(a) || is.na(rval[i=1]) || a==rval[i-1]) max(x[(i-k+1):i]) else max(rval[i-1], x[i]); # max of window = rval[i-1] a <- x[i-k+1] # point that will be removed from window } rval <- rval[-seq(k-1)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(getDataPart(x), 2, .rollmax.default, k = k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMAX", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMin <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling minimum of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol = 2), timeCalendar()) # R = rollMin(x = X, k = 3); R; plot(R) # FUNCTION: .rollmax.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { n <- length(x) rval <- rep(0, n) a <- 0 for (i in k:n) { rval[i] <- if (is.na(a) || is.na(rval[i=1]) || a==rval[i-1]) max(x[(i-k+1):i]) else max(rval[i-1], x[i]); # max of window = rval[i-1] a <- x[i-k+1] # point that will be removed from window } rval <- rval[-seq(k-1)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } .rollmin.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { ans = -.rollmax.default(-x, k, na.pad = na.pad, align = align, ...) ans } # Roll: ans <- apply(getDataPart(x), 2, .rollmin.default, k=k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMIN", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } # ------------------------------------------------------------------------------ rollMedian <- function(x, k, na.pad=FALSE, align=c("center", "left", "right"), ...) { # Description: # Returns rolling median of a 'timeSeries' object # Arguments: # x - an object of class timeSeries # k - integer width of the rolling window. Must be odd for rollmedian. # na.pad - a logical. Should NA padding be added at beginning? # align - character specifying whether result should be left- or # right-aligned or centered (default). # ... - furter arguments passed to methods. # Note: # Internal function are borrowed from package zoo ... # Example: # X = timeSeries(matrix(rnorm(24), ncol=2), timeCalendar()) # R = rollMedian(x = X, k = 3); R; plot(R) # FUNCTION: # Internal Function: .rollmedian.default <- function(x, k, na.pad = FALSE, align = c("center", "left", "right"), ...) { stopifnot(k <= length(x), k %% 2 == 1) n <- length(x) m <- k %/% 2 rval <- runmed(x, k, ...) attr(rval, "k") <- NULL rval <- rval[-c(1:m, (n-m+1):n)] if (na.pad) { rval <- switch(match.arg(align), "left" = { c(rval, rep(NA, k-1)) }, "center" = { c(rep(NA, floor((k-1)/2)), rval, rep(NA, ceiling((k-1)/2))) }, "right" = { c(rep(NA, k-1), rval) }) } rval } # Roll: ans <- apply(x, 2, .rollmedian.default, k=k, na.pad=na.pad, align=align) x <- setDataPart(x[seq.int(1,NROW(ans)),], ans) colnames(x) <- paste(colnames(x), "RMED", sep = "_") if(!na.pad) x = na.omit(x) # Return Value: x } ################################################################################ .rollmean.timeSeries <- function(...) { # FUNCTION: # Deprecated: .Deprecated("rollMean") # Return Value: rollMean(...) } # ------------------------------------------------------------------------------ .rollmin.timeSeries <- function(...) { # FUNCTION: # Deprecated: .Deprecated("rollMin") # Return Value: rollMin(...) } # ------------------------------------------------------------------------------ .rollmax.timeSeries <- function(...) { # FUNCTION: # Deprecated: .Deprecated("rollMax") # Return Value: rollMax(...) } # ------------------------------------------------------------------------------ .rollmedian.timeSeries <- function(...) { # FUNCTION: # Deprecated: .Deprecated("rollMedian") # Return Value: rollMedian(...) } ################################################################################ timeSeries/R/timeSeries-dummy.R0000644000176000001440000000511412620124746016206 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # dummySeries Creates a dummy monthly 'timeSeries' object # dummyDailySeries Creates a dummy daily 'timeSeries' object ################################################################################ # DW: # A more natural name for the function dummySeries() would be # dummyMonthlySeries() to have the same naming conventions like in the # case of the dummy daily series. dummySeries <- function(...) { # A function implemented by Diethelm Wuertz # Description: # Creates a monthly dummy 'time Series' object # Arguments: # ... - optional arguments passed to the function timeSeries(). # FUnction: # Return Value: timeSeries(matrix(runif(24), ncol = 2), as.character(timeCalendar()), ...) } # ------------------------------------------------------------------------------ dummyDailySeries <- function(x = rnorm(365), units = NULL, zone = "", FinCenter = "") { # A function implemented by Diethelm Wuertz # Description: # Creates a dummy daily time Series # Arguments: # x - a numeric vector # origin - the first date in the series # FUNCTION: if (zone == "") zone <- getRmetricsOptions("myFinCenter") if (FinCenter == "") FinCenter <- getRmetricsOptions("myFinCenter") # Check: stopifnot(is.numeric(x)) if (is.null(units)) units <- paste("X", 1:NCOL(x), sep = "") stopifnot(length(units) == NCOL(x)) # Time Series: if (is.vector(x)) data = matrix(x, ncol = 1) if (is.matrix(x)) data = x positions <- timeSequence(from = "1970-01-01", length.out = NROW(data), zone = zone, FinCenter = FinCenter) ans <- timeSeries(data = data, charvec = positions, units = units, zone = zone, FinCenter = FinCenter) # Return Value: ans } ################################################################################ timeSeries/R/timeSeries-slotSeries.R0000644000176000001440000002141412620124746017210 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # series,timeSeries Get data slot from a'timeSeries' # series<-,timeSeries,ANY Set new data slot to a 'timeSeries' # series<-,timeSeries,matrix Set new data slot to a 'timeSeries' # SYNONYMES: DESCRIPTION: # coredata,timeSeries Get data slot from a'timeSeries' # coredata<-,timeSeries,ANY Set new data slot to a 'timeSeries' # coredata<-,timeSeries,matrix Set new data slot to a 'timeSeries' # FUNCTION: DESCRIPTION: # getSeries # getSeries.default # getSeries.timeSeries Get data slot from a 'timeSeries' # setSeries<- Set new data slot to a 'timeSeries' ################################################################################ # DEPRECATED: DESCRIPTION: # seriesData Deprecated, use series ################################################################################ setMethod("series", "timeSeries", function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns the series Data of an ordered data object. # Arguments: # x - a 'timeSeries' object # Value: # Returns an object of class 'matrix'. # FUNCTION: # Get Data Slot: ans <- as.matrix(x) # Return Value: ans } ) # ------------------------------------------------------------------------------ setMethod("series<-", signature(x = "timeSeries", value = "ANY"), function(x, value) { # A function implemented by Yohan Chalabi # Return Value: callGeneric(x, as(value, "matrix")) } ) # ------------------------------------------------------------------------------ setMethod("series<-", signature(x = "timeSeries", value = "matrix"), function(x, value) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Assign the series Data to a timeSeries object. # Arguments: # object - a 'timeSeries' object # Value: # Assign to be assign as series Data of a timeSeries. # FUNCTION: # if value same dimension as time series # we we can assign the value directly to @.Data # This can speed up math Ops significantly if (identical(dim(x), dim(value))) { x@.Data <- value if (!is.null(cn <- colnames(value))) colnames(x) <- cn return(x) } if (is.null(charvec <- rownames(value))) charvec <- rownames(x) if (is.null(units <- colnames(value))) units <- colnames(value) # now that we have charvec and units, better to remove # dimnames of value to avoid problems attr(value, "dimnames") <- NULL if (!identical(length(units), NCOL(value))) units <- NULL # if now same dim , drop charvec and returns .signalSeries if (!identical(length(charvec), NROW(value))) return(.signalSeries(value, units)) format <- x@format zone <- FinCenter <- finCenter(x) title <- x@title documentation <- x@documentation recordIDs <- if (identical(NROW(x), NROW(value))) x@recordIDs else data.frame() # Return Value: timeSeries(data = value, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title) } ) ################################################################################ # COREDATA SYNONYME setMethod("coredata", "timeSeries", function(x) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Returns the series Data of an ordered data object. # Arguments: # x - a 'timeSeries' object # Value: # Returns an object of class 'matrix'. # FUNCTION: # Get Data Slot: ans <- as.matrix(x) # Return Value: ans }) # ------------------------------------------------------------------------------ setMethod("coredata<-", signature(x = "timeSeries", value = "ANY"), function(x, value) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Return Value: callGeneric(x, as(value, "matrix")) }) # ------------------------------------------------------------------------------ setMethod("coredata<-", signature(x = "timeSeries", value = "matrix"), function(x, value) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Assign the series Data to a timeSeries object. # Arguments: # object - a 'timeSeries' object # Value: # Assign to be assign as series Data of a timeSeries. # FUNCTION: # if value same dimension as time series # we we can assign the value directly to @.Data # This can speed up math Ops significantly if (identical(dim(x), dim(value))) { x@.Data <- value if (!is.null(cn <- colnames(value))) colnames(x) <- cn return(x) } if (is.null(charvec <- rownames(value))) charvec <- rownames(x) if (is.null(units <- colnames(value))) units <- colnames(value) # now that we have charvec and units, better to remove # dimnames of value to avoid problems attr(value, "dimnames") <- NULL if (!identical(length(units), NCOL(value))) units <- NULL # if now same dim , drop charvec and returns .signalSeries if (!identical(length(charvec), NROW(value))) return(.signalSeries(value, units)) format <- x@format zone <- FinCenter <- finCenter(x) title <- x@title documentation <- x@documentation recordIDs <- if (identical(NROW(x), NROW(value))) x@recordIDs else data.frame() # Return Value: timeSeries(data = value, charvec = charvec, units = units, format = format, zone = zone, FinCenter = FinCenter, recordIDs = recordIDs, title = title) }) ################################################################################ ## getSeries <- ## function(x) ## { ## # A function implemented by Diethelm Wuertz ## ## # Note: ## # Used for getSeries methods in fPortfolio package. ## ## # FUNCTION: ## ## # Return Value: ## UseMethod("getSeries") ## } # ------------------------------------------------------------------------------ ## getSeries.default <- ## function(x) ## { ## # Description: ## # Get data slot from a 'timeSeries' object ## ## # Arguments: ## # x - a 'timeSeries' object ## ## # FUNCTION: ## ## # Return Value: ## series(x) ## } # ------------------------------------------------------------------------------ ## getSeries.timeSeries <- ## function(x) ## { ## # Description: ## # Get data slot from a 'timeSeries' object ## ## # Arguments: ## # x - a 'timeSeries' object ## ## # FUNCTION: ## ## # Return Value: ## series(x) ## } # ------------------------------------------------------------------------------ ## "setSeries<-" <- ## function(x, value) ## { ## # Description: ## # Set data slot to a 'timeSeries' object ## ## # Arguments: ## # x - a 'timeSeries' object ## ## # FUNCTION: ## ## # Assign Series Slot: ## series(x) <- value ## ## # Return Value: ## x ## } ################################################################################ # DEPRECATED seriesData <- function(object) { # A function implemented by Diethelm Wuertz # Description: # Returns the series Data of an ordered data object. # Arguments: # object - a 'timeSeries' object # Value: # Returns an object of class 'matrix'. # FUNCTION: # Test: if (class(object) != "timeSeries") stop("Object is not a time Series") # Deprecated .Deprecated(new = "series", package = "timeSeries") # Get Data Slot: ans <- as.matrix(object) # Return Value: ans } ################################################################################ timeSeries/R/base-sample.R0000644000176000001440000000211712620124746015135 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # sample,timeSeries Resamples a 'timeSeries' object in time ################################################################################ setMethod("sample", "timeSeries", function(x, size, replace = FALSE, prob = NULL) { x[sample(seq(NROW(x)), size, replace, prob), ] } ) ################################################################################ timeSeries/R/statistics-colSums.R0000644000176000001440000001151112620124746016557 0ustar ripleyusers # 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 ################################################################################ # FUNCTION: COLUMN STATISTICS: # colStats Computes sample statistics by column # colSums Computes sums of all values in each column # colMeans Computes means of all values in each column # colSds Computes standardard deviation of each column # colVars Computes sample variance by column # colSkewness Computes sample skewness by column # colKurtosis Computes sample kurtosis by column # colMaxs Computes maximum values in each colum # colMins Computes minimum values in each colum # colProds Computes product of all values in each colum # colQuantiles Computes quantiles of all values in each colum # DEPRECATED: NO LONGER USED: # colAvgs Computes sample mean by column # colStdevs Computes sample standard deviation by column # mean.timeSeries Computes sample means by column # var.timeSeries Computes sample variance by column ################################################################################ colStats <- function(x, FUN, ...) { # A function implemented by Diethelm Wuertz # Description: # Computes sample statistics by column # FUNCTION: # Statistics: if (inherits(x, "timeSeries")) apply(na.omit(getDataPart(x), ...), 2, FUN, ...) #<< YC : as.matrix is slow ! else apply(na.omit(as.matrix(x), ...), 2, FUN, ...) } # ------------------------------------------------------------------------------ # YC important because default colSums is unefficient since it retrieves # full dimnames, i.e. rownames which is very time consuming if (getRversion() < "2.9.0") { setMethod("colSums", "timeSeries", function(x, na.rm = FALSE, dims = 1L) { x <- getDataPart(x) callGeneric() }) } else { setMethod("colSums", "timeSeries", function(x, na.rm = FALSE, dims = 1L, ...) { x <- getDataPart(x) callGeneric() }) } # ------------------------------------------------------------------------------ # YC important because default colSums is unefficient since it retrieves # full dimnames, i.e. rownames which is very time consuming if (getRversion() < "2.9.0") { setMethod("colMeans", "timeSeries", function(x, na.rm = FALSE, dims = 1L) { x <- getDataPart(x) callGeneric() }) } else { setMethod("colMeans", "timeSeries", function(x, na.rm = FALSE, dims = 1L, ...) { x <- getDataPart(x) callGeneric() }) } # ------------------------------------------------------------------------------ colSds <- function(x, ...) { colStats(x, "sd", ...) } colVars <- function(x, ...) { colStats(x, "var", ...) } colSkewness <- function(x, ...) { colStats(x, "skewness", ...) } colKurtosis <- function(x, ...) { colStats(x, "kurtosis", ...) } colMaxs <- function(x, ...) { colStats(x, "max", ...) } colMins <- function(x, ...) { colStats(x, "min", ...) } colProds <- function(x, ...) { colStats(x, "prod", ...) } # ------------------------------------------------------------------------------ colQuantiles <- function(x, prob = 0.05, ...) { # FUNCTION: stopifnot(length(prob) == 1) colStats(x, "quantile", probs = prob, ...) } ################################################################################ # DEPRECATED: colAvgs <- function(x, ...) { # FUNCTION: colMeans(x, ...) } # ------------------------------------------------------------------------------ colStdevs <- function(x, ...) { # FUNCTION: colStats(x, "sd", ...) } # ------------------------------------------------------------------------------ # mean.timeSeries <- colMeans # var.timeSeries <- colVars ################################################################################ timeSeries/R/base-sort.R0000644000176000001440000000352312632505222014640 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # sort,timeSeries Sorts a 'timeSeries' object in time ################################################################################ .sort.timeSeries <- function (x, decreasing = FALSE, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Time sorts a 'timeSeries' object # Arguments: # x - a 'timeSeries' object. # Value: # Returns a time sorted object of class 'timeSeries'. # FUNCTION: # check if really necessary to sort x # important in order to improve efficiency ## NB: is.unsorted can return NA if (!decreasing && !isTRUE(is.unsorted(x@positions))) return(x) if (length(x@positions)>0) x[order(x@positions, decreasing = decreasing), ] else x } setMethod("sort", "timeSeries", function (x, decreasing = FALSE, ...) .sort.timeSeries(x, decreasing = decreasing, ...)) # until UseMethod dispatches S4 methods in 'base' functions sort.timeSeries <- function(x, decreasing = FALSE, ...) .sort.timeSeries(x, decreasing = decreasing, ...) ################################################################################ timeSeries/R/methods-is.R0000644000176000001440000000435212633117102015013 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # is.timeSeries Tests for a 'timeSeries' object ################################################################################ is.timeSeries <- function (x) { # A function implemented by Diethelm Wuertz # Description: # Tests for a 'timeSeries' object. # Arguments: # object - a 'timeSeries' object to be tested. # Value: # Returns 'TRUE' or 'FALSE' depending on whether its # argument is of 'timeSeries' type or not. # Changes: # # FUNCTION: # Check: ans <- is(x, "timeSeries") # Return Value: ans } # ------------------------------------------------------------------------------ is.signalSeries <- function(x) { !as.logical(length(x@positions)) } # ------------------------------------------------------------------------------ # YC: # Note if is.na returns a timeSeries objects then we have problem # with the function quantile... setMethod("is.na", "timeSeries", function(x) setDataPart(x, is.na(getDataPart(x)))) # ------------------------------------------------------------------------------ # something like this would be needed if is.unsorted again became an internal generic #if(getRversion() >= "3.3.0") { # setGeneric("is.unsorted", signature = "x", useAsDefault = base::is.unsorted) #} setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE, strictly = FALSE) callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) ################################################################################ timeSeries/R/utils-head.R0000644000176000001440000000647112620124746015012 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # METHOD: SUBSETTING METHODS ON DATA: # head,timeSeries Returns the head of a 'timeSeries' object # tail,timeSeries Returns the tail of a 'timeSeries' object ################################################################################ .head.timeSeries <- function(x, n = 6, recordIDs = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns the head of a 'timeSeries' object # Arguments: # x - a 'timeSeries' object. # n - a single integer. If positive, number of the first n records (rows) # to be returned. If negative, all but the n first number of # elements of x are returned. # recordIDs - a logical flag, should the record identification # be shown? By default FALSE. # ... - # Value: # Returns the tail of an object of class 'timeSeries'. # FUNCTION: # Head: if (recordIDs & dim(x)[1] == dim(x@recordIDs)[1]) cbind(head.matrix(x, n = n, ...), head(x@recordIDs, n = n, ...)) else head.matrix(x, n = n, ...) } setMethod("head", "timeSeries", function(x, n = 6, recordIDs = FALSE, ...) .head.timeSeries(x, n, recordIDs, ...)) # until UseMethod dispatches S4 methods in 'base' functions head.timeSeries <- function(x, ...) .head.timeSeries(x, ...) # ------------------------------------------------------------------------------ .tail.timeSeries <- function(x, n = 6, recordIDs = FALSE, ...) { # A function implemented by Diethelm Wuertz # Description: # Returns the tail of a 'timeSeries' object # Arguments: # x - a 'timeSeries' object. # n - a single integer. If positive, number of the last n records (rows) # to be returned. If negative, all but the n last number of # elements of x are returned. # recordIDs - a logical flag, should the record identification # be shown? By default FALSE. # ... - # Value: # Returns the tail of an object of class 'timeSeries'. # FUNCTION: # Tail: if (recordIDs & dim(x)[1] == dim(x@recordIDs)[1]) cbind(tail.matrix(x, n = n, addrownums = FALSE, ...), tail(x@recordIDs, n = n, addrownums = FALSE, ...)) else tail.matrix(x, n = n, addrownums = FALSE, ...) } setMethod("tail", "timeSeries", function(x, n = 6, recordIDs = FALSE, ...) .tail.timeSeries(x, n, recordIDs, ...)) # until UseMethod dispatches S4 methods in 'base' functions tail.timeSeries <- function(x, ...) .tail.timeSeries(x, ...) ################################################################################ timeSeries/R/statistics-smoothLowess.R0000644000176000001440000001507312620124746017647 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # smoothSupsmu Smoothes a timeSeries with the supsmu function # smoothLowess Smoothes a timeSeries with the lowess function # smoothSpline Smoothes a timeSeries with the smooth.spline function # DEPRECATED: DESCRIPTION: # .supsmuSmoother Smoothes a timeSeries with the supsmu function # .lowessSmoother Smoothes a timeSeries with the lowess function # .splineSmoother Smoothes a timeSeries with the smooth.spline function ################################################################################ # DW: # These are older functions which have to be rewritten ... # The functions are thought to be used to smooth financial # price or index series. # ------------------------------------------------------------------------------ smoothSupsmu <- function(x, bass = 5, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the supsmu function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # bass - controls the smoothness of the fitted curve. Values of up # to 10 indicate increasing smoothness. # ... - further arguments passed to the function supsmu() # Example: # x <- smoothSupsmu(MSFT[, 4], bass = 0.1); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::supsmu(x = 1:length(x), y = x, bass = bass, ... ) data <- cbind(x, ans$y) colnames(data) <- c(colnames(X), "supsmu") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ------------------------------------------------------------------------------ smoothLowess <- function(x, f = 0.5, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the lowess function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # f - the smoother span. This gives the proportion of points in the # plot which influence the smooth at each value. Larger values # give more smoothness. # ... - further arguments passed to the function lowess() # Example: # x = smoothLowess(MSFT[, 4], f = 0.05); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::lowess(x, f = f, ...)$y data <- cbind(x, ans) colnames(data) <- c(colnames(X), "lowess") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } # ------------------------------------------------------------------------------ smoothSpline <- function(x, spar = NULL, ...) { # A function implemented by Diethelm Wuertz # Description: # Smoothes a time series with the smooth.spline function # Arguments: # x - an univariate timeSeries object, e.g. a price or index series # f - the smoother span. This gives the proportion of points in the # plot which influence the smooth at each value. Larger values # give more smoothness. # ... - further arguments passed to the function smooth.spline() # Details: # smooth.spline(x, y = NULL, w = NULL, df, spar = NULL, cv = FALSE, # all.knots = FALSE, nknots = NULL, keep.data = TRUE, df.offset = 0, # penalty = 1, control.spar = list()) # Example: # x = smoothSpline(MSFT[, 4], spar = NULL); x; plot(x) # x = smoothSpline(MSFT[, 4], spar = 0.4); x; plot(x) # FUNCTION: # Settings: stopifnot(isUnivariate(x)) # Extract Title and Documentation: Title <- x@title Documentation <- x@documentation # Handle Missing Values: x <- na.omit(x) # Convert to Vector: X <- x x <- as.vector(x) # Smooth: ans <- stats::smooth.spline(x, spar = spar, ...)$y data <- cbind(x, ans) colnames(data) <- c(colnames(X), "spline") rownames(data) <- as.character(time(X)) series(X) <- data # Preserve Title and Documentation: X@title <- Title X@documentation <- Documentation # Return Value: X } ################################################################################ .supsmuSmoother <- function(...) { # FUNCTION: # Deprecated: .Deprecated("smoothSupsmu") # Return Value: smoothSupsmu(...) } # ------------------------------------------------------------------------------ .lowessSmoother <- function(...) { # FUNCTION: # Deprecated: .Deprecated("smoothLowess") # Return Value: smoothLowess(...) } # ------------------------------------------------------------------------------ .splineSmoother <- function(...) { # FUNCTION: # Deprecated: .Deprecated("smoothSpline") # Return Value: smoothSpline(...) } ################################################################################ timeSeries/R/base-attach.R0000644000176000001440000000265612620124746015130 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # S4 METHOD: DATABASE ATTACHEMENT: # attach,timeSeries Attaches a 'timeSeries' object to the search path ################################################################################ setMethod("attach", "timeSeries", function(what, pos = 2, name = deparse(substitute(what)), warn.conflicts = TRUE) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Attaches a 'timeSeries' object # Details: # The function works in the same way as in the case of a # data.frame, i.e. the return values are vectors. # FUNCTION: # Return Value: callGeneric(as.data.frame(what), pos, name, warn.conflicts) }) ################################################################################ timeSeries/R/timeSeries-slotUnits.R0000644000176000001440000000344612620124746017065 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # getUnits Get units slot from a 'timeSeries' # setUnits<- Set new units slot to a 'timeSeries' ################################################################################ getUnits <- function(x) { # A function implemented by Diethelm Wuertz # FUNCTION: # Return Value: UseMethod("getUnits") } getUnits.default <- function(x) { # Description: # Get units slot from a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Return Value: colnames(x) } # ------------------------------------------------------------------------------ "setUnits<-" <- function(x, value) { # Description: # Set units slot to a 'timeSeries' object. # Arguments: # x - a 'timeSeries' object # FUNCTION: # Assign Time Slot: colnames(x) <- value # Return Value: x } ################################################################################ timeSeries/R/stats-na.omit.R0000644000176000001440000002337612620124746015457 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: DESCRIPTION: # na.omit,timeSeries Handles missing values in objects # .naOmitMatrix Internal function called from na.omit,timeSeries # OLD FUNCTIONS: DESCRIPTION: # removeNA Remove NAs from a matrix object # substituteNA Substitute NAs by zero, the column mean or median # interpNA Interpolate NAs using R's "approx" function ################################################################################ # DW: # I think we should deprecate the following functions: # removeNA, substituteNA, and interpNa since the function # na.omit() can already handle all these cases. # DW: # note we do interpolation with approx(), zoo also offers # interpolation by splines, we should also add this. ################################################################################ .na.omit.timeSeries <- function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), ...) { # Description # Handles NAs in timeSeries objects # Detials: # Linear Interpolation is done by the function approx. # Spline interpolation like in zoo is not yet supported. # Arguments: # object - an object of class 'timeSeries' # method - how to handle NAs # interp - how to interpolate NAs # ... - arguments passed to function approx() # FUNCTION: # Check Arguments: stopifnot(is.timeSeries(object)) # Extract Title and Documentation: Title <- object@title Documentation <- object@documentation # Settings: method <- match.arg(method) interp <- match.arg(interp) # Skip ? if (method == "s") return(object) # Handle NAs: if (method == "r") { # Remove NAs: # DW: # object <- stats:::na.omit.default(object) object <- as.timeSeries(na.omit(series(object))) } else if (method == "z") { # Substitute NAs by Zero's: object[is.na(object)] <- 0 } else if (substr(method, 1, 1) == "i") { # Interpolate: interp = match.arg(interp) f = 0 if (interp == "before") { interp = "constant" f = 0 } if (interp == "after") { interp = "constant" f = 1 } n = nrow(object) for (i in 1:ncol(object)) { y = object[, i] idy = (1:n)[!is.na(y)] ## DW: ... added # Linear/Constant Interpolation: y = approx(x = idy, y = y[idy], xout = 1:n, method = interp, f = f, ...)$y object[, i] = y } modID = FALSE if (method == "ir") { # Remove Start and End NAs: # DW: # object <- stats:::na.omit.default(object) object <- as.timeSeries(na.omit(series(object))) } else if (method == "iz") { # Set Start and End NAs to Zero: object[is.na(object)] = 0 } else if (method == "ie") { n = nrow(object) for (k in 1:ncol(object)) { y = object[, k] if (NA %in% y) { start = sum(cumprod(is.na(y))) if (start > 0) for (i in start:1) y[i] = y[i+1] end = n+1 - sum(cumprod(rev(is.na(y)))) if (end <= n) for (i in end:n) y[i] = y[i-1] object[, k] = y } } } } # Handle recordIDs ... recordIDs <- object@recordIDs modID <- c(r = TRUE, z = FALSE, ir = TRUE, iz = FALSE, ie = FALSE) if(modID[method] > 0 && sum(dim(recordIDs)) > 0 ) { index <- attr(object, "n.action") recordIDs <- recordIDs[index, ] } # Preserve Title and Documentation: object@title <- Title object@documentation <- Documentation # Return Value: object } setMethod("na.omit", "timeSeries", function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), ...) .na.omit.timeSeries(object, method, interp, ...)) # until UseMethod dispatches S4 methods in 'base' functions na.omit.timeSeries <- function(object, ...) .na.omit.timeSeries(object, ...) # ------------------------------------------------------------------------------ .naOmitMatrix <- function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after")) { # Description: # Internal Function called from na.omit.timSeries() # Arguments: # FUNCTION: # Extract matrix: x = object stopifnot (is.matrix(x)) # Match Arguments: method <- match.arg(method) interp <- match.arg(interp) # Handle NAs: if (method == "r") { # Remove NAs: x <- na.omit(x) } else if (method == "z") { # Substitute NAs by Zero's: x[is.na(x)] <- 0 } else if (substr(method, 1, 1) == "i") { # Interpolate: interp = match.arg(interp) f = 0 if (interp == "before") { interp = "constant" f = 0 } if (interp == "after") { interp = "constant" f = 1 } n = nrow(x) for (i in 1:ncol(x)) { y = x[, i] idy = (1:n)[!is.na(y)] y = approx(idy, y[idy], 1:n, method = interp, f = f)$y x[, i] = y } modID = FALSE if (method == "ir") { # Remove Start and End NAs: x = na.omit(x) } else if (method == "iz") { # Set Start and End NAs to Zero: x[is.na(x)] = 0 } else if (method == "ie") { n = nrow(x) for (k in 1:ncol(x)) { y = x[, k] if (NA %in% y) { start = sum(cumprod(is.na(y))) if (start > 0) for (i in start:1) y[i] = y[i+1] end = n+1 - sum(cumprod(rev(is.na(y)))) if (end <= n) for (i in end:n) y[i] = y[i-1] x[, k] = y } } } } # Add Control: if (substr(method, 1, 1) == "i") { attr(x, "control") = c(method = method, interp = interp) } else { attr(x, "control") = c(method = method) } # Return Value: x } ################################################################################ removeNA <- function (x, ...) { # A function implemented by Diethelm Wuertz and Yohan Chalabi # Description: # Remove NA's from objects which can be transformed to a matrix # Arguments: # x - an object which can be transformed to a matrix # FUNCTION: na.omit(x, ...) } # ------------------------------------------------------------------------------ substituteNA <- function(x, type = c("zeros", "mean", "median"), ...) { # A function implemented by Diethelm Wuertz # Description: # Imputes missing data by zeros, the median or the # mean values of all matrix elements # Arguments: # x - an object which can be transformed to a matrix # type - method specifies the substitution method to be # used. Choices are "zeros", "mean", or "constant" # FUNCTION: if (!inherits(x, "matrix") && !inherits(x, "timeSeries")) x <- as(x, "matrix") # Type: type <- match.arg(type) ans <- switch(type, "zeros" = apply(x, 2, function(z) { z[is.na(z)] <- 0 z}), "median" = apply(x, 2, function(z) { z[is.na(z)] = median(z, na.rm = TRUE) z}), "mean" = apply(x, 2, function(z) { z[is.na(z)] = mean(z, na.rm = TRUE) z})) # Return Value: ans } # ------------------------------------------------------------------------------ interpNA <- function(x, method = c("linear", "before", "after"), ...) { # A function implemented by Diethelm Wuertz # Description: # Interpolates missing values in a matrix object # Arguments: # x - a numeric vector or time series object of class 'ts'. # method - the method how to interpolate the vector, one of # the applied vector strings: "linear", "before" or # after. # Details: # To interpolate the function 'approx' is used. # Value: # Returns a vector or time series object where the missing # values are interpolated. # FUNCTION: if (!inherits(x, "matrix") && !inherits(x, "timeSeries")) x <- as(x, "matrix") # Internal Function: interpVectorNA <- function(x, method, f) { n <- length(x) idx <- (1:n)[!is.na(x)] x <- approx(idx, x[idx], 1:n, method = method, f = f)$y x} # Select Method: method = method[1]; f = 0 if (method == "before") { method = "constant" f = 0 } if (method == "after") { method = "constant" f = 1 } # For each Column: for (i in 1:ncol(x)) { x[, i] = interpVectorNA(x[, i], method, f) } # Return Value: x } ################################################################################ timeSeries/R/zzz.R0000644000176000001440000000201612620124746013577 0ustar ripleyusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program 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 General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ .onLoad <- function(libname, pkgname) { if(!is.numeric(getRmetricsOptions("max.print"))) setRmetricsOptions(max.print = 100) #-> show() of large matrices # YC: This should really go in methods package. keep it here for # the time being. 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\usepackage{hyperref} \hypersetup{colorlinks,% citecolor=black,% linkcolor=blue,% urlcolor=darkgreen,% } \title{\bf Plotting 'timeSeries' Objects} \author{Diethelm W\"urtz and Tobias Setz\\ETH Zurich and Rmetrics Association Zurich} \date{May 12, 2014} \begin{document} \SweaveOpts{concordance=TRUE} \maketitle \tableofcontents \setlength{\parskip}{20pt} %\SweaveOpts{strip.white=FALSE} \setkeys{Gin}{width=0.9\textwidth} % plot.ts <- function ( % x, y = NULL, plot.type = c("multiple", "single"), % xy.labels, xy.lines, panel = lines, nc, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(6, 0, 5, 0), axes = TRUE, ...) % plot.zoo <- function ( % x, y = NULL, screens, plot.type, panel = lines, % xlab = "Index", ylab = NULL, main = NULL, % xlim = NULL, ylim = NULL, % xy.labels = FALSE, xy.lines = NULL, % yax.flip = FALSE, % oma = c(6, 0, 5, 0), % mar = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % col = 1, lty = 1, lwd = 1, pch = 1, type = "l", log = "", % nc, widths = 1, heights = 1, ...) % plot.xts <- function ( % x, y = NULL, type = "l", auto.grid = TRUE, % major.ticks = "auto", minor.ticks = TRUE, major.format = TRUE, % bar.col = "grey", candle.col = "white", % ann = TRUE, axes = TRUE, ...) % .plot.timeSeries <-function( % x, y, FinCenter = NULL, type = NULL, plot.type = c("multiple", "single"), % format = "auto", at = c("chic", "pretty"), % col, pch, cex, lty, lwd, % grid = FALSE, frame.plot = TRUE, panel = lines, % axes = TRUE, ann = TRUE, cex.axis = 1, cex.lab = 1, % yax.flip = FALSE, % mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), % oma.multi = c(7.75, 1.1, 6.1, 1.1), % ...) % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 1 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Introduction} The Rmetrics \texttt{timeDate} and \texttt{timeSeries} packages are workhorses to deal with chronological objects. Since their inception 2009 under their original names \texttt{fCalendar} and \texttt{fSeries} they have been only slightly modified. With version R 3.1. we have essentially improved the \texttt{plot} function, but we also took care that the functionality is almost upward compatible. In this vignette we show how to work with the recently updated S4 generic plot function \texttt{plot}. The function is written to display Rmetrics S4 \texttt{timeSeries} objects. The basic functionality of the \texttt{plot} function is to display single and multiple views on univariae and multivariate \texttt{timeSeries} objects. The function \texttt{plot.ts} from R's base environment, which displays basic \texttt{ts} time series objects, served as a model for our design of the generic S4 \texttt{plot} function for \texttt{timeSeries} objects. Similarily, \texttt{plot.ts} can be considered as the prototype for the S3 \texttt{plot.zoo} method. The \texttt{xts} plot function was build to display univariate \texttt{xts} time series objects which inherit from \texttt{zoo}'s objects for ordered time series objects. The generic S4 time series plotting function can dispay \emph{univariate} and \emph{multivariate} time series in \emph{single} and \emph{multiple} frames. The plots can be tailored with respect to several viewing components: colors (col), line types (lty), plot symbols (pch), line widths (lwd), symbol sizes (cex), axis layout (pretty, chic, tailored), minor tick mark appearence, font styles and font sizes, frame positioning (mar, oma), as well as tailored panel functions (panel). \noindent\emph{General Plot Settings and Design Apects}: \noindent\emph{Plot Type}: Univariate time series are displayed by default in \texttt{plot.type="single"} frames, multivariate time series are displayed by default in \texttt{plot.type="multiple"} frames. The default line style for a plot is \texttt{type ="l"} is drawn with "lines". \noindent\emph{Time Axis Layout}: For the time axis layout the function \texttt{pretty} determines in an automative way the \texttt{at="pretty"} positions of the ticks. The \texttt{format="auto"} is extracted from the time stamps of the time series object or can be overwritten by the user with a POSIX format string. Alternatively, one can select \texttt{"chic"} to generate time axis styles. We called this method "chic" to give reference to the underlying function \texttt{axTicksByTime} from the Chicago \texttt{xts} package which generates tick positions and axis labels. Furthermore, a "tailored" method can be applied which allows for fully arbitrary user defined positions and formatted labels. Minor ticks can be added in several fashions. \noindent\emph{Annotations}: The annotations of the plots are reduced to the y-label. These are taken by default from the column names of the time series object. This gives the user the freedom to have full control about his views how the plot should be look like. Note, multivariate time series in single plots show the string \texttt{"Values"} as label on the y-axis. Main title, sub title, and the x-label on the time axis are not shown by default. We prefer and recommend to add these decorations calling the function \texttt{title}. This allows also much more flexibility compared to passing the arguments through the plot functions. All default annotations (including the y-label) can be suppressed setting the plot function argument to \texttt{ann=FALSE}. The argument \texttt{axes=FALSE} suppresses to draw both axes on the plot frame. \noindent\emph{Decorations}: There are several options to decorate the plot: These include colors (col), plotting symbols (pch), scaling factor of plotting characters and symbols (cex), line types (lty), and lindwidths (lwd). Note, all these parameters may be vectors of the same length as the number of time series, so that each series can be addressed to its own individual style, color, and size. A grid and the plot frame (box) can be added or suppresse specifying the arguments \texttt{grid} and \texttt{frame.plot} in the argument list of the \texttt{plot} function. \noindent\emph{Panel Function}: In the case of multiple plots the plot frames, are also called \emph{panels}. By default in each panel the appropriate curve is drawn calling R's \texttt{lines} function \texttt{panel=lines}. This function can be replaced by a user defined function. This offers a wide range of new views on your time series. So for example yo can show zero or any other reference lines on the panels, or you can add rugs to (return) charts, or you can add for an example an EMA indicator (or any other kind of indicator) to curves shown in individual panels. \noindent\emph{Example "timeSeries" Objects}: To demonstrate the wide range of options to dispaly S4 \texttt{timeSeries} objects, we use the the daily index values from the Swiss Pension Fund Benchmark \emph{LPP2005}. The time series is part of the \texttt{timeSeries} package. For this we have introduced some abbreviations: <>= Sys.setlocale("LC_ALL", "C") @ <>= require(timeSeries) require(xts) require(PerformanceAnalytics) require(fTrading) tS1 <- 100 * cumulated(LPP2005REC[, 1]) # SBI (univariate) tS2 <- 100 * cumulated(LPP2005REC[, 1:2]) # SBI & SPI (bivariate) tS3 <- 100 * cumulated(LPP2005REC[, 1:3]) # SBI, SPI, SWIIT (Swiss Market) tS6 <- 100 * cumulated(LPP2005REC[, 1:6]) # Swiss and Foreign Market Indexes @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 2 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Standard Time Series Plots} The \texttt{plot} function from the \texttt{timeSeries} package allows for five different views on standard plot layouts. These include \begin{itemize} \item Univeriate single plots \item Multivariate single plots \item One column multiple plots \item Two column multiple plots \item Scatter plots \end{itemize} \noindent The only argument we have to set is the \texttt{plot.type} parameter to determine the layout of the plot. The default value is \texttt{"multiple"}, and the alternative value is \texttt{"single"}. The arguments can be abbreviated as \texttt{"m"} or \texttt{"s"}, respectively. \noindent \emph{Univariate Single Plots} were designed to plot univariate \texttt{timeSeries} objects in one single graph frame. Nothing then the \texttt{timeSeries} object has to be specified, the \texttt{plot.type} is forced to \texttt{"s"}. \noindent \emph{Multivariate Single Plots} will be used when a set of multivariate \texttt{timeSeries} objects should be drawn in one common data frame. For this argument the vlue \texttt{plot.type="s"} has to be specified. \noindent \emph{One Column Multiple Plots} display multivariate \texttt{timeSeries} objects, such that each series is plotted in its own frame. Up to four series, the frames are displayed in one column, for more series the frames are arranged in a two colum column display. \noindent \emph{Two Column Multiple Plots} handel the case of more than four \texttt{timeSeries} objects. Then the the series are displayed in two colums. In total, the number of rows is not restricted. % ---------------------------------------------------------------------------- \pagebreak \subsection{Univariate Single Plots} The most simple time series plot shows an univariate curve in a single plot. The axis is designed from "pretty" positions calculated from R's base function \texttt{pretty}. The labels are printed in the ISO 8601 standard date/time format. <>= par(mfrow=c(1, 1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The chart shows an univariate time series (here the Swiss Bond Index) in a single frame. For all plot options default values have been chosen. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color (col), add a main title and x-label calling the function \texttt{title}, or remove the grid lines setting the argument \texttt{grid=FALSE}. You can also design the minor tick marks, setting instead of the value \texttt{"auto"} oe of the following spreads: \texttt{"day"}, the default, \texttt{"week"}, or \texttt{"month"}. } \end{figure} \end{center} % ---------------------------------------------------------------------------- \pagebreak \subsection*{} Tow other plot function implementations for \texttt{xts} time series objects can be found in the contributesd R packages \texttt{xts} and \texttt{PerformanceAnalytics}. Let us compare how they generate plot positions and time label formats. \vspace{-0.3cm} <>= require(PerformanceAnalytics) par(mfrow=c(3, 1)) xts::plot.xts(as.xts(tS1)) PerformanceAnalytics::chart.TimeSeries(as.xts(tS1)) plot(tS1) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The group of the three charts shows an univariate time series in a single frame for the plot functions as implemented in the packages \texttt{xts}, \texttt{PerformanceAnalytics}, and \texttt{timeSeries}. For example in the case of daily time series records \texttt{xts} uses U.S. style labels whereas \texttt{PerformanceAnalytics} and \texttt{timeSeries} use ISO standard date labels \texttt{YYYY-mm-dd}. The plot decorations are those from default settings.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multivariate Single Plots} Multivariate time series plots in a single panel are constructed by default in the way that the first curve is plotted calling the function \texttt{plot} and the remaining curves by calling the function \texttt{lines}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This chart shows a multivariate time series in a single frame. Note, we have to set the argument \texttt{plot.type="s"}. Again, for all plot options the default settings have been used. You can decorate the plot, making it more conveniant to your needs, e.g.: change the color vector (col), add a main title and x-label calling the function \texttt{title}, or remove grid lines setting the argument \texttt{grid=FALSE}. Note, to change the color settings you can set the argument \texttt{col=1:3} which would result in "black", "red", "green" for the three curves, or you can just set the colors by name, or selecting them from a color palette.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us compare the plot function from the \texttt{timeSeries} package with the \texttt{chart.TimeSeries} plotting function from the \texttt{PerformanceAnalytics} function. (Note, the \texttt(xts) has no multivariate plot function implemented.) <>= par(mfrow=c(2, 1)) require(PerformanceAnalytics) PerformanceAnalytics::chart.TimeSeries(as.xts(tS3)) plot(tS3, plot.type="s") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The two charts show a multivariate time series plotted in a single frame. We use for the plot the functions as implemented in the packages \texttt{PerformanceAnalytics}, and \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Multiple Plots} Multiple plots enormously simplify the display of different curves in multiple panels. These are the ideal plots when it comes to the task to create a quick overview over several time series. Multiple plotting is exclusive to \texttt{timeSeries} objects, \texttt(xts) and \texttt{PerformanceAnalytics} offer no multiple plotting tool. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{As long as we plot less than 4 time series in a multivariate frame, we get a one column layout. Annotations show by default only the y-labels which are taken from the colmun names of the time series to be drawn. Feel free to add main title, sub title, and x-label calling the function \texttt{title}}. \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} For more than four curves the frames of the plot design are arranged in two columns. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the layout how it is created for six curves. There are two columns with three panels to the left and also three panels to the right. Note, it is easily possible to adapt the margin sizes and the gap between the two columns of plots calling the function \texttt{mar} and \texttt{oma}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} If you like a design with a small gap between the panel rows, you can modify the \texttt{mar} parameter to introduce a small gap, here with a width of 0.3. Feel free to modify it. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", mar=c(gap=0.3, 5.1, gap=0.3, 2.1)) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in a multiple plot the \texttt{mar} parameter setting to create a small gap between the rows of the individual charts. This lets a plot look more elegant.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Combining Single Plots} You can also create your own multiple panel plots. Just combine single panels in an array of rows and columns using the parameter settings for \texttt{mfrow}, \texttt{mfcol}, and \texttt{mar}. <>= par(mfrow=c(2, 1)) par(mar = c(bottom=1.5, 5.1, top=4, 2.1)) plot(tS2[, 1]) par(mar = c(bottom=4, 5.1, top=1.5, 2.1)) plot(tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{One can use in single plots the \texttt{mfrow} and \texttt{mar} parameter settings to place single plots either row by row or column by column. Here, \texttt{mfrow} and \texttt{mfcol} to the job. In this case a vector of the form \texttt{c(nr, nc)} draws subsequent figures in an nr-by-nc array on the device by columns (mfcol) or rows (mfrow), respectively.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Layout of Single Plots} There is another option in R to create panel layouts, not necessarilly in an rectangular array. Have a look to the help page of the function \texttt{layout}, her comes a simple example. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow = TRUE, nrow=2)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=2, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=2, 2.1)) plot(tS3[, 3]) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} you can devide the plot device in rows and columns expressed in matrix form defined by the argument \texttt{mat}. } \end{figure} \end{center} %To be more specific, the graph \texttt{i} is allocated a region composed %from a subset of these rows and columns, based on the rows and columns %in which \texttt{i} occurs in the matrix \texttt{mat}. %The argument \texttt{layout.show(n)} plots (part of) the current layout, %namely the outlines of the next \texttt{n} figures. % ----------------------------------------------------------------------------- \pagebreak \subsection*{} In addition widths and heights of the layout can be different from row to row, and/or from column to column. The sizes are expressed by the arguments \texttt{widths} and \texttt{heights} of the function \texttt{layout}. <>= nf <- layout(mat=matrix(c(1, 1, 2, 3), byrow=TRUE, nrow=2), heights=c(2.5,1)) par(mar = c(bottom=2, 5.1, top=3, 2.1)) plot(tS3[, 1]) par(mar = c(bottom=3, 5.1, top=1.5, 1.1)) plot(tS3[, 2]) par(mar = c(bottom=3, 4.1, top=1.5, 2.1)) plot(tS3[, 3]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{With the function \texttt{layout} one can also define the widths and heights of the columns and rows.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Bivariate Scatter Plots} For historical reasons, like in the function \texttt{plot.ts}, there is also the option to create an scatter plot from two univariaye time series. Since this is not a "true" time series plot, we will not go in further detail for this display. <>= par(mfrow=c(1,1)) plot(tS2[, 1], tS2[, 2]) @ \begin{center} \begin{figure}[h] <>= <> @ \caption{If \texttt(x) and \texttt(y) are univariate time series, then the plot function will display a scatter plot.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 3 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Time Axis Layout} The function \texttt{plot} comes with three options to design the time axis layout: \texttt{"pretty"}, \texttt{"chic"}, and \emph{} (note this not a string argument. \emph{} should just abbreviate that we have to input character strings of fully arbitray \texttt{at} positions. For the first two options the style of the axis annotation is generated in a fully automated way, whereas in the tailored case each tick on the axis to be user defined. \noindent The \emph{"pretty"} time axis layout is the default setting for the argument \texttt{at}. Internally the function \texttt{pretty} is used to compute a sequence of about \texttt{n+1} equally spaced round values which cover the range of the values in the time stamps \texttt{time(x)} of the series \texttt{x}. The values are chosen so that they are 1, 2 or 5 times a power of 10. \noindent The \emph{"chic"} time axis layout is the alternative setting for the argument \texttt{at}. Internally the function \texttt{axTicksByTime} from the package \texttt{xts} is used to compute the sequence of axis positions and the format labels. \noindent The \emph{} time axis layout leaves it to the user to specify by himself the positions (at), the time label formatting (format), and the minor tick marks (minor.ticks). % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis: "pretty" or "chic"?} Our plotting function comes with two axis-styles. The first is called \texttt{"pretty"}, which is the default style, and calculates positions from R's base function \texttt{pretty}. The other is called \texttt{"chic"} to remember its origin, arising from the "Chicago" \texttt{xts} package. \vspace{-0.7cm} <>= par(mfcol = c(2, 1)) plot(tS1, at = "pretty") plot(tS1, at = "chic") @ \vspace{-0.3cm} \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows the two flavours from the \texttt{at="pretty"} and the \texttt{"chic"} axis designs. The first style uses the function \texttt{pretty} from R's \texttt{base} environment to compute the positions for the major ticks. The second style uses the function \texttt{axTicksByTime} from the \texttt{xts} package to compute x-axis tick mark locations by time. In the upper graph the minor ticks are calendar days, whereas in the lower graph weekdays are drawn (therefore the small gaps between the minor ticks become visible). Note, the time series is in both cases an object of class \texttt{timeSeries}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection*{} Now let us plot a multivariate 3-column time series in a single panel. Again we compare the outcome of the \texttt{"pretty"} and the \texttt{"chic"} axis style. <>= par(mfcol=c(2, 1)) plot(tS3, plot.type="s", at="pretty") plot(tS3, plot.type="s", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The only difference of this graph compared to the previous is the fact that we consider here a multivariate time series. Three curves are shown in a common plot. Note, when using the \texttt{"chic"} style, then the vertical gridlines are narrower compared to the \texttt{"pretty"} style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Selecting Minor Tick Marks} The \texttt{"pretty"} style allows to draw the minor tick marks on different time scales. These are: \texttt{"day"}, \texttt{"week"}, and \texttt{"month"}. <>= par(mfrow=c(3, 1)) plot(tS1, minor.ticks="day", at="pretty") plot(tS1, minor.ticks="week", at="pretty") plot(tS1, minor.ticks="month", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{It is worth to note that a good selection of minor tick marks makes a plot much better readable.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - One Column Multiple Plot Layout} In the multiple plot layout the axis are drawn along the same principles as they are drawn in the case of the single plot layout. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a one column multiple plot layout. The one column layout is generated for up to four time series. When the multivariate time series has more then four time series then a two column layout is displayed. It is up to you which axis style you prefer, \texttt{at="pretty"} or \texttt{at="chic".}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Time Axis - Two Column Multiple Plot Layout} Concerning the style of the axis, there is now difference between the one and two column plot designs. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="m", at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we have more then four time series, then the display will be generated in two columns. Note, it is possible to modify the width of the gap between the two columns.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Tick and Format Layout: The axis style} The third alternative to style the axis offers the users to define format positions according to his preferences. Here comes an example: <>= par(mfrow=c(2, 1)) at <- paste0("200", c("6-01", "6-04", "6-07", "6-10", "7-01", "7-04"), "-01") plot(tS3, plot.type="s", format="%B\n%Y", at=at) plot(tS3, plot.type="s", format="%b/%y", at=at) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows plots with user tailored positions and formatted axis labels.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 4 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Annotations} Plot annotations are elelents which can be added to plots or completely discarded. To discard all annotations you have to set \texttt{ann=FALSE} in the argument list of the timSeries \texttt{plot} function. To display annotation you can call the function \texttt{title}. This allows to add the main title, the sub title, and the x- and y-labels to a plot. Together with the appropriate character strings, you can also specify the placement of these annotations by the arguments \texttt{line} and \texttt{outer}. There are additional functions to add annotations to a plot. These are \texttt{text} and \texttt{mtext}. % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding all Annotations} In a default plot we display only the value-label(s) which are taken from the units or column names of the time time series object. Title, sub title, and time-label are not shown. To discard the appearance of all annotations on a plot you have to set the plot argument \texttt{ann=FALSE}. <>= par(mfrow=c(2, 2)) plot(tS1, ann=FALSE) plot(tS3, plot.type="s", ann=FALSE, at="pretty") plot(tS6, plot.type="s", ann=FALSE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows a plot where all annotations have been discarded. Now feel free to add your own annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding Title and Labels} To add a main title, a sub, title, and x- and y-labels you can call the function \texttt{title}. <>= par(mfrow=c(2, 2)) plot(tS1); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index") plot(tS3, plot.type="s"); title(main = "Index", xlab = "Date") plot(tS6, plot.type="s"); title(main = "Index", xlab = "Date") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph displays in a two by two array four single plots. We have added title and x-lable annotations.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Axis Font Size} Sometimes the axis font size may be considered as too small or too large. Then you can use the plot argument \texttt{cex.axis} to upsize or downsize the font. <>= par(mfrow=c(3, 1)) plot(tS3, at="chic", plot.type="s", cex.axis=0.75) plot(tS3, at="chic", plot.type="s", cex.axis=1.00) plot(tS3, at="chic", plot.type="s", cex.axis=1.25) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This is an example how to change the size of the axis labels relatively to its default value. The upper graph shows a font size decreased by 20\%, the lower graph a font size increased by 25\%. You can proceed in the same way when using the \texttt{"pretty"} axis style.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Flipping Value Axes} Flipping every second axis label in a multiple plot from left to rigth might be meaningful in the case when axis labels overwrite themselves. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", yax.flip = TRUE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows an one column multiple plot, where the axis of the middle panel is flipped from the left to the right. Note, the same procedure can also be applied two two column multiple plots.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 5 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Decorations} There exist several options to decorate plots in different ways. Plot types (lines, points, horizontal bars, etc.) can be modified, colors can be changed, lines can be modified by style and seize, points can be selected by symbol and size. \noindent In the following we will give some examples \begin{itemize} \item Modifying Types \item Changing Colors by Names \item Changing Colors by Color Palettes \item Changing Line Styles \item Modifying Line Widths \item Changing Plot Symbols \item Modifying Plot Symbol Sizes \item Discarding Grid Lines \item Drawing a Box \end{itemize} \noindent to show a few of the many types of cdecorations. Play around to achieve your perfect layout. % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Types} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", type=c("l", "p", "h"), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{If we like to consider in a multiple plot for each panel its own plot style then we can set the parameter \texttt{type}.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Names} Colors can be changed in several ways. Just by their numbers, e.g. 1 (black), 2 (red), 3 (green) etc., or by name, e.g. "black", "red", "green", etc. or by using well designed color palettes. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=c("blue", "orange", "darkgreen")) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows how to assign colors by name in the case of a multiple plot. You can do it in the same way setting \texttt{plot.type="s"} if you like to display all three curves in a common single plot.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Colors by Color Palettes} When the number of curves increases, then it can become quite difficult to find a set of nice colors. In such cases it is convenient to select the colors from color palettes. <>= par(mfrow=c(1, 1)) plot(tS6, plot.type="s", col=heat.colors(n=6, alpha = 1), at="chic", format = "%B\n%Y") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This graph shows an example of six curves diplayed in a sequence of reds. For this we called the function \texttt{seqPalette}.} \end{figure} \end{center} \pagebreak \begin{verbatim} FUNCTION: COLOUR PALETTE rainbowPalette Contiguous rainbow colour palette heatPalette Contiguous heat colour palette terrainPalette Contiguous terrain colour palette topoPalette Contiguous topo colour palette cmPalette Contiguous cm colour palette greyPalette R's gamma-corrected gray palette timPalette Tim's MATLAB-like colour palette rampPalette Colour ramp palettes seqPalette Sequential colour brewer palettes divPalette Diverging colour brewer palettes qualiPalette Qualified colour brewer palettes focusPalette Red, green and blue focus palettes monoPalette Red, green and blue mono palettes \end{verbatim} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Line Styles} In multiple plot to each curve an own line style \texttt{lty} can be assigned: 0 "blank", 1 "solid", 2 "dashed", 3 "dotted", 4 "dotdash", 5 "longdash", or 6 "twodash". <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lty=1:3, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{When we like to print plots in black and white, then its makes much sense to use different line types so that we can distinguish the curves one from each other.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Line Widths} Not only the line type, but also the line width can be modified for each curve in an individual kind. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="m", col=1, lwd=3:1, at="chic") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{The graph shows three line widths, the upper's curve width is thick, the middle's curve width is medium, and the lowest's curve width is the thinnest one.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Changing Plot Symbols} To use different plot symbols we can assign them by the parameter \texttt{pch}. Don't forget also to set \texttt{type="p"}. %<>= %par(mfrow=c(1, 1)) %tS3weekly <- align(tS3, by="1w") %plot(tS3weekly, plot.type="s", type="p", col=1:3, pch=21:23, at="chic") %@ \medskip %\begin{center} %\begin{figure}[h] %<>= %<> %@ %\caption{This plot shows how to assign different plot symbols to the curves %in a single plot.} %\end{figure} %\end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Modifying Plot Symbol Sizes} The argument \texttt{cex.pch} allows to increase or decrease plot symbol sizes with respect to the current plot symbol size. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", type="p", col=1:3, pch=21:23, cex.pch=c(0.2, 0.2, 0.2), at="pretty") @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{This plot show how to change the size of plot symbols in a single plot setting the argument \texttt{cex.pch}. Note, for each curve its own size can be set. The same approach can be used also for multiple plots.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Discarding Grid Lines} By default grid lines are displayed. To discard the grid lines from the plot set the arguments \texttt{grid=FALSE}. <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", grid=FALSE) @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default a grid is drawn on top of the plot. You can remove it by setting the argument \texttt{grid=FALSE}} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Drawing a Box} <>= par(mfrow=c(1, 1)) plot(tS3, plot.type="s", frame.plot=FALSE, grid=FALSE) box() box(bty = "7", col = "white") # boxL grid(NA, NULL, col = "darkgrey") # hgrid @ \medskip \begin{center} \begin{figure}[h] <>= <> @ \caption{By default the plots are displayed as frame plots. This means that the graphs are surrounded by a box. This box can be discarded setting the plot argument \texttt{frame.plot=FALSE}.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 6 \newpage\thispagestyle{empty}\mbox{} %\newpage\thispagestyle{empty}\mbox{} \pagebreak \section{The Panel Function} Multiple plots are very powerful plotting designs. Each panel in a graph can individually tailored by the user. By default each curve in a panel is generated by the function \texttt{lines}. You can define your own panel function(s) by setting the plot argument \texttt{panel} to a user dfined functions. In the following we will show three examples. % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding a Horizental Zero Line} In this example we show how to write a panel function which allows to add a horizontal zero line to each plot panel. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, col=col) abline(h=0, col = "brown", lwd=2)} plot(returns(tS3), plot.type="m", col = .colorwheelPalette(3), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with curves having a horizontal zero reference line.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an Rug to Multiple Return Plots} This example shows how to add in each panel rugs to the righ Y-axis. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="h", col=col) rug(Y, side=4, col="steelblue") } plot(returns(tS6), plot.type="m", col = .colorwheelPalette(6), panel=lines2, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{This multiple plot shows panels with rugs on the right Y-axis.} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Adding an EMA to Multiple Index Plots} This example shows how to add an EMA indicator to each plot panel. The \texttt{emaTA()} function is provided by the \texttt{fTrading} package. <>= par(mfrow=c(1, 1)) lines2 <- function(X, Y, type, xlab, ylab, col, pch, lty, lwd, cex) { lines(x=X, y=Y, type="l", col=col) lines(x=X, y=emaTA(Y), col="black") } plot(tS3, plot.type="m", col = .colorwheelPalette(3), panel=lines2, grid=TRUE, at="pretty") @ \begin{center} \begin{figure}[h] <>= <> @ \caption{A multiple graph with EMA indicators in each panel.} \end{figure} \end{center} % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 7 \newpage\thispagestyle{empty}\mbox{} \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Conclusions} The \texttt{plot} function in the \texttt{timeSeries} package is a very powerful tool to create plots from time series objects. This includes to display univariate and multivariate time series in single and multiple panels, to select from two styles for the time-axis or even to tailor positions and formats according to his own needs, and to modifiy annotations and decorations of plots. % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% % 8 \newpage\thispagestyle{empty}\mbox{} \pagebreak \section{Appendix} In the appendix we have summarized graphs and tables which are useful tools to create plots. We have reprinted the default color table from R, we have summarized several color palettes as available in the \texttt{fBasics} package and other contributed R packages, and two tables with font characters and plot symbols. % ----------------------------------------------------------------------------- \pagebreak \subsection{Margins: mar and oma} <>= # Plot: # - oma stands for 'Outer Margin Area' # - mar represents the 'figure Margins' # - The default size is c(5,4,4,2) + 0.1 # - The axes tick marks will go in the first lines par(mfrow=c(1, 1)) par(oma=c(3,3,3,3)) # all sides have 3 lines of space par(mar=c(5,4,4,2) + 0.1) plot(x=1:10, y=1:10, type="n", xlab="X", ylab="Y") # Add Text tot the Plot Part - red text(5,5, "Plot", col="red", cex=2) text(5,4, "text(5,5, \"Plot\", col=\"red\", cex=2)", col="red", cex=1) box("plot", col="red", lwd=2) # Add text to thebThe Figure Part - grey mtext("Margins", side=3, line=2, cex=1.5, col="grey") mtext("par(mar=c(5,4,4,2) + 0.1)", side=3, line=1, cex=1, col="grey") mtext("Line 0", side=3, line=0, adj=1.0, cex=1, col="grey") mtext(" 1", side=3, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=3, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=3, line=3, adj=1.0, cex=1, col="grey") mtext("Line 0", side=2, line=0, adj=1.0, cex=1, col="grey") mtext("Line 1", side=2, line=1, adj=1.0, cex=1, col="grey") mtext("Line 2", side=2, line=2, adj=1.0, cex=1, col="grey") mtext("Line 3", side=2, line=3, adj=1.0, cex=1, col="grey") box("figure", col="grey") # The title will fit in the third line on the top of the graph. title("Ttitle - Third Line") # Note 'outer=TRUE' moves us from the figure to the outer margins. mtext("Outer Margin Area", side=1, line=1, cex=1.8, col="brown", outer=TRUE) mtext("par(oma=c(3,3,3,3))", side=1, line=2, cex=1, col="orange", outer=TRUE) mtext("Line 0", side=1, line=0, adj=0.0, cex=0.8, col="orange", outer=TRUE) mtext("Line 1", side=1, line=1, adj=0.0, cex=1, col="orange", outer=TRUE) mtext("Line 2", side=1, line=2, adj=0.0, cex=1, col="orange", outer=TRUE) box("outer", col="orange") @ \pagebreak \subsection*{} \begin{center} <>= <> @ \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Character Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{characterTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Color Palettes I} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes1Fig.pdf} \end{figure} \end{center} \pagebreak \subsection{Color Palettes II} \medskip \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{colorPalettes2Fig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Symbol Table} \begin{center} \begin{figure}[h] \includegraphics[width=\textwidth]{symbolTableFig.pdf} \end{figure} \end{center} % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "pretty"} <>= FORMAT <- tS1@format FORMAT POSITIONS <- pretty(tS1) POSITIONS LABELS <- pretty(tS1) LABELS @ % ----------------------------------------------------------------------------- \pagebreak \subsection{Axis Style "chic"} <>= axTicksByTime <- function (x, ticks.on = "auto", k = 1, labels = TRUE, format.labels = TRUE, ends = TRUE, gt = 2, lt = 30) { if (timeBased(x)) x <- xts(rep(1, length(x)), x) tick.opts <- c("years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c(10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0, length(tick.opts)), .Names = tick.opts) for (i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], " ")[[1]] ep <- endpoints(x, y[1], as.numeric(y[2])) is[i] <- length(ep) - 1 if (is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } if (is.null(cl)) ep <- NULL else ep <- endpoints(x, cl, ck) if (ends) ep <- ep + c(rep(1, length(ep) - 1), 0) if (labels) { if (is.logical(format.labels) || is.character(format.labels)) { unix <- ifelse(.Platform$OS.type == "unix", TRUE, FALSE) time.scale <- periodicity(x)$scale fmt <- ifelse(unix, "%n%b%n%Y", "%b %Y") if (time.scale == "weekly" | time.scale == "daily") fmt <- ifelse(unix, "%b %d%n%Y", "%b %d %Y") if (time.scale == "minute" | time.scale == "hourly") fmt <- ifelse(unix, "%b %d%n%H:%M", "%b %d %H:%M") if (time.scale == "seconds") fmt <- ifelse(unix, "%b %d%n%H:%M:%S", "%b %d %H:%M:%S") if (is.character(format.labels)) fmt <- format.labels names(ep) <- format(index(x)[ep], fmt) } else { names(ep) <- as.character(index(x)[ep]) } ep } } @ <>= ticks <- axTicksByTime(as.xts(tS1)) ticks @ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About the Authors} % ----------------------------------------------------------------------------- % Diethelm Wuertz \noindent\textbf{Diethelm W\"urtz} is professor at the Institute for Theoretical Physics, ITP, and for the Curriculum Computational Science and Engineering, CSE, at the Swiss Federal Institute of Technology in Zurich. He teaches Econophysics at ITP and supervises seminars in Financial Engineering. Diethelm is senior partner of Finance Online, an ETH spin-off company in Zurich, and co-founder of the Rmetrics Association in Zurich.\\ % ----------------------------------------------------------------------------- % Tobias Setz \noindent \textbf{Tobias Setz} has a Bachelor and Master in Computational Science from ETH in Zurich and has contributed with his Thesis projects on wavelet analytics and Bayesian change point analytics to this handbook. He is now a PhD student in the Econophysics group at ETH Zurich at the Institute for Theoretical Physics.\\ % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak \section*{About Rmetrics} \noindent\emph{Rmetrics Open Source Project} With hundreds of functions built on modern methods, the Rmetrics open source software combines exploratory data analysis, statistical modelling and rapid model prototyping. The R/Rmetrics packages are embedded in R, building an environment which creates a first class system for applications in teaching statistics and finance. Rmetrics covers Time Series Econometrics, Hypothesis Testing, GARCH Modelling and Volatility Forecasting, Extreme Value Theory and Copulae, Pricing of Derivatives, Portfolio Analysis, Design and Optimization, and much more. \noindent\emph{The Rmetrics Association}\\ is a non-profit taking association working in the public interest. The Rmetrics Association provides support for innovations in financial computing. We believe that the Rmetrics Open Source software has become a valuable educational tool and that it is worth ensuring its continued development and the development of future innovations in software for statistical and computational research in finance. Rmetrics provides a reference point for individuals and institutions that want to support or interact with the Rmetrics development community. Rmetrics encourages students to participate in Rmetrics' activities in the context of Student Internships. \noindent\emph{Rmetrics Software Evalution}\\ If you like to get an impression of the size and quality of the Open Source Rmetrics Program have a look on the Ohloh Rmetrics Software Evaluation. The Evalutions gives an overview about the Software Development (Code Analysis, Estimated Cost), the people behind it, and its community. \noindent\emph{Contributions to Rmetrics}\\ are coming from several educuational institutions world wide. These include the Rmetrics web site and documentation project supported by ITP/CSE ETH Zurich, the organization of Summerschools and Workshops supported by ITP/CSE ETH Zurich, the R-sig-Finance Help and Mailing List, supported by SfS ETH Zurich, the R-forge development server, supported by University of Economics in Vienna, CRAN Test and Distribution Server for R software, supported by University of Economics Vienna, the Debian Linux integration supported by the Debian Association. Many thanks to all behind these projects who gave us continuous support over the last years.\\ \noindent Rmetrics Association\\ www.rmetrics.org % %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \pagebreak % References \begin{thebibliography}{99} \bibitem{zoo} Achim Zeileis and Gabor Grothendieck (2005): \emph{ zoo: S3 Infrastructure for Regular and Irregular Time Series.} Journal of Statistical Software, 14(6), 1-27. URL http://www.jstatsoft.org/v14/i06/ \bibitem{tseries} Adrian Trapletti and Kurt Hornik (2007): \emph{tseries: Time Series Analysis and Computational Finance.} R package version 0.10-11. \bibitem{rmetrics} Diethelm W\"urtz et al. (2007): \emph{Rmetrics: Rmetrics - Financial Engineering and Computational Finance.} R package version 260.72. http://www.rmetrics.org \bibitem{ISO} International Organization for Standardization (2004): \emph{ISO 8601: Data elements and interchage formats --- Information interchange --- Representation of dates and time} URL http://www.iso.org \bibitem{R} R Development Core Team: \emph{R: A Language and Environment for Statistical Computing}, R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org \bibitem{quantmod} Jeffrey A. Ryan (2008): \emph{quantmod: Quantitative Financial Modelling Framework.} R package version 0.3-5. 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Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.60 332.87 Tm (.) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.49 330.67 Tm (/) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 320.93 Tm (0) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 320.92 Tm (1) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.60 320.87 Tm (2) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.60 320.93 Tm (3) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.60 320.87 Tm (4) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.60 320.91 Tm (5) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 320.93 Tm (6) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 320.99 Tm (7) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 311.37 Tm (8) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.60 311.38 Tm (9) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.49 312.28 Tm (:) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.49 312.82 Tm (;) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 311.96 Tm (<) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.40 311.93 Tm (=) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 311.96 Tm (>) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.82 311.37 Tm (?) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 302.59 Tm (@) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.71 301.80 Tm (A) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.93 301.80 Tm (B) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 301.80 Tm (C) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.15 301.74 Tm (D) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.16 301.80 Tm (E) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.55 301.80 Tm (F) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 301.80 Tm (G) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.71 292.23 Tm (H) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.27 292.23 Tm (I) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.08 292.24 Tm (J) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.71 292.23 Tm (K) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 292.17 Tm (L) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.04 292.23 Tm (M) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.71 292.26 Tm (N) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.71 292.25 Tm (O) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 282.67 Tm (P) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.64 282.69 Tm (Q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.38 282.67 Tm (R) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.23 282.67 Tm (S) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.16 282.67 Tm (T) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.84 282.67 Tm (U) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.84 284.29 Tm (V) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 282.61 Tm (W) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.02 273.11 Tm (X) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.42 273.06 Tm (Y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.16 273.11 Tm (Z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.27 273.72 Tm ([) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.15 273.85 Tm (\\) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 312.27 273.72 Tm (]) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.97 273.10 Tm (^) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.60 276.60 Tm (_) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.60 259.04 Tm (`) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.08 264.31 Tm (a) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 264.16 Tm (b) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 265.16 Tm (c) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 263.35 Tm (d) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.84 264.30 Tm (e) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 264.44 Tm (f) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.96 265.14 Tm (g) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.19 255.42 Tm (h) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 254.73 Tm (i) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 255.57 Tm (j) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 254.67 Tm (k) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 253.78 Tm (l) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 255.56 Tm (m) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.52 254.71 Tm (n) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.40 254.75 Tm (o) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 245.24 Tm (p) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.52 244.42 Tm (q) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 246.03 Tm (r) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.19 245.19 Tm (s) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.84 245.18 Tm (t) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.30 245.15 Tm (u) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 244.85 Tm (v) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.86 245.18 Tm (w) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.63 235.38 Tm (x) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 236.46 Tm (y) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.62 235.42 Tm (z) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.68 235.58 Tm ({) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.80 235.89 Tm (|) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.68 235.58 Tm (}) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 235.50 Tm (~) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 237.54 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 227.98 Tm (€) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 227.98 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 227.98 Tm (‚) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 227.98 Tm (ƒ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 227.98 Tm („) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 227.98 Tm (…) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 227.98 Tm (†) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 227.98 Tm (‡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 218.42 Tm (ˆ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 218.42 Tm (‰) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 218.42 Tm (Š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 218.42 Tm (‹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 218.42 Tm (Œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 218.42 Tm (Ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 218.42 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 208.86 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 208.86 Tm (‘) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 208.86 Tm (’) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 208.86 Tm (“) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 208.86 Tm (”) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 208.86 Tm (•) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 208.86 Tm (–) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 208.86 Tm (—) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 199.29 Tm (˜) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 161.60 199.29 Tm (™) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 199.60 199.29 Tm (š) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 237.60 199.29 Tm (›) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 275.60 199.29 Tm (œ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 313.60 199.29 Tm () Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 351.60 199.29 Tm (ž) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 389.60 199.29 Tm (Ÿ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.60 187.04 Tm ( ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.12 186.99 Tm (¡) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.61 184.95 Tm (¢) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 187.17 Tm (£) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 274.93 187.07 Tm (¤) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 187.62 Tm (¥) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.60 187.76 Tm (¦) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.59 187.70 Tm (§) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.59 178.11 Tm (¨) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.59 178.17 Tm (©) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.59 178.12 Tm (ª) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 178.18 Tm («) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 178.18 Tm (¬) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 176.53 Tm (­) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 178.18 Tm (®) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 176.70 Tm (¯) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.00 166.32 Tm (°) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 168.02 Tm (±) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.96 165.82 Tm (²) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 168.05 Tm (³) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.40 168.47 Tm (´) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 168.49 Tm (µ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 167.70 Tm (¶) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.76 168.26 Tm (·) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.40 158.93 Tm (¸) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.40 158.94 Tm (¹) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.40 158.94 Tm (º) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 235.40 158.92 Tm (») Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.60 160.73 Tm (¼) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 157.48 Tm (½) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.60 159.06 Tm (¾) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.97 158.59 Tm (¿) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.31 148.92 Tm (À) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.86 148.73 Tm (Á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.42 148.60 Tm (Â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.65 150.03 Tm (Ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.53 148.85 Tm (Ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.53 148.84 Tm (Å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.31 148.70 Tm (Æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.53 149.44 Tm (Ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 140.01 Tm (È) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 140.03 Tm (É) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.75 140.53 Tm (Ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.75 140.03 Tm (Ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.75 140.03 Tm (Ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 140.53 Tm (Í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 348.75 140.04 Tm (Î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 386.75 139.92 Tm (Ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.53 129.66 Tm (Ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 158.75 129.55 Tm (Ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 129.73 Tm (Ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 129.71 Tm (Ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.04 128.49 Tm (Ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.31 129.75 Tm (Õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.40 128.83 Tm (Ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.60 130.27 Tm (×) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 120.75 121.63 Tm (Ø) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 159.19 120.97 Tm (Ù) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 197.19 120.88 Tm (Ú) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 233.43 120.83 Tm (Û) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 271.65 120.79 Tm (Ü) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.19 119.13 Tm (Ý) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 347.65 120.83 Tm (Þ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.19 119.30 Tm (ß) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 121.62 110.24 Tm (à) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 111.03 Tm (á) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 196.44 110.62 Tm (â) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.44 110.58 Tm (ã) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.46 109.36 Tm (ä) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.75 110.65 Tm (å) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 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105.98 256.33 l 95.18 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 248.20 Tm (13) Tj ET 1.000 0.000 1.000 rg 95.18 217.80 m 105.98 217.80 l 105.98 228.60 l 95.18 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 220.39 Tm (14) Tj ET 1.000 1.000 0.000 rg 95.18 190.07 m 105.98 190.07 l 105.98 200.87 l 95.18 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 192.73 Tm (15) Tj ET 0.745 0.745 0.745 rg 95.18 162.33 m 105.98 162.33 l 105.98 173.13 l 95.18 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 165.00 Tm (16) Tj ET 95.18 134.60 m 105.98 134.60 l 105.98 145.40 l 95.18 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 137.19 Tm (17) Tj ET 1.000 0.000 0.000 rg 95.18 106.87 m 105.98 106.87 l 105.98 117.67 l 95.18 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 109.53 Tm (18) Tj ET 0.000 0.804 0.000 rg 95.18 79.13 m 105.98 79.13 l 105.98 89.93 l 95.18 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 109.12 81.80 Tm (19) Tj ET 0.000 0.000 1.000 rg 124.03 328.73 m 134.83 328.73 l 134.83 339.53 l 124.03 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 331.40 Tm (20) Tj ET 0.000 1.000 1.000 rg 124.03 301.00 m 134.83 301.00 l 134.83 311.80 l 124.03 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 303.59 Tm (21) Tj ET 1.000 0.000 1.000 rg 124.03 273.27 m 134.83 273.27 l 134.83 284.07 l 124.03 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 275.85 Tm (22) Tj ET 1.000 1.000 0.000 rg 124.03 245.53 m 134.83 245.53 l 134.83 256.33 l 124.03 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 248.20 Tm (23) Tj ET 0.745 0.745 0.745 rg 124.03 217.80 m 134.83 217.80 l 134.83 228.60 l 124.03 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 220.39 Tm (24) Tj ET 124.03 190.07 m 134.83 190.07 l 134.83 200.87 l 124.03 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 192.73 Tm (25) Tj ET 1.000 0.000 0.000 rg 124.03 162.33 m 134.83 162.33 l 134.83 173.13 l 124.03 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 165.00 Tm (26) Tj ET 0.000 0.804 0.000 rg 124.03 134.60 m 134.83 134.60 l 134.83 145.40 l 124.03 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 137.19 Tm (27) Tj ET 0.000 0.000 1.000 rg 124.03 106.87 m 134.83 106.87 l 134.83 117.67 l 124.03 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 109.53 Tm (28) Tj ET 0.000 1.000 1.000 rg 124.03 79.13 m 134.83 79.13 l 134.83 89.93 l 124.03 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 137.96 81.80 Tm (29) Tj ET 1.000 0.000 1.000 rg 152.88 328.73 m 163.68 328.73 l 163.68 339.53 l 152.88 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 331.40 Tm (30) Tj ET 1.000 1.000 0.000 rg 152.88 301.00 m 163.68 301.00 l 163.68 311.80 l 152.88 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 303.66 Tm (31) Tj ET 0.745 0.745 0.745 rg 152.88 273.27 m 163.68 273.27 l 163.68 284.07 l 152.88 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 275.93 Tm (32) Tj ET 152.88 245.53 m 163.68 245.53 l 163.68 256.33 l 152.88 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 248.20 Tm (33) Tj ET 1.000 0.000 0.000 rg 152.88 217.80 m 163.68 217.80 l 163.68 228.60 l 152.88 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 220.46 Tm (34) Tj ET 0.000 0.804 0.000 rg 152.88 190.07 m 163.68 190.07 l 163.68 200.87 l 152.88 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 192.73 Tm (35) Tj ET 0.000 0.000 1.000 rg 152.88 162.33 m 163.68 162.33 l 163.68 173.13 l 152.88 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 165.00 Tm (36) Tj ET 0.000 1.000 1.000 rg 152.88 134.60 m 163.68 134.60 l 163.68 145.40 l 152.88 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 137.26 Tm (37) Tj ET 1.000 0.000 1.000 rg 152.88 106.87 m 163.68 106.87 l 163.68 117.67 l 152.88 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 109.53 Tm (38) Tj ET 1.000 1.000 0.000 rg 152.88 79.13 m 163.68 79.13 l 163.68 89.93 l 152.88 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 166.81 81.80 Tm (39) Tj ET 0.745 0.745 0.745 rg 181.73 328.73 m 192.53 328.73 l 192.53 339.53 l 181.73 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 331.40 Tm (40) Tj ET 181.73 301.00 m 192.53 301.00 l 192.53 311.80 l 181.73 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 303.59 Tm (41) Tj ET 1.000 0.000 0.000 rg 181.73 273.27 m 192.53 273.27 l 192.53 284.07 l 181.73 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 275.85 Tm (42) Tj ET 0.000 0.804 0.000 rg 181.73 245.53 m 192.53 245.53 l 192.53 256.33 l 181.73 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 248.20 Tm (43) Tj ET 0.000 0.000 1.000 rg 181.73 217.80 m 192.53 217.80 l 192.53 228.60 l 181.73 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 220.39 Tm (44) Tj ET 0.000 1.000 1.000 rg 181.73 190.07 m 192.53 190.07 l 192.53 200.87 l 181.73 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 192.73 Tm (45) Tj ET 1.000 0.000 1.000 rg 181.73 162.33 m 192.53 162.33 l 192.53 173.13 l 181.73 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 165.00 Tm (46) Tj ET 1.000 1.000 0.000 rg 181.73 134.60 m 192.53 134.60 l 192.53 145.40 l 181.73 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 137.19 Tm (47) Tj ET 0.745 0.745 0.745 rg 181.73 106.87 m 192.53 106.87 l 192.53 117.67 l 181.73 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 109.53 Tm (48) Tj ET 181.73 79.13 m 192.53 79.13 l 192.53 89.93 l 181.73 89.93 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 195.66 81.80 Tm (49) Tj ET 1.000 0.000 0.000 rg 210.58 328.73 m 221.38 328.73 l 221.38 339.53 l 210.58 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 331.40 Tm (50) Tj ET 0.000 0.804 0.000 rg 210.58 301.00 m 221.38 301.00 l 221.38 311.80 l 210.58 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 303.66 Tm (51) Tj ET 0.000 0.000 1.000 rg 210.58 273.27 m 221.38 273.27 l 221.38 284.07 l 210.58 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 275.93 Tm (52) Tj ET 0.000 1.000 1.000 rg 210.58 245.53 m 221.38 245.53 l 221.38 256.33 l 210.58 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 248.20 Tm (53) Tj ET 1.000 0.000 1.000 rg 210.58 217.80 m 221.38 217.80 l 221.38 228.60 l 210.58 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 220.46 Tm (54) Tj ET 1.000 1.000 0.000 rg 210.58 190.07 m 221.38 190.07 l 221.38 200.87 l 210.58 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 192.79 Tm (55) Tj ET 0.745 0.745 0.745 rg 210.58 162.33 m 221.38 162.33 l 221.38 173.13 l 210.58 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 165.00 Tm (56) Tj ET 210.58 134.60 m 221.38 134.60 l 221.38 145.40 l 210.58 145.40 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 137.32 Tm (57) Tj ET 1.000 0.000 0.000 rg 210.58 106.87 m 221.38 106.87 l 221.38 117.67 l 210.58 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 109.53 Tm (58) Tj ET 0.000 0.804 0.000 rg 210.58 79.13 m 221.38 79.13 l 221.38 89.93 l 210.58 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 224.51 81.80 Tm (59) Tj ET 0.000 0.000 1.000 rg 239.42 328.73 m 250.22 328.73 l 250.22 339.53 l 239.42 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 331.40 Tm (60) Tj ET 0.000 1.000 1.000 rg 239.42 301.00 m 250.22 301.00 l 250.22 311.80 l 239.42 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 303.66 Tm (61) Tj ET 1.000 0.000 1.000 rg 239.42 273.27 m 250.22 273.27 l 250.22 284.07 l 239.42 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 275.93 Tm (62) Tj ET 1.000 1.000 0.000 rg 239.42 245.53 m 250.22 245.53 l 250.22 256.33 l 239.42 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 248.20 Tm (63) Tj ET 0.745 0.745 0.745 rg 239.42 217.80 m 250.22 217.80 l 250.22 228.60 l 239.42 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 220.46 Tm (64) Tj ET 239.42 190.07 m 250.22 190.07 l 250.22 200.87 l 239.42 200.87 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 192.73 Tm (65) Tj ET 1.000 0.000 0.000 rg 239.42 162.33 m 250.22 162.33 l 250.22 173.13 l 239.42 173.13 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 165.00 Tm (66) Tj ET 0.000 0.804 0.000 rg 239.42 134.60 m 250.22 134.60 l 250.22 145.40 l 239.42 145.40 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 137.26 Tm (67) Tj ET 0.000 0.000 1.000 rg 239.42 106.87 m 250.22 106.87 l 250.22 117.67 l 239.42 117.67 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 109.53 Tm (68) Tj ET 0.000 1.000 1.000 rg 239.42 79.13 m 250.22 79.13 l 250.22 89.93 l 239.42 89.93 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 253.36 81.80 Tm (69) Tj ET 1.000 0.000 1.000 rg 268.27 328.73 m 279.07 328.73 l 279.07 339.53 l 268.27 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 331.40 Tm (70) Tj ET 1.000 1.000 0.000 rg 268.27 301.00 m 279.07 301.00 l 279.07 311.80 l 268.27 311.80 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 303.59 Tm (71) Tj ET 0.745 0.745 0.745 rg 268.27 273.27 m 279.07 273.27 l 279.07 284.07 l 268.27 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 275.85 Tm (72) Tj ET 268.27 245.53 m 279.07 245.53 l 279.07 256.33 l 268.27 256.33 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 282.21 248.20 Tm (73) 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282.21 81.80 Tm (79) Tj ET 0.745 0.745 0.745 rg 297.12 328.73 m 307.92 328.73 l 307.92 339.53 l 297.12 339.53 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 331.40 Tm (80) Tj ET 297.12 301.00 m 307.92 301.00 l 307.92 311.80 l 297.12 311.80 l h f BT /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 303.66 Tm (81) Tj ET 1.000 0.000 0.000 rg 297.12 273.27 m 307.92 273.27 l 307.92 284.07 l 297.12 284.07 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 275.93 Tm (82) Tj ET 0.000 0.804 0.000 rg 297.12 245.53 m 307.92 245.53 l 307.92 256.33 l 297.12 256.33 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 248.20 Tm (83) Tj ET 0.000 0.000 1.000 rg 297.12 217.80 m 307.92 217.80 l 307.92 228.60 l 297.12 228.60 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 220.46 Tm (84) Tj ET 0.000 1.000 1.000 rg 297.12 190.07 m 307.92 190.07 l 307.92 200.87 l 297.12 200.87 l h f BT 0.000 0.000 0.000 rg /F2 1 Tf 8.00 0.00 -0.00 8.00 311.06 192.73 Tm 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efb5d1677a5e3f01559bf066ec86cc1a *vignettes/colorwheel.pdf 3c2c1851329140ae3c375a6da33bd27c *vignettes/symbolTableFig.pdf 618f03bbb6cb368076660795995056a9 *vignettes/timeSeriesPlot.Rnw timeSeries/build/0000755000176000001440000000000012620124760013512 5ustar ripleyuserstimeSeries/build/vignette.rds0000644000176000001440000000033012620124760016045 0ustar ripleyusers‹‹àb```b`fbb`b2™… 1# '*ÉÌM N-ÊL-ÈÉ/Ñ Ê+GS! /ÉÌKWPG¨UWðOÊJM.)&`\AJš t nËCÔ ’zÖ¼ÄÜTtkØ]R RóR@Âÿ°ëgü¦…Ã;µ²<¿¦E T ‹[fN*ÌÞÌ8‡9ÀÅ Êd Bw†ù(îç,Ê/׃ù# @â? {49'±Ý£\)‰%‰ziE@ý wœ³ÿ„ÓtimeSeries/DESCRIPTION0000644000176000001440000000163112633467257014141 0ustar ripleyusersPackage: timeSeries Title: Rmetrics - Financial Time Series Objects Date: 2015-12-12 Version: 3022.101.2 Author: Rmetrics Core Team, Diethelm Wuertz [aut], Tobias Setz [cre], Yohan Chalabi [ctb] Maintainer: Tobias Setz Description: Environment for teaching "Financial Engineering and Computational Finance". Managing financial time series objects. Depends: R (>= 2.10), graphics, grDevices, stats, methods, utils, timeDate (>= 2150.95) Suggests: RUnit, robustbase, xts, PerformanceAnalytics, fTrading Note: SEVERAL PARTS ARE STILL PRELIMINARY AND MAY BE CHANGED IN THE FUTURE. THIS TYPICALLY INCLUDES FUNCTION AND ARGUMENT NAMES, AS WELL AS DEFAULTS FOR ARGUMENTS AND RETURN VALUES. LazyData: yes License: GPL (>= 2) URL: http://www.rmetrics.org NeedsCompilation: no Packaged: 2015-12-14 07:05:22 UTC; ripley Repository: CRAN Date/Publication: 2015-12-14 08:24:31 timeSeries/ChangeLog0000644000176000001440000006332512620124746014202 0ustar ripleyusers2015-11-09 tsetz * updated rank function * exported getUnits and .DollarNames as S3 methods 2014-06-17 wuertz * vignette 'plotting timeSeries objects' added * DESCRIPTION, required packages added 2014-06-16 wuertz * Refcard PDF added to doc directory 2013-03-25 chalabi * DESCRIPTION: Updated version number * R/methods-mathOps.R: callGeneric() seems to have troubles in finding variables defined in the function frame and passed to the generic with the dots arguments. 2013-03-15 chalabi * ChangeLog, DESCRIPTION: Updated ChangeLog and DSC files * DESCRIPTION: Updated maintainer field and version number * R/zzz.R: Removed deprecated .First.lib() * R/base-apply.R: Added out of range test in apply,method-timeSeries 2013-02-22 tsetz * R/statistics-rollMean.R: functions rollMin and rollMax exchanged and function rollMin corrected, * rollStats timeSeries name corrected 2012-08-12 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog 2012-08-07 chalabi * DESCRIPTION: updated version number * R/base-subsetting.R: Removed C level call to 'find_interv_vec' due to changes in R-devel 2012-03-21 chalabi * ChangeLog, DESCRIPTION: updated ChangeLog and DESC * NAMESPACE, R/utils-getArgs.R, man/utils-getArgs.Rd: moved getArgs to fBasics where it was already defined * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * man/utils-getArgs.Rd: added manual page for getArgs * R/utils-getArgs.R: getArgs() can now handle signature of length > 1 * NAMESPACE: added getArgs to NAMESPACE * R/methods-plot.R: pretty.timeSeries can not handle signal series * NAMESPACE: updated NAMESPACE * ChangeLog, DESCRIPTION: updated DESCRIPTION and ChangeLog * DESCRIPTION: updated version number 2012-03-20 chalabi * DESCRIPTION: updated DESC file 2012-03-20 wuertz * NAMESPACE: updated * R/timeSeries-slotSeries.R: had conflicts with mu fPortfolio * man/timeSeries-slotSeries.Rd: 2012-03-19 chalabi * DESCRIPTION: updated DSC * man/methods-plot.Rd: updated manual page * R/fin-daily.R: fixed patial argument names * data/LPP2005REC.rda, data/MSFT.rda, data/USDCHF.rda: resaved data to reduce file disk storage 2012-03-17 wuertz * man/methods-plot.Rd: plot examples updated for pretty label positions * ChangeLog, NAMESPACE, R/methods-plot.R, man/methods-plot.Rd: pretty added 2012-03-16 wuertz * R/fin-drawdowns.R: drawdowns made visible 2011-11-01 chalabi * DESCRIPTION, R/fin-align.R, R/fin-daily.R, man/fin-align.Rd, man/fin-daily.Rd: alignDailySeries is now based on the align timeSeries method which is now based on the align timeDate method in timeDate (>= 2150.95). * inst/unitTests/runit.TimeSeriesData.R: updated unit test with new lag,timeSeries-method * R/stats-lag.R: fixed lag,timeSeries method when colnames are provided by 'units' argument. * R/base-cbind.R: Better handling of FinCenter in cbind.timeSeries method. * R/timeSeries.R: timeSeries method now works also with non default FinCenter when timestamps are given as a numerical vector. 2011-10-24 chalabi * R/stats-lag.R, inst/unitTests/runit.lag.R: Thanks to Daniele Amberti, lag timeSeries methods now returns proper colnames when used with multiple lag indexes. * inst/unitTests/runit.merge.R: * R/base-merge.R: Thanks to Daniele Amberti, merge timeSeries method now poperly converts colnames to valid data.frame names. 2011-10-11 tsetz * R/fin-drawdowns.R: Drawdowns are now compatible to the results of the drawdown function from performanceAnalytics ... 2011-09-23 mmaechler * DESCRIPTION: remove deprecated "LazyLoad" entry 2011-08-02 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog 2011-05-04 chalabi * R/fin-align.R: Fixed integer overflow when dealing with very long historical data (1800 - today) in align method. 2011-04-21 wuertz * NAMESPACE, R/statistics-rollMean.R, man/statistics-rollMean.Rd: function rollStats added 2011-03-31 wuertz * NAMESPACE, R/fin-smooth.R, R/statistics-smoothLowess.R, man/base-rev.Rd, man/base-sort.Rd, man/base-start.Rd, man/fin-smooth.Rd, man/statistics-smoothLowess.Rd, man/timeSeries-readSeries.Rd, man/timeSeries-slotFinCenter.Rd, man/timeSeries-slotSeries.Rd, man/timeSeries-slotTime.Rd, man/timeSeries-slotUnits.Rd: help pages added and script files renamed * man/timeSeries-finCenter.Rd, man/timeSeries-slotFinCenter.Rd, man/timeSeries-slotUnits.Rd, man/utils-str.Rd, man/utils-structure.Rd: function renames * R/timeSeries-description.R, R/timeSeries-finCenter.R, R/timeSeries-series.R, R/timeSeries-slotFinCenter.R, R/timeSeries-slotSeries.R, R/timeSeries-slotTime.R, R/timeSeries-slotUnits.R, R/timeSeries-time.R, R/utils-description.R, R/utils-str.R, R/utils-structure.R: files renamed * man/timeSeries-description.Rd, man/timeSeries-series.Rd, man/timeSeries-slotSeries.Rd, man/timeSeries-slotTime.Rd, man/timeSeries-time.Rd, man/utils-description.Rd: files renamed * R/base-colCumsums.R, R/base-colSums.R, R/base-rowCumsums.R, R/fin-orderColnames.R, R/fin-orderStatistics.R, R/fin-rollmean.R, R/statistics-colCumsums.R, R/statistics-colSums.R, R/statistics-orderColnames.R, R/statistics-orderStatistics.R, R/statistics-rollMean.R, R/statistics-rowCumsums.R, man/base-colCumsums.Rd, man/base-colSums.Rd, man/base-rowCumsums.Rd, man/fin-orderColnames.Rd, man/fin-orderStatistics.Rd, man/fin-rollMean.Rd, man/statistics-colCumsums.Rd, man/statistics-colSums.Rd, man/statistics-orderColnames.Rd, man/statistics-orderStatistics.Rd, man/statistics-rollMean.Rd, man/statistics-rowCumsums.Rd: new file group for statistics and inference introduced. * NAMESPACE, R/aaa-Deprecated.R, R/base-Extract.R, R/base-attach.R, R/base-colSums.R, R/base-comment.R, R/base-rowCumsums.R, R/base-sort.R, R/base-start.R, R/base-subsetting.R, R/base-t.R, R/data-examples.R, R/fin-align.R, R/fin-daily.R, R/fin-drawdowns.R, R/fin-durations.R, R/fin-monthly.R, R/fin-periodical.R, R/fin-rollmean.R, R/fin-runlengths.R, R/fin-smooth.R, R/fin-splits.R, R/fin-spreads.R, R/fin-turnpoints.R, R/graphics-plot.R, R/methods-as.R, R/methods-comment.R, R/methods-is.R, R/methods-mathOps.R, R/methods-plot.R, R/old2new.R, R/stats-aggregate.R, R/stats-filter.R, R/stats-lag.R, R/stats-model.frame.R, R/stats-na.contiguous.R, R/stats-na.omit.R, R/stats-window.R, R/timeSeries-description.R, R/timeSeries-dummy.R, R/timeSeries-finCenter.R, R/timeSeries-getDataPart.R, R/timeSeries-isOHLC.R, R/timeSeries-isRegular.R, R/timeSeries-isUnivariate.R, R/timeSeries-readSeries.R, R/timeSeries-series.R, R/timeSeries-signalCounts.R, R/timeSeries-time.R, R/timeSeries.R, R/utils-getArgs.R, R/utils-head.R, R/utils-old2new.R, R/utils-str.R, man/00timeSeries-package.Rd, man/base-diff.Rd, man/base-merge.Rd, man/base-rev.Rd, man/base-sample.Rd, man/base-scale.Rd, man/base-sort.Rd, man/base-start.Rd, man/base-subsetting.Rd, man/data-examples.Rd, man/data.Rd, man/fin-align.Rd, man/fin-cumulated.Rd, man/fin-daily.Rd, man/fin-drawdowns.Rd, man/fin-durations.Rd, man/fin-periodical.Rd, man/fin-rollMean.Rd, man/fin-runlengths.Rd, man/fin-smooth.Rd, man/fin-splits.Rd, man/fin-turnpoints.Rd, man/graphics-plot.Rd, man/methods-comment.Rd, man/methods-plot.Rd, man/stats-window.Rd: several smaller updates: man pages improved, rolling statistics and smoother function added, some obsolete functions declared as deprecated. 2011-03-09 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * inst/doc: removed empty directory * DESCRIPTION: updated version number * R/timeSeries-readSeries.R: Fixed readSeries when reading tables with multiple columns. (Reported by Chris Breton). 2011-02-10 chalabi * ChangeLog, DESCRIPTION: updated Date and Revision field in DESC file and updated Changelog file * DESCRIPTION: updated DESC file * man/timeSeries-readSeries.Rd, man/timeSeries.Rd: added manual page for function readSeries() * inst/doc/TimeSeriesFAQ.pdf: removed pdf file because new version can be found on the website 2011-01-31 chalabi * R/timeSeries-readSeries.R, man/timeSeries.Rd: Added the optional 'format' argument and and a warning when the provided format produces NAs in readSeries function 2010-10-27 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog file 2010-10-26 chalabi * R/base-cbind.R: cbind now extends vectors to match number of rows of the timeSeries object. 2010-09-30 chalabi * NAMESPACE: updated NAMESPACE 2010-09-24 chalabi * DESCRIPTION, NAMESPACE: updated NAMESPACE and version number in DESC file 2010-08-20 chalabi * R/stats-na.omit.R: removeNA is now the same as na.omit 2010-08-12 chalabi * R/graphics-plot.R: added grid function in .plotTimeSeries when using single plot type. * R/base-cbind.R, inst/unitTests/runit.bind.R: fixed issued reported by Thomas Etheber when using cbind method with timeSeries object with one record. 2010-07-26 chalabi * ChangeLog, DESCRIPTION: updated DESC and ChangeLog * DESCRIPTION: updated version number 2010-07-08 chalabi * R/methods-mathOps.R, R/stats-lag.R, R/stats-na.omit.R: improved support recordIDs * R/timeSeries-finCenter.R: cleanup code * R/base-apply.R, R/base-cbind.R, R/base-diff.R, R/base-rowCumsums.R, R/fin-runlengths.R: improved support of recordIDs 2010-07-06 chalabi * ChangeLog, DESCRIPTION: updated DESC and Changelog 2010-07-05 chalabi * R/methods-show.R: code cleanup 2010-07-02 chalabi * R/methods-show.R: Improved recordIDs handling in show method. 2010-05-17 chalabi * R/base-start.R: updated start/end to handle TZ 2010-04-22 chalabi * R/timeSeries-readSeries.R: improved dates management in readSeries() 2010-04-14 chalabi * NAMESPACE: updated NAMESPACE 2010-01-23 wuertz * NAMESPACE, R/aaa-Deprecated.R, R/fin-align.R, R/timeSeries-description.R, R/utils-getArgs.R: internal function .getArgs added 2010-01-22 wuertz * R/aaa-Deprecated.R, R/fin-durations.R, R/fin-spreads.R: deprecated functions moved to aaa-deprecated.R, started to clean up .... * R/fin-rollmean.R: code cleaned and description added * NAMESPACE, R/fin-runlength.R, R/fin-runlengths.R, man/fin-runlengths.Rd: .runlengths added 2010-01-06 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * inst/unitTests/runit.aggregate.R, inst/unitTests/runit.subset.R: fixed unit tests for new years 2009-12-24 chalabi * NAMESPACE: updated NAMESPACE 2009-12-20 wuertz * NAMESPACE, R/fin-runlength.R: .runlengths() function added 2009-12-13 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * man/base-subset.Rd: added aliased in manual pages 2009-12-10 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated Changelog and DESC file * R/base-Extract.R: added completion method after the $ sign. * R/base-Extract.R, man/base-subset.Rd: updated signature list of timeSeries,$ method according to changes in r50609 in R-devel. 2009-10-26 wuertz * R/fin-drawdowns.R: example lin in script corrected 2009-10-05 chalabi * R/graphics-plot.R: improved handling of NA's in plot,timeSeries-method 2009-09-30 chalabi * inst/doc, inst/doc/TimeSeriesFAQ.pdf: added pdf files in inst/doc 2009-09-28 chalabi * DESCRIPTION: updated version number * ChangeLog, DESCRIPTION: updated DESCR and ChangeLog * R/AllClass.R: Added prototype in timeSeries class definition. 2009-09-02 chalabi * NAMESPACE, R/base-Extract.R, man/base-subset.Rd, man/timeSeries.Rd: added methods to subset timeSeries object with POSIXt and Date time stamps * NAMESPACE, R/stats-filter.R, man/stats-filter.Rd: added filter,timeSeries-method * NAMESPACE, R/base-t.R, man/base-t.Rd: added t,timeSeries-method * R/stats-na.contiguous.R, inst/unitTests/runit.na.contiguous.R, man/stats-na.contiguous.Rd: added na.contiguous,timeSeries-method. 2009-08-30 wuertz * R/base-apply.R: back to the previous version * R/base-attach.R: description added to attach * R/base-apply.R: apply should work now in all cases 2009-08-30 chalabi * NAMESPACE, R/timeSeries-isRegular.R, man/timeSeries-isRegular.Rd: isRegular methods are now proper S4 methods. 2009-08-30 wuertz * man/base-colCumsumsRd: file with missing dot deleted * man/base-colCumsums.Rd: missing dot in file name added 2009-08-28 wuertz * R/base-diff.R, R/methods-Ops.R, R/methods-mathOps.R, man/base-rowCumsums.Rd, man/fin-align.Rd, man/methods-DataPart.Rd, man/methods-align.Rd, man/methods-rowCum.Rd, man/time.Rd, man/timeSeries-getDataPart.Rd, man/timeSeries-time.Rd: renaming of man files finished * NAMESPACE, R/fin-monthly.R, R/fin-rollmean.R, R/timeSeries-isRegular.R, man/apply.Rd, man/base-apply.Rd, man/base-attach.Rd, man/base-cbind.Rd, man/base-colCumsumsRd, man/base-colSums.Rd, man/base-dim.Rd, man/base-rank.Rd, man/base-subset.Rd, man/colCum.Rd, man/colStats.Rd, man/cumulated.Rd, man/daily.Rd, man/description.Rd, man/drawdowns.Rd, man/durations.Rd, man/fin-cumulated.Rd, man/fin-daily.Rd, man/fin-drawdowns.Rd, man/fin-durations.Rd, man/fin-monthly.Rd, man/fin-orderColnames.Rd, man/fin-orderStatistics.Rd, man/fin-returns.Rd, man/fin-spreads.Rd, man/finCenter.Rd, man/graphics-plot.Rd, man/is.Rd, man/isUnivariate.Rd, man/lag.Rd, man/methods-aggregate.Rd, man/methods-attach.Rd, man/methods-bind.Rd, man/methods-dim.Rd, man/methods-is.Rd, man/methods-na.Rd, man/methods-plot.Rd, man/methods-returns.Rd, man/methods-str.Rd, man/methods-subset.Rd, man/model.frame.Rd, man/monthly.Rd, man/order.Rd, man/orderStatistics.Rd, man/rank.Rd, man/series.Rd, man/spreads.Rd, man/stats-aggregate.Rd, man/stats-lag.Rd, man/stats-model.frame.Rd, man/stats-na.omit.Rd, man/timeSeries-description.Rd, man/timeSeries-finCenter.Rd, man/timeSeries-isRegular.Rd, man/timeSeries-isUnivariate.Rd, man/timeSeries-series.Rd, man/utils-str.Rd: man script files Rd renamed to be more compatible with the script R files and the eBook * man/colCum.Rd: deleted by mistake * man/colCum.Rd: * R/timeSeries-isPeriod.R: isPeriod file no longer needed 2009-08-27 wuertz * inst/unitTests/runit.NA.R, inst/unitTests/runit.Omit.R, inst/unitTests/runit.TimeSeriesClass.R, inst/unitTests/runit.TimeSeriesCoercion.R, inst/unitTests/runit.TimeSeriesData.R, inst/unitTests/runit.TimeSeriesPositions.R, inst/unitTests/runit.aggregate.R, inst/unitTests/runit.align.R, inst/unitTests/runit.apply.R, inst/unitTests/runit.as.R, inst/unitTests/runit.attach.R, inst/unitTests/runit.bind.R, inst/unitTests/runit.colCum.R, inst/unitTests/runit.colStats.R, inst/unitTests/runit.cor.R, inst/unitTests/runit.cumulated.R, inst/unitTests/runit.daily.R, inst/unitTests/runit.dim.R, inst/unitTests/runit.drawdowns.R, inst/unitTests/runit.durations.R, inst/unitTests/runit.lag.R, inst/unitTests/runit.mathOps.R, inst/unitTests/runit.merge.R, inst/unitTests/runit.methods-plot.R, inst/unitTests/runit.methods-print.R, inst/unitTests/runit.methods-summary.R, inst/unitTests/runit.model.frame.R, inst/unitTests/runit.monthly.R, inst/unitTests/runit.order.R, inst/unitTests/runit.periodical.R, inst/unitTests/runit.rank.R, inst/unitTests/runit.returns.R, inst/unitTests/runit.rowCum.R, inst/unitTests/runit.signalCounts.R, inst/unitTests/runit.spreads.R, inst/unitTests/runit.subset.R, inst/unitTests/runit.time.R, inst/unitTests/runit.timeSeries.R, man/apply.Rd, man/colCum.Rd, man/colStats.Rd, man/cumulated.Rd, man/daily.Rd, man/data.Rd, man/description.Rd, man/drawdowns.Rd, man/durations.Rd, man/finCenter.Rd, man/is.Rd, man/isUnivariate.Rd, man/lag.Rd, man/methods-DataPart.Rd, man/methods-aggregate.Rd, man/methods-align.Rd, man/methods-as.Rd, man/methods-attach.Rd, man/methods-base.Rd, man/methods-bind.Rd, man/methods-comment.Rd, man/methods-dim.Rd, man/methods-mathOps.Rd, man/methods-na.Rd, man/methods-plot.Rd, man/methods-returns.Rd, man/methods-rowCum.Rd, man/methods-show.Rd, man/methods-stats.Rd, man/methods-str.Rd, man/methods-subset.Rd, man/model.frame.Rd, man/monthly.Rd, man/order.Rd, man/orderStatistics.Rd, man/rank.Rd, man/series.Rd, man/spreads.Rd, man/time.Rd, man/timeSeries.Rd: As a consequnece of introducing rda data files I have adapted all manual pages and all unit test files where it was necessary * data/LPP2005REC.rda, data/MSFT.rda, data/USDCHF.rda: now the rda files are there * data/LPP2005REC.Rda, data/MSFT.Rda, data/USDCHF.Rda: data files removed * data/LPP2005REC.Rda, data/LPP2005REC.csv, data/MSFT.Rda, data/USDCHF.Rda: Rda data files added, csv deleted, now all data files are in the same format makes life easier * data/MSFT.rda, data/msft.dat.csv, data/usdchf.csv: csv and rda files deleted 2009-08-17 chalabi * R/base-colCumsums.R, inst/unitTests/runit.colCum.R: Rewrite all Colcum methods to take advantages of new apply,timeSeries-methods. * R/base-apply.R: Improved apply,timeSeries-method to handle timeSeries with one row. * NAMESPACE: new NAMESPACE structure which should ease maintenance of packages. * R/methods-show.R: show,timeSeries-method is now more friendly with default "max.print" R option. * DESCRIPTION, NAMESPACE, R/base-Extract.R, R/base-cbind.R, R/base-merge.R, R/methods-Ops.R, R/methods-as.R, R/methods-show.R, R/timeSeries.R, inst/unitTests/runit.bind.R, man/methods-bind.Rd: Merge branch 'devel-timeSeries' Conflicts: pkg/timeSeries/R/base-Extract.R pkg/timeSeries/R/timeSeries.R * NAMESPACE, R/AllClass.R, R/base-Extract.R, R/timeSeries-getDataPart.R, man/methods-subset.Rd: better handling of @recordIDs with $<-,timeSeries-method. * R/methods-as.R: improved as.ts,timeSeries-method with monthly and quarterly data. 2009-06-13 chalabi * R/base-Extract.R: Improved $,timeSeries-method when matching names in @recordIDs * R/AllClass.R, R/base-Extract.R, R/base-dim.R: names,timeSeries-method returns now also the names of data in @recordIDs. names<-,timeSeries-methods works both for data part and @recordIDs. 2009-05-17 wuertz * inst/unitTests/runit.TimeSeriesCoercion.R: unit tests - still to be updated as.ts * man/methods-as.Rd, man/methods-bind.Rd, man/timeSeries.Rd: man pages updated * NAMESPACE: namespace adapted * NAMESPACE: * R/AllClass.R, R/AllGeneric.R, R/base-Extract.R, R/base-apply.R, R/base-attach.R, R/base-cbind.R, R/base-colCumsums.R, R/base-colSums.R, R/base-comment.R, R/base-diff.R, R/base-dim.R, R/base-merge.R, R/base-rank.R, R/base-rev.R, R/base-rowCumsums.R, R/base-sample.R, R/base-scale.R, R/base-sort.R, R/base-start.R, R/base-subset.R, R/fin-align.R, R/fin-cumulated.R, R/fin-daily.R, R/fin-drawdowns.R, R/fin-durations.R, R/fin-monthly.R, R/fin-orderColnames.R, R/fin-orderStatistics.R, R/fin-periodical.R, R/fin-returns.R, R/fin-smooth.R, R/fin-splits.R, R/fin-spreads.R, R/fin-turnpoints.R, R/graphics-plot.R, R/methods-Ops.R, R/methods-as.R, R/methods-is.R, R/methods-show.R, R/old2new.R, R/stats-aggregate.R, R/stats-lag.R, R/stats-model.frame.R, R/stats-na.omit.R, R/stats-window.R, R/timeSeries-description.R, R/timeSeries-dummy.R, R/timeSeries-finCenter.R, R/timeSeries-getDataPart.R, R/timeSeries-isOHLC.R, R/timeSeries-isPeriod.R, R/timeSeries-isUnivariate.R, R/timeSeries-readSeries.R, R/timeSeries-series.R, R/timeSeries-signalCounts.R, R/timeSeries-time.R, R/timeSeries.R, R/utils-head.R, R/utils-str.R, R/zzz.R: NEW FILE ORDERING CHECKED IN ... * R/AllClass.R, R/AllGeneric.R, R/colCum.R, R/colStats.R, R/cumulated.R, R/daily.R, R/description.R, R/drawdowns.R, R/dummy.R, R/durations.R, R/is.R, R/isUnivariate.R, R/methods-DataPart.R, R/methods-aggregate.R, R/methods-align.R, R/methods-apply.R, R/methods-as.R, R/methods-attach.R, R/methods-bind.R, R/methods-comment.R, R/methods-dim.R, R/methods-finCenter.R, R/methods-head.R, R/methods-lag.R, R/methods-mathOps.R, R/methods-merge.R, R/methods-na.R, R/methods-outlier.R, R/methods-plot.R, R/methods-returns.R, R/methods-rowCum.R, R/methods-series.R, R/methods-show.R, R/methods-str.R, R/methods-subset.R, R/methods-tail.R, R/methods-window.R, R/model.frame.R, R/monthly.R, R/old2new.R, R/order.R, R/orderStatistics.R, R/periodical.R, R/rank.R, R/readSeries.R, R/signalCounts.R, R/spreads.R, R/time.R, R/timeSeries.R, R/turnpoints.R, R/zzz.R: 2009-05-07 wuertz * R/methods-show.R: .print.timeSeries missing column names fixed on "h" style can now also handle "%Q" format for quarterly data * R/methods-plot.R: .plotTimeSeries can now handle different colors and plot symbols for multivariate series 2009-04-19 chalabi * DESCRIPTION: added explicit version number in Depends field for key packages * R/methods-aggregate.R: fixed colnames in aggregate,timeSeries-method * R/AllClass.R: initialize,timeSeries-method checks object with validObject * R/colStats.R: added colMeans and colSums,timeSeries-method because default function is unefficient with large timeSeries objects. 2009-04-02 chalabi * NAMESPACE: updated NAMESPACE * DESCRIPTION: more explicit depends and suggests field in DESC file. 2009-04-01 chalabi * DESCRIPTION: updated DESC file 2009-03-31 chalabi * R/AllClass.R, R/is.R, R/methods-DataPart.R: small changes to make timeSeries work with R-2.7.0. 2009-03-30 chalabi * man/data.Rd: added MSFT (timeSeries version of mstf.dat) in data folder. * NAMESPACE, R/AllClass.R, R/zzz.R: define S4 class 'difftime' with 'setOldClass()'. We will keep it until 'methods' pkg will define it alongside the other old 'base' classes. * R/methods-mathOps.R, R/timeSeries.R, inst/unitTests/runit.mathOps.R, inst/unitTests/runit.timeSeries.R, man/methods-mathOps.Rd: added explicit methods for Ops with 'ts' and 'timeSeries' arguments. 2009-03-25 chalabi * data/MSFT.rda: added timeSeries version of msft.dat dataset 2009-03-19 chalabi * R/is.R, R/methods-aggregate.R, R/methods-as.R, R/methods-bind.R, R/methods-dim.R, R/methods-head.R, R/methods-lag.R, R/methods-mathOps.R, R/methods-merge.R, R/methods-na.R, R/methods-plot.R, R/methods-str.R, R/methods-tail.R, R/methods-window.R, R/model.frame.R, R/time.R, man/lag.Rd, man/methods-aggregate.Rd, man/methods-as.Rd, man/methods-bind.Rd, man/methods-mathOps.Rd, man/methods-na.Rd, man/methods-subset.Rd, man/time.Rd: 'base' generics have now S3 and S4 methods. S3 methods are used because 'UseMethod' does not dispatch S4 methods in 'base' functions. For example 'base' functions starting with something like 'as.list' would failed without the S3 method. * R/old2new.R: added functions to convert old timeSeries format to new class 'timeSeries' * R/methods-subset.R, R/time.R, R/timeSeries.R: @positions is numeric and makes timeSeries object much faster. * R/methods-show.R, R/zzz.R: added getRmetricsOptions("max.print") * R/methods-bind.R, R/methods-merge.R: new implementation of [cb]bind and merge functions * R/is.R: added function is.signalSeries * R/methods-tail.R: optimized tail for large timeSeries * R/methods-as.R, man/methods-as.Rd: new as.list.timeSeries S3 methods. This means that functions like sapply and lapply can now work with timeSeries objects. * R/methods-bind.R, man/methods-bind.Rd: new cbind and rbind implementation in S3 method since methods:::bind_activation might create problems. Names of arguments are now supported. * R/methods-series.R: series<-,matrix-method uses now the new timeSeires() methods. * R/methods-as.R: new implementation of as.timeSeries.data.frame. Should has the same features as the previous implementation. * R/daily.R, R/methods-dim.R: improved handling of colnames and unit. functions like var() should now returns with the appropriate colnames. * R/AllClass.R, R/AllGeneric.R, R/timeSeries.R, man/timeSeries.Rd: timeSeries() is now a generic function with methods. timeSeries() should now take advantage of new implementation of timeDate() and should be faster in creating new timeSeries objects. * R/methods-subset.R: added $,timeSeries method with auto-completion of column names * NAMESPACE, R/methods-dim.R: added name,timeSeries method which return the column names 2009-02-04 chalabi * ChangeLog: * inst/NEWS: * DESCRIPTION: updated version number 2009-01-29 chalabi * R/methods-aggregate.R, man/methods-aggregate.Rd: improved aggregate,timeSeries-method 2009-01-28 chalabi * R/methods-plot.R: small changed in plot,timeSeries to avoid warning when dealing with signal series * R/methods-subset.R: timeSeries()[''] now returns a nuermic(NA) instead of logical(NA) * inst/unitTests/runit.subset.R: added RUnit test with subsetting 2009-01-12 chalabi * man/apply.Rd: fixed warning with new Rd parser * R/AllClass.R: use getDataPart method * R/is.R: use getDataPart method * R/methods-as.R: change as.matrix to use the getDatPart method * R/methods-subset.R: improved speed of sub-setting and sub-assignment and fixed problem when sub-setting with character argument without comma. * R/methods-dim.R, R/methods-head.R, R/methods-show.R, R/methods-tail.R: faster method * R/methods-bind.R: fixed colnames problem with c,rbind * NAMESPACE, R/methods-DataPart.R: added getDataPart,timeSeries method 2009-01-11 wuertz * R/align.R: old align.R script removed * R/methods-align.R: align established as method filename renamed 2009-01-07 wuertz * NAMESPACE, R/align.R, inst/unitTests/runit.aggregate.R, inst/unitTests/runit.subset.R, man/methods-align.Rd: unit tests which failed from the change 2008 to 2009 repaired, function align added the same as .align.timeSeries and documented, added to namespace 2009-01-06 wuertz * R/methods-aggregate.R: example modified was not working for 2009 * R/methods-lag.R: example modified timeSeries/man/0000755000176000001440000000000012620124746013172 5ustar ripleyuserstimeSeries/man/fin-splits.Rd0000644000176000001440000000236112620124746015553 0ustar ripleyusers\name{splits} \title{splits} \alias{splits} \description{ Searches for outlier splits in a 'timeSeries' object. } \details{ This function is thought to find splits in financial price or index series If a price or index is splitted we observe in the returns a big jump of several standard deviations which is identified usual as an outlier. } \usage{ splits(x, sd = 3, complement = TRUE, ...) } \arguments{ \item{x}{ a 'timeSeries' object. } \item{sd}{ a numeric value of standard deviations, e.g. 5 means that values larger or smaller than five times the standard deviation of the series will be detected. } \item{complement}{ a logical flag, should the outlier series or its complements be returned? } \item{\dots}{ arguments to be passed. } } \examples{ ## Create a Return Series with a Split - data <- runif(12, -1, 1) data[6] <- 20 x <- timeSeries(data, timeCalendar(), units="RUNIF") x ## Search for the Split: splits(x, sd=3, complement=TRUE) splits(x, sd=3, complement=FALSE) } timeSeries/man/timeSeries-deprecated.Rd0000644000176000001440000000052212620124746017667 0ustar ripleyusers\name{timeSeries-deprecated} \title{Deprecated functions in timeSeries package} \alias{seriesPositions} \alias{newPositions<-} \description{ \tabular{ll}{ \code{seriesPositions} \tab Extracts positions slot from a 'timeSeries', \cr \code{newPositions<-} \tab Modifies positions of a 'timeSeries' object, \cr } } timeSeries/man/timeSeries-getDataPart.Rd0000644000176000001440000000061112620124746017766 0ustar ripleyusers\name{DataPart,timeSeries-method} \title{DataPart,timeSeries-method} \alias{getDataPart,timeSeries-method} \alias{setDataPart,timeSeries-method} \description{ Utilities called to implement object@.Data of \code{timeSeries} objects. } \examples{ ## Load Microsoft Data - X <- MSFT[1:10, 1:4] ## Get Data Part - DATA <- getDataPart(X) class(DATA) } \keyword{chron} timeSeries/man/methods-show.Rd0000644000176000001440000000126512620124746016106 0ustar ripleyusers\name{print-methods} \title{Print a Time Series} \alias{show,timeSeries-method} \alias{print,timeSeries-method} \description{ Print 'timeSeries' pbjects. } % \usage{ % show.timeSeries(object) % } \arguments{ \item{object}{ an object of class \code{timeSeries}. } } \value{ Prints an object of class \code{timeSeries}. } \examples{ ## Load Micsrosoft Data - setRmetricsOptions(myFinCenter = "GMT") LPP <- MSFT[1:12, 1:4] ## Abbreviate Column Names - colnames(LPP) <- abbreviate(colnames(LPP), 6) ## Print Data Set - print(LPP) ## Alternative Use, Show Data Set - show(LPP) } \keyword{chron} timeSeries/man/timeSeries-slotUnits.Rd0000644000176000001440000000136712620124746017603 0ustar ripleyusers\name{units} \alias{getUnits} \alias{getUnits.default} \alias{setUnits<-} \title{Get and Set Unit Names of a 'timeSeries'} \description{ Gets and sets the column names of a 'timeSeries' object. The column names are also called units or unit names. } \usage{ getUnits(x) setUnits(x) <- value } \arguments{ \item{x}{ a 'timeSeries' object. } \item{value}{ a \code{vector} of unit names. } } \seealso{timeSeries()} \examples{ ## A Dummy timeSeries Object tS <- dummySeries() tS ## Get the Units - getUnits(tS) ## Assign New Units to the Series - setUnits(tS) <- c("A", "B") head(tS) } \keyword{programming} timeSeries/man/base-sort.Rd0000644000176000001440000000324012620124746015357 0ustar ripleyusers\name{sort} \alias{sort,timeSeries-method} \alias{sort.timeSeries} \title{Sorting a 'timeSeries' by Time Stamps} \description{ Sorts a 'timeSeries' object with respect to its time stamps. } \details{ Sorts a time series either in increasing or decreasing time stamp order. Internally the function \code{order} from R's base packahe is used. \code{order} generates a permutation which rearranges the time stamps in ascending or descending order. To find out if the series is unsorted, the function \code{is.unsorted} from R's base package can be called. } \usage{ \S4method{sort}{timeSeries}(x, decreasing = FALSE, \dots) } \arguments{ \item{x}{ an uni- or multivariate \code{timeSeries} object. } \item{decreasing}{ a logical flag. Should we sort in increasing or decreasing order? By default FALSE. } \item{\dots}{ optional arguments passed to other methods. } } \value{ Returns a sorted 'timeSeries' object, which can be increasing or decreasing in time. } \examples{ ## Monthly Calendar Series - x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] ## Resample the Series with respect to the time stamps - resampled <- sample(x) resampled is.unsorted(resampled) ## Now sort the serie in decreasing time order - sorted <- sort(resampled, , decreasing = TRUE) sorted is.unsorted(sorted) ## Is the reverted series ordered? - reverted <- rev(sorted) reverted is.unsorted(reverted) } \keyword{chron} timeSeries/man/fin-durations.Rd0000644000176000001440000000263712620124746016253 0ustar ripleyusers\name{durations} \title{Durations from a Time Series} \alias{durations} \alias{durationSeries} \description{ Computes durations from an object of class 'timeSeries'. } \usage{ durations(x, trim = FALSE, units = c("secs", "mins", "hours", "days")) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{trim}{ a logical value. By default \code{TRUE}, the first missing observation in the return series will be removed. } \item{units}{ a character value or vector which allows to set the units in which the durations are measured. By default durations are measured in seconds. } } \details{ Durations measure how long it takes until we get the next record in a \code{timesSeries} object. We return a time series in which for each time stamp we get the length of the period from when we got the last record. This period is measured in length specified by the argument \code{units}, for daily data use \code{units="days"}. } \value{ returns an object of class \code{timeSeries}. } \examples{ ## Compute Durations in days for the MSFT Sereries - head(durations(MSFT, units = "days")) head(durations(MSFT, trim = TRUE, units = "days")) ## The same in hours - head(durations(MSFT, trim = TRUE, units = "hours")) } \keyword{chron} timeSeries/man/methods-as.Rd0000644000176000001440000000753012620124746015532 0ustar ripleyusers\name{as} \title{timeSeries Class, Coercion and Transformation} \alias{as} \alias{as.timeSeries} \alias{as.timeSeries.default} \alias{as.timeSeries.ts} \alias{as.timeSeries.data.frame} \alias{as.timeSeries.character} \alias{as.timeSeries.zoo} \alias{as.ts,timeSeries-method} \alias{as.data.frame,timeSeries-method} \alias{as.list,timeSeries-method} \alias{as.matrix,timeSeries-method} \alias{as.ts.timeSeries} \alias{as.data.frame.timeSeries} \alias{as.list.timeSeries} \alias{as.matrix.timeSeries} \alias{coerce,ANY,timeSeries-method} \alias{coerce,ts,timeSeries-method} \alias{coerce,data.frame,timeSeries-method} \alias{coerce,character,timeSeries-method} \alias{coerce,ANY,timeSeries-method} \alias{coerce,timeSeries,tse-method} \alias{coerce,timeSeries,data.frame-method} \alias{coerce,timeSeries,list-method} \alias{coerce,timeSeries,matrix-method} \alias{coerce,timeSeries,ts-method} \description{ Functions and methods dealing with the coercion of 'timeSeries' objects. } \details{ Functions to create 'timeSeries' objects from other objects: \tabular{ll}{ \code{as.timeSeries} \tab Generic to convert an object to a 'timeSeries', \cr \code{as.timeSeries.default} \tab Returns the unchanged object, \cr \code{as.timeSeries.numeric} \tab Converts from a numeric vector, \cr \code{as.timeSseries.data.frame} \tab Converts from a numeric vector, \cr \code{as.timeSeries.matrix} \tab Converts from a matrix, \cr \code{as.timeSeries.ts} \tab Converts from an object of class 'ts', \cr \code{as.timeSeries.character} \tab Converts from a named demo file, \cr \code{as.timeSeries.zoo} \tab Converts an object of class zoo. } Functions to transform 'timeSeries' objects into other objects: \tabular{ll}{ % \code{as.vector.timeSeries} \tab Coerces a 'timeSeries' to a vector, \cr % \code{as.numeric.timeSeries} \tab Coerces a 'timeSeries' to numeric, \cr \code{as.matrix.timeSeries} \tab Coerces a 'timeSeries' to a matrix, \cr \code{as.data.frame.timeSeries} \tab Coerces a 'timeSeries' to a data.frame, \cr \code{as.ts.timeSeries} \tab S3: Coerces a 'timeSeries' to a 'ts' object. \cr \code{as.ts.timeSeries} \tab S3: Coerces a 'timeSeries' to a 'logical' object. } } \usage{ \method{as.timeSeries}{default}(x, \dots) \method{as.timeSeries}{ts}(x, \dots) \method{as.timeSeries}{data.frame}(x, \dots) \method{as.timeSeries}{character}(x, \dots) \method{as.timeSeries}{zoo}(x, \dots) \S4method{as.matrix}{timeSeries}(x, \dots) \S4method{as.ts}{timeSeries}(x, \dots) \S4method{as.data.frame}{timeSeries}(x, row.names = NULL, optional = FALSE, \dots) \S4method{as.ts}{timeSeries}(x, \dots) } \arguments{ \item{optional}{ A logical value. If \code{TRUE}, setting row names and converting column names (to syntactic names) is optional. } \item{row.names}{ \code{NULL} or a character vector giving the row names for the data frame. Missing values are not allowed. } \item{x}{ an object which is coerced according to the generic function. } \item{\dots}{ arguments passed to other methods. } } \value{ Function \code{as.timeSeries} returns a S4 object of class 'timeSeries'.\cr Functions \code{as.numeric}, \code{as.data.frame}, \code{as.matrix}, \code{as.ts} return depending on the generic function a numeric vector, a data frame, a matrix, or an object of class \code{ts}. } \examples{ ## Create an Artificial timeSeries Object - setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() data <- matrix(rnorm(12)) TS <- timeSeries(data, charvec, units = "RAND") TS ## Coerce to Vector - as.vector(TS) ## Coerce to Matrix - as.matrix(TS) ## Coerce to Data Frame - as.data.frame(TS) } \keyword{chron} timeSeries/man/base-diff.Rd0000644000176000001440000000234412620124746015304 0ustar ripleyusers\name{diff} \title{diff} \alias{diff} \alias{diff,timeSeries-method} \description{ Differences a 'timeSeries' 0bject. } \usage{ diff(x, \dots) } % lag=1, diff=1, trim=FALSE, pad=NA, \arguments{ \item{x}{ an object of class 'timeSeries'. } \item{\dots}{ further arguments to be passed. These may include } } \details{ Arguments to be passed may include:\cr \code{lag} - an integer indicating which lag to use. By default 1.\cr \code{diff} - an integer indicating the order of the difference. By default 1.\cr \code{trim} - a logical flag. Should NAs at the beginning of the series be removed? By default FALSE.\cr \code{pad} - a umeric value with which NAs should be replaced at the beginning of the series. By default NA. } \value{ Returns a differenced S4 'timeSeries' object. } \examples{ ## Load Microsoft Data Set - x <- MSFT[1:12, ] x ## Compute Differences - diff(x) ## Trimmed Differences - diff(x, trim=TRUE) ## Padded Differences - diff(x, trim=FALSE, pad=0) } \keyword{chron} timeSeries/man/base-sample.Rd0000644000176000001440000000103212620124746015646 0ustar ripleyusers\name{sample} \title{sample} \description{ Takes a sample of the specified size from the elements of a 'timeSeries. } \value{ Returns a resampled 'timeSeries' object. } \examples{ ## Monthly Calendar Series - x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] ## Resample the Series with respect to the time stamps - resampled <- sample(x) resampled is.unsorted(resampled) } \keyword{chron} timeSeries/man/base-attach.Rd0000644000176000001440000000367612620124746015651 0ustar ripleyusers\name{attach} \title{Attach a timSeries to the search path} \alias{attach} \alias{attach,timeSeries-method} \description{ Attaches a 'timeSeries' object to the search path. } \usage{ \S4method{attach}{timeSeries}(what, pos = 2, name = deparse(substitute(what)), warn.conflicts = TRUE) } \note{ Note, the function \code{detach} from the \code{base} package can be used to detach the attached objects. } \arguments{ \item{name}{ alternative way to specify the database to be attached. See for details \code{help(attach,package=base)}. } \item{pos}{ an integer specifying position in \code{search()} where to attach the database. See for details \code{help(attach,package=base)}. } \item{warn.conflicts}{ a logical value. If \code{TRUE}, warnings are printed about conflicts from attaching the database, unless that database contains an object \code{.conflicts.OK}. A conflict is a function masking a function, or a non-function masking a non-function. See for details \code{help(attach,package=base)}. } \item{what}{ [attach] - \cr database to be attached. This may currently be a timeSeries object, a data.frame or a list or a R data file created with save or NULL or an environment. See for details \code{help(attach, package=base)}. } } \value{ The environment is returned invisibly with a \code{name} attribute. } \examples{ ## Load Microsoft Data Set - x <- MSFT[1:10, ] colnames(x) ## Attach the Series and Compute the Range - attach(x) range <- High - Low range ## Convert Vector to a timeSeries Object - timeSeries(data=range, charvec=time(x), units="Range") ## Detach the series from the search path - detach("x") ans <- try(High, silent=TRUE) cat(ans[1]) } \keyword{chron} timeSeries/man/timeSeries-isRegular.Rd0000644000176000001440000000363712620124746017536 0ustar ripleyusers\name{isRegular} \title{Checks if a time series is regular} \alias{isDaily,timeSeries-method} \alias{isMonthly,timeSeries-method} \alias{isQuarterly,timeSeries-method} \alias{isRegular,timeSeries-method} \alias{frequency,timeSeries-method} \description{ Checks if a time series is regular. } \details{ What is a regular time series? If a series is a daily, a monthly, or a weekly time series then we speak of a regular series. This can be tested calling the functions \code{isDaily}, \code{isMonthly}, \code{isQuarterly}, or in general \code{isRegular} If the series is regular then the frequency of the series can be determined calling the function \code{frequency}. A time series is defined as daily if the series has not more than one date/time stamp per day.\cr A time series is defined as monthly if the series has not more than one date/time stamp per month.\cr A time series is defined as quarterly if the series has not more than one date/time stamp per quarter.\cr Note, amonthly series is also a daily series, a quarterly series is alsona monthly series.\cr With these definitions a regular series is either a monthly or a quarterly series.\cr NOT yet implemented is the case of weekly series. } \usage{ \S4method{isDaily}{timeSeries}(x) \S4method{isMonthly}{timeSeries}(x) \S4method{isQuarterly}{timeSeries}(x) \S4method{isRegular}{timeSeries}(x) \S4method{frequency}{timeSeries}(x, \dots) } \arguments{ \item{x}{ an R object of class 'timeSeries'. } \item{\dots}{ arguments to be passed. } } \value{ The \code{is*} functions return \code{TRUE} or \code{FALSE} depending on whether the series fulfills the condition or not. The function frequency returns in general 1, for quarterly series 4, and for monthly series 12. } \examples{ ## None } \keyword{chron} timeSeries/man/base-rev.Rd0000644000176000001440000000122312620124746015163 0ustar ripleyusers\name{rev} \alias{rev,timeSeries-method} \alias{rev.timeSeries} \title{Reversion of a 'timeSeries'} \description{ Reverses an uni- or multivariate 'timeSeries' object by reversing the order of the time stamps. } \usage{ \S4method{rev}{timeSeries}(x) } \arguments{ \item{x}{ an uni- or multivariate 'timeSeries' object. } } \value{ Returns a reversed 'timeSeries' object. } \examples{ ## Create Dummy timeSeries - tS <- dummySeries() ## Reverse Series - rev(tS) } \keyword{chron} timeSeries/man/stats-na.contiguous.Rd0000644000176000001440000000141112620124746017406 0ustar ripleyusers\name{na.contiguous} \title{Find Longest Contiguous Stretch of non-NAs} \alias{na.contiguous,timeSeries-method} \description{ Find the longest consecutive stretch of non-missing values in a timeSeries object. (In the event of a tie, the first such stretch.) } \usage{ \S4method{na.contiguous}{timeSeries}(object, ...) } \arguments{ \item{object}{ a timeSeries object. } \item{\dots}{ further arguments passed to or from other methods. } } \value{ A timeSeries object without missing values. } \examples{ ## Dummy timeSeries with NAs entries data <- matrix(sample(c(1:20, rep(NA,4))), ncol = 2) s <- timeSeries(data, timeCalendar()) ## Find the longest consecutive non-missing values na.contiguous(s) } timeSeries/man/timeSeries-readSeries.Rd0000644000176000001440000000402012620124746017652 0ustar ripleyusers\name{readSeries} \title{Reads a 'timeSeries' from a File} \alias{readSeries} \description{ Reads a file in table format and creates a \code{timeSeries} object from it. } \details{ The first column of the table must hold the timestamps. Format of the stimestamps can be either specified in the header of the first column or by the \code{format} argument. } \usage{ readSeries(file, header = TRUE, sep = ";", zone = "", FinCenter = "", format, \dots) } \arguments{ \item{file}{ the filename of a spreadsheet data set from which to import the data records. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{header}{ a logical value indicating whether the file contains the names of the variables as its first line. If missing, the value is determined from the file format: 'header' is set to 'TRUE' if and only if the first row contains one fewer field than the number of columns. } \item{format}{ a character string with the format in POSIX notation specifying the timestamp format. Note, the format has not to be specified if the first column in the file has the timestamp format specifyer, e.g. "\%Y-\%m-\%d" for the short ISO 8601 format. } \item{sep}{ the field seperator used in the spreadsheet file to separate columns. By default ";". Note, if \code{sep=";"} is specified, and reading the series fails, then the reading is automatically repeated with \code{sep=","}. } \item{zone}{ the time zone or financial center where the data were recorded. By default \code{zone=""} which is short for GMT. } \item{\dots}{ Additional arguments passed to \code{read.table()} function which is used to read the file. } } \value{ Returns a S4 object of class \code{timeSeries}. } timeSeries/man/stats-aggregate.Rd0000644000176000001440000000545312620124746016552 0ustar ripleyusers\name{aggregate-methods} \docType{methods} \alias{aggregate} \alias{aggregate.timeSeries} \alias{aggregate-methods} \alias{aggregate,timeSeries-method} \alias{daily2weekly} \alias{daily2monthly} \title{timeSeries Class, Functions and Methods} \description{ Aggregates a 'timeSeries' Object. } \details{ The function \code{aggregate} is a function which can aggregate time series on general aggregation periods. In addition there are two tailored function for simple usage: Function \code{daily2monthly} and \code{daily2weekly} which allow to aggregate 'timeSeries' objects from daily to monthly or weekly levels, respectively. In the case of the function \code{daily2weekly} one can explicitely the starting day of the week, the default value is Tuesday, \code{startOn="Tue"}. } \usage{ \S4method{aggregate}{timeSeries}(x, by, FUN, \dots) daily2monthly(x, init=FALSE) daily2weekly(x, startOn="Tue", init=FALSE) } \arguments{ \item{x}{ an object of class 'timeSeries'. } \item{by}{ a sequence of \code{timeDate} objects denoting the aggregation period. } \item{FUN}{ the function to be applied. } \item{startOn}{ a string value, specifying the day of week as a three letter abbreviation. Weekly aggregated data records are then fixed to the weekdays given by the argument \code{startOn}.} \item{init}{ a logical value, if set to \code{TRUE} then the time series will be indexed to 1 for its first value. By default init is set to \code{FALSE}. } \item{\dots}{ arguments passed to other methods. } } \value{ \code{aggregate} returns an aggregated S4 object of class \code{timeSeries}. \code{daily2monthly} returns an aggregated monthly object of class \code{timeSeries}. \code{daily2weekly} returns an aggregated weekly object of class \code{timeSeries} starting on the specified day of week. } \examples{ ## Load Microsoft Data Set - x <- MSFT ## Aggregate by Weeks - by <- timeSequence(from = start(x), to = end(x), by = "week") aggregate(x, by, mean) ## Aggregate to Last Friday of Month - by <- unique(timeLastNdayInMonth(time(x), 5)) X <- aggregate(x, by, mean) X dayOfWeek(time(X)) isMonthly(X) ## Aggregate to Last Day of Quarter - by <- unique(timeLastDayInQuarter(time(x))) X <- aggregate(x, by, mean) X isQuarterly(X) ## Aggregate daily records to end of month records - X <- daily2monthly(x) X isMonthly(X) ## Aggregate da, ily records to end of week records - X <- daily2weekly(x, startOn="Fri") X dayOfWeek(time(X)) } \keyword{methods} \keyword{chron} timeSeries/man/base-dim.Rd0000644000176000001440000000446112620124746015147 0ustar ripleyusers\name{dimnames} \title{Time Series Columns and Rows} \alias{dim,timeSeries-method} \alias{dim<-,timeSeries-method} \alias{dimnames,timeSeries-method} \alias{dimnames<-,timeSeries,list-method} \alias{colnames<-,timeSeries-method} \alias{colnames,timeSeries-method} \alias{rownames,timeSeries-method} \alias{rownames<-,timeSeries,timeDate-method} \alias{rownames<-,timeSeries,ANY-method} \alias{names,timeSeries-method} \alias{names<-,timeSeries-method} \description{ Handling columns and rows of 'timeSeries' objects. } \details{ \tabular{ll}{ \code{dim} \tab Returns the dimension of a 'timeSeries' object \cr \code{dimnames} \tab Returns the dimension names of a 'timeSeries' object \cr \code{colnames<-} \tab Assigns column names to a 'timeSeries' object \cr \code{rownames<-} \tab Assigns row names to a 'timeSeries' object } } % \usage{ % %\S4method{dim}{timeSeries}(x) % %\S4method{dimnames}{timeSeries}(x) % %\S4method{dimnames}{timeSeries}(x) <- value % dim(x) % dimnames(x) % dimnames(x) <- value % colnames(x) % colnames(x) <- value % rownames(x) % rownames(x) <- value % \method{is.array}{timeSeries}(x) % } %\arguments{ % % \item{value}{ % a valid value for names component of \code{dimnames(x)}. % For a \code{"timeSeries"} object this is either \code{NULL} or a % character vector of length the column dimension. Not, row names % cannot be assigne for a \code{"timeSeries"} object, the function % \code{rownames()} will stop and return an error message. % } % \item{x}{ % an object of class \code{timeSeries}. % } % %} \value{ Returns the dimensions and related numbers of a 'timeSeries' object. } \examples{ ## Load Swiss Pension Fund Benchmark Data - X <- LPP2005REC[1:10, 1:3] ## Get Dimension - dim(X) ## Get Column and Row Names - dimnames(X) ## Get Column / Row Names - colnames(X) rownames(X) ## Try your own DIM - DIM <- function(x) {c(NROW(x), NCOL(x))} DIM(X) DIM(X[, 1]) ## Try length / LENGTH - length(X) length(X[, 1]) LENGTH <- function(X) NROW(X) LENGTH(X) ## Columns / Rows - ncol(X); NCOL(X) nrow(X); NROW(X) ## See also - isUnivariate(X) isMultivariate(X) } \keyword{chron} timeSeries/man/base-rank.Rd0000644000176000001440000000367512620124746015337 0ustar ripleyusers\name{rank} \title{Sample Ranks of a Time Series} \alias{rank,timeSeries-method} \description{ Returns the sample ranks of the values of a 'timeSeries' object. } \details{ If all components are different (and no NAs), the ranks are well defined, with values in \code{seq_len(x)}. With some values equal (called ???ties???), the argument ties.method determines the result at the corresponding indices. The \code{"first"} method results in a permutation with increasing values at each index set of ties. The \code{"random"} method puts these in random order whereas the default, \code{"average"}, replaces them by their mean, and \code{"max"} and \code{"min"} replaces them by their maximum and minimum respectively, the latter being the typical sports ranking. NA values are never considered to be equal: for \code{na.last = TRUE} and \code{na.last = FALSE} they are given distinct ranks in the order in which they occur in \code{x}. } \usage{ \S4method{rank}{timeSeries}(x, na.last = TRUE, ties.method = ) } \arguments{ \item{x}{ an univariate object of class \code{timeSeries}. } \item{na.last}{ for controlling the treatment of NAs. If TRUE, missing values in the data are put last; if FALSE, they are put first; if NA, they are removed; if "keep" they are kept with rank NA. } \item{ties.method}{ a character string specifying how ties are treated; can be abbreviated. } } \value{ returns the ranks of a \code{timeSeries} object. } \examples{ ## Load Microsoft Data - X <- 100 * returns(MSFT) ## Compute the Ranks - head(rank(X[, "Open"]), 10) ## Only Interested in the Vector, then use - head(rank(series(X[, "Open"])), 10) } \keyword{chron} timeSeries/man/fin-daily.Rd0000644000176000001440000001232112620124746015334 0ustar ripleyusers\name{SpecialDailySeries} \title{Special Daily Time Series} \alias{daily} \alias{dummyDailySeries} \alias{dummySeries} \alias{alignDailySeries} \alias{rollDailySeries} %alias{ohlcDailyPlot} \description{ Special daily 'timeSeries' functions. } \details{ \tabular{ll}{ \code{dummyDailySeries} \tab Creates a dummy daily 'timeSeries' object, \cr \code{alignDailySeries} \tab Aligns a daily 'timeSeries' to new positions,\cr \code{rollDailySeries} \tab Rolls daily a 'timeSeries' on a given period, \cr \code{ohlcDailyPlot} \tab Plots open high low close bar chart, \cr \code{dummySeries} \tab Creates a dummy monthly 'timeSeries' object} } \usage{ dummyDailySeries(x = rnorm(365), units = NULL, zone = "", FinCenter = "") alignDailySeries(x, method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, units = NULL, zone = "", FinCenter = "", ...) rollDailySeries(x, period = "7d", FUN, \dots) %ohlcDailyPlot(x, volume = TRUE, colOrder = c(1:5), units = 1e6, % xlab = c("Date", "Date"), ylab = c("Price", "Volume"), % main = c("O-H-L-C", "Volume"), grid.nx = 7, grid.lty = "solid", \dots) } \arguments{ %\item{colOrder}{ % [ohlcDailyPlot] - \cr % an integer vector which gives the order of the prices and the % volume in the input object. By default the following order of % columns from 1 to 5 is assumed: Open, high, low, close, and volume. % } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{FUN}{ the function to be applied.\cr [applySeries] - \cr a function to use for aggregation, by default \code{colAvgs}. } %\item{grid.lty, grid.nx}{ % [ohlcDailyPlot] - \cr % The type of grid line and the number of grid lines used in the % plot. % } \item{include.weekends}{ [alignDailySeries] - \cr a logical value. Should weekend dates be included or removed from the series. } %\item{main}{ % [ohlcDailyPlot] - \cr % a character string to title the price and volume plot. % } \item{method}{ [alignDailySeries] - \cr the method to be used for the alignment. A character string, one of \code{"before"}, use the data from the row whose position is just before the unmatched position, or \code{"after"}, use the data from the row whose position is just after the unmatched position, or \code{"linear"}, interpolate linearly between \code{"before"} and \code{"after"}. } \item{period}{ [rollDailySeries] - \cr a character string specifying the rollling period composed by the length of the period and its unit, e.g. \code{"7d"} represents one week. } \item{units}{ [allignDailySeries] - \cr an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. %\cr %[ohlcDailyPlot] - \cr %a numeric value, specifying in which multiples the volume should %be referenced on the plot labels. By default 1e6, i.e. in units %of 1 Million. } %\item{volume}{ % [ohlcDailyPlot] - \cr % a logigical value. Should a volume plot added to the OHLC Plot. % By default \code{TRUE}. % } \item{x}{ an object of class \code{timeSeries}. } %\item{xlab, ylab}{ % [ohlcDailyPlot] - \cr % two string vectors to name the x and y axis of the price and % volume plot. % } \item{zone}{ the time zone or financial center where the data were recorded. } \item{\dots}{ arguments passed to interpolating methods. } } \value{ \code{dummyDailySeries} \cr creates from a numeric matrix with daily records of unknown dates a \code{timeSeries} object with dummy daily dates. \cr \code{alignDailySeries} \cr returns from a daily time series with missing holidays a weekly aligned daily \code{timeSeries} object \cr \code{rollDailySeries}\cr \cr returns an object of class \code{timeSeries} with rolling values, computed from the function \code{FUN}. %\cr % %\code{ohlcDailyPlot} %displays a Open-High-Low-Close Plot of daily data records. } \examples{ ## Use Microsofts' OHLCV Price Series - head(MSFT) end(MSFT) ## Cut out April Data from 2001 - Close <- MSFT[, "Close"] tsApril01 <- window(Close, start="2001-04-01", end="2001-04-30") tsApril01 ## Align Daily Series with NA - tsRet <- returns(tsApril01, trim = TRUE) GoodFriday(2001) EasterMonday(2001) alignDailySeries(tsRet, method = "fillNA", include.weekends = FALSE) alignDailySeries(tsRet, method = "fillNA", include.weekends = TRUE) ## Align Daily Series by Interpolated Values - alignDailySeries(tsRet, method = "interp", include.weekend = FALSE) alignDailySeries(tsRet, method = "interp", include.weekend = TRUE) } \keyword{chron} timeSeries/man/fin-periodical.Rd0000644000176000001440000000403512620124746016350 0ustar ripleyusers\name{periodical} \alias{endOfPeriod} \alias{endOfPeriodSeries} \alias{endOfPeriodStats} \alias{endOfPeriodBenchmarks} \title{End-of-Period Series, Stats, and Benchmarks} \description{ Computes perodical statistics back to a given period. } \details{ The function \code{endOfPeriodSeries} returns series back to a given period.\cr The function \code{endOfPeriodStats} returns statistics back to a given period.\cr The function \code{endOfPeriodBenchmarks} returns benchmarks back to a given period. \code{x} must be end of month data. Note you can create such series using for example the functions: \code{align}, \code{alignDailySeries}, \code{daily2monthly}. } \usage{ endOfPeriodSeries(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) endOfPeriodStats(x, nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) endOfPeriodBenchmarks(x, benchmark = ncol(x), nYearsBack = c("1y", "2y", "3y", "5y", "10y", "YTD")) } \arguments{ \item{x}{ an end-of-month recorded multivariate 'timeSeries' object. One of the columns holds the benchmark series specified by the argument \code{benchmark}, By defauklt this is the last column of \code{x}. } \item{nYearsBack}{ a period string. How long back should the series be treated? Options include values from 1 year to 10 years, and year-to-date: "1y", "2y", "3y", "5y", "10y", "YTD". } \item{benchmark}{ an integer giving the position of the benchmar series in \code{x}. } } \examples{ ## Load Series: Column 1:3 Swiss Market, Column 8 (4) Benchmark x <- 100 * LPP2005REC[, c(1:3, 8)] colnames(x) x <- daily2monthly(x) x ## Get the Monthly Series - endOfPeriodSeries(x, nYearsBack="1y") ## Compute the Monthly Statistics - endOfPeriodStats(x, nYearsBack="1y") ## Compute the Benchmark - endOfPeriodBenchmarks(x, benchmark=4) } \keyword{chron} timeSeries/man/base-subsetting.Rd0000644000176000001440000001230212620124746016556 0ustar ripleyusers\name{TimeSeriesSubsettings} \alias{TimeSeriesSubsettings} \alias{[,timeSeries,ANY,index_timeSeries-method} \alias{[,timeSeries,character,character-method} \alias{[,timeSeries,character,index_timeSeries-method} \alias{[,timeSeries,character,missing-method} \alias{[,timeSeries,index_timeSeries,character-method} \alias{[,timeSeries,index_timeSeries,index_timeSeries-method} \alias{[,timeSeries,index_timeSeries,missing-method} \alias{[,timeSeries,matrix,index_timeSeries-method} \alias{[,timeSeries,matrix,missing-method} \alias{[,timeSeries,missing,character-method} \alias{[,timeSeries,missing,index_timeSeries-method} \alias{[,timeSeries,missing,missing-method} \alias{[,timeSeries,timeDate,character-method} \alias{[,timeSeries,timeDate,index_timeSeries-method} \alias{[,timeSeries,timeDate,missing-method} \alias{[,timeSeries,timeSeries,index_timeSeries-method} \alias{[,timeSeries,timeSeries,missing-method} \alias{[,timeSeries,time_timeSeries,ANY-method} \alias{[,timeSeries,time_timeSeries,character-method} \alias{[,timeSeries,time_timeSeries,index_timeSeries-method} \alias{[,timeSeries,time_timeSeries,missing-method} \alias{$,timeSeries-method} \alias{[<-,timeSeries,timeDate,index_timeSeries-method} \alias{[<-,timeSeries,timeDate,missing-method} \alias{[<-,timeSeries,timeSeries,index_timeSeries-method} \alias{[<-,timeSeries,timeSeries,missing-method} \alias{[<-,timeSeries,character,character-method} \alias{[<-,timeSeries,character,index_timeSeries-method} \alias{[<-,timeSeries,character,missing-method} \alias{[<-,timeSeries,index_timeSeries,character-method} \alias{[<-,timeSeries,matrix,character-method} \alias{[<-,timeSeries,timeDate,character-method} \alias{[<-,timeSeries,timeSeries,character-method} \alias{[<-,timeSeries,character,ANY-method} \alias{[<-,timeSeries,timeDate,ANY-method} \alias{$<-,timeSeries,ANY-method} \alias{$<-,timeSeries,factor-method} \alias{$<-,timeSeries,numeric-method} \alias{$<-,timeSeries,ANY,ANY-method} \alias{$<-,timeSeries,ANY,factor-method} \alias{$<-,timeSeries,ANY,numeric-method} \alias{window,timeSeries-method} \alias{cut,timeSeries-method} \alias{head,timeSeries-method} \alias{tail,timeSeries-method} \alias{window.timeSeries} \alias{cut.timeSeries} \alias{head.timeSeries} \alias{tail.timeSeries} \alias{outlier} \alias{outlier,timeSeries-method} \alias{outlier,ANY-method} \title{Subsettig Time Series} \description{ Subset a 'timeSeries' objects due to different aspects. \cr \tabular{ll}{ \code{[} \tab "[" method for a 'timeSeries' object, \cr \code{[<-} \tab "[<-" method to assign value for a subset of a 'timeSeries' object, \cr \code{window} \tab Windows a piece from a 'timeSeries' object, \cr \code{cut} \tab A no longer used synonyme for window, \cr \code{head} \tab Returns the head of a 'timeSeries' object, \cr \code{tail} \tab Returns the tail of a 'timeSeries' object, \cr \code{outliers} \tab Removes outliers from a 'timeSeries' object. } } \usage{ % \method{[}{timeSeries}(x, i, j, drop) % \method{[}{timeSeries}(x, i, j) <- value \S4method{window}{timeSeries}(x, start, end, \dots) \S4method{head}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) \S4method{tail}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) \S4method{outlier}{timeSeries}(x, sd = 3, complement = TRUE, \dots) \S4method{cut}{timeSeries}(x, from, to, \dots) } \arguments{ \item{complement}{ [outlierSeries] - \cr a logical flag, should the outler series or its complement be returns, by default \code{TRUE} which returns the series free of outliers. } \item{from, to}{ starting date and end date, \code{to} must be after \code{from}. } \item{start, end}{ starting date and end date, \code{end} must be after \code{start}. } % \item{i, j}{ % ["["] - \cr % index arguments used for subsettings. % } \item{n}{ [head][tail] - \cr an integer specifying the number of lines to be returned. By default \code{n=6}. } \item{recordIDs}{ [head][tail] - \cr a logical value. Should the \code{recordIDs} returned together with the data matrix and time series positions? } \item{sd}{ [outlierSeries] - \cr a numeric value of standard deviations, e.g. 10 means that values larger or smaller tahn ten times the standard deviation will be removed from the series. } % \item{value}{ % a numeric value to use as a replacement. It will be repeated a % whole number of times if necessary. % } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments passed to other methods. } } \value{ All functions return an object of class 'timeSeries'. } \examples{ ## Create an Artificial timeSeries Object - setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() set.seed(4711) data <- matrix(exp(cumsum(rnorm(12, sd = 0.1)))) tS <- timeSeries(data, charvec, units = "tS") tS ## Subset Series by Counts "[" - tS[1:3, ] ## Subset the Head of the Series - head(tS, 6) } \keyword{chron} timeSeries/man/timeSeries-slotSeries.Rd0000644000176000001440000000305212620124746017724 0ustar ripleyusers\name{series-methods} \docType{methods} \alias{series} \alias{series<-} \alias{series,timeSeries-method} \alias{series<-,timeSeries,matrix-method} \alias{series<-,timeSeries,ANY-method} \alias{series<-,timeSeries,data.frame-method} \alias{series<-,timeSeries,vector-method} \alias{coredata} \alias{coredata<-} \alias{coredata,timeSeries-method} \alias{coredata<-,timeSeries,matrix-method} \alias{coredata<-,timeSeries,ANY-method} \alias{coredata<-,timeSeries,data.frame-method} \alias{coredata<-,timeSeries,vector-method} %\alias{getSeries} %\alias{getSeries.timeSeries} %\alias{setSeries<-} \title{Get and Set Data of a 'timeSeries'} \description{ \code{series} returns the \code{@.Data} slot of a \code{timeSeries} object in \code{matrix} form. New series can also be assign to an already existing \code{timeSeries}. \code{coredata} is a synonyme function nameing for \code{series}. } \usage{ %\S4method{series}{timeSeries}(x) series(x) series(x) <- value % %getSeries(x) %setSeries(x) <- value } \arguments{ \item{x}{ a \code{timeSeries} object. } \item{value}{ a \code{vector}, a \code{data.frame} or a \code{matrix} object of numeric data. } } \seealso{timeSeries()} \examples{ ## A Dummy timeSeries Object ts <- timeSeries() ts ## Get the Matrix Part - mat <- series(ts) class(mat) mat ## Assign a New Univariate Series - series(ts) <- rnorm(12) ts ## Assign a New Bivariate Series - series(ts) <- rnorm(12) ts } \keyword{programming} timeSeries/man/statistics-rollMean.Rd0000644000176000001440000000454212620124746017427 0ustar ripleyusers\name{rollMean} \title{Rolling Statistics} \alias{rollStats} \alias{rollMean} \alias{rollMin} \alias{rollMax} \alias{rollMedian} \description{ Computes rolling mean, min, max and median for a 'timeSeries' object. } \usage{ rollStats(x, k, FUN=mean, na.pad=FALSE, align=c("center", "left", "right"), \dots) rollMean(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMin(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMax(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) rollMedian(x, k, na.pad = FALSE, align = c("center", "left", "right"), \dots) } \arguments{ \item{x}{ an uni- or multivariate 'timeSeries' object. } \item{k}{ an integer width of the rolling window. Must be odd for \code{rollMedian}. } \item{FUN}{ the function to be rolled. } \item{na.pad}{ a logical flag. Should NA padding be added at beginning? By default \code{FALSE}. } \item{align}{ a character string specifying whether the index of the result should be left- or right-aligned or centered compared to the rolling window of observations. The default choice is set to \code{align="center"}. } \item{\dots}{ optional arguments to be passed. } } \value{ returns an object of class 'timeSeries'. } \details{ The code in the core of the functions \code{rollMean}, \code{rollMin}, \code{rollMax}, and \code{rollMedian} was borrowed from the package \code{zoo} authored by Achim Zeileis, Gabor Grothendieck and Felix Andrews. } \author{ Achim Zeileis, Gabor Grothendieck and Felix Andrews for code from the contributed R package \code{zoo} used in the functions \code{rollMean}, \code{rollMin}, \code{rollMax}, and \code{rollMedian}. } \examples{ ## Use Swiss Pension Fund Data Set of Returns - head(LPP2005REC) SPI <- LPP2005REC[, "SPI"] head(SPI) ## Plot Drawdowns - rmean <- rollMean(SPI, k = 10) plot(rmean) } \keyword{chron} timeSeries/man/timeSeries-slotDocumentation.Rd0000644000176000001440000000411712620124746021306 0ustar ripleyusers\name{attributes} \title{Get and Set Optional Attributes of a 'timeSeries'} \alias{attributes} \alias{documentation} \alias{getAttributes} \alias{setAttributes<-} \description{ Extracts or assigns optional attributes from or to a \code{timeSeries} object. } \usage{ getAttributes(obj) setAttributes(obj) <- value } \arguments{ \item{obj}{ a \code{timeSeries} object whose optional attributes are to be accessed. } \item{value}{ an object, the new value of the attribute, or NULL to remove the attribute. } } \details{ Each \code{timeSeries} object is documented. By default a time series object holds in the documentation slot a string with creation time and the user who has defined it. But this is not all. Optionally the whole creation process and history can be recorded. For this the \code{@documentation} slot may have an optional \code{"Attributes"} element. This attribute is tracked over the whole life time of the object whenever the time series is changed. Whenever you like to be informed about the optional attributes, or you like to recover them you can dot it, and evenmore, whenever you like to add information as an addiitonal attribute you can also do it. The two functions \code{getAttributes} and \code{setAttributes} provide access to and allow to modify the optional attributes of a \code{timeSeries} object. %The replacement form causes the named attribute to take the value %specified (or create a new attribute with the value given). %Optional attributes are attached to the \code{@documentation} slot of the %S4 \code{timeSeries} object. These attributes are preserved during operations %on \code{timeSeries} objects using the internal function \code{.appendList}. } \examples{ ## Create an artificial timeSeries Object - tS <- dummySeries() tS ## Get Optional Attributes - getAttributes(tS) tS@documentation ## Set a new Optional Attribute - setAttributes(tS) <- list(what="A dummy Series") tS getAttributes(tS) tS@documentation } \keyword{programming} timeSeries/man/base-scale.Rd0000644000176000001440000000313612620124746015463 0ustar ripleyusers\name{scale} \title{scale} \description{ Scales a 'timeSeries' object. } \details{ \code{scale} is a function to center and/or scale the columns of a 'timeSeries' object. The value of \code{center} determines how column centering is performed. If \code{center} is a numeric vector with length equal to the number of columns of \code{x}, then each column of \code{x} has the corresponding value from \code{center} subtracted from it. If \code{center} is TRUE then centering is done by subtracting the column means (omitting NAs) of \code{x} from their corresponding columns, and if \code{center} is FALSE, no centering is done. The value of \code{scale} determines how column scaling is performed (after centering). If \code{scale} is a numeric vector with length equal to the number of columns of \code{x}, then each column of \code{x} is divided by the corresponding value from \code{scale}. If \code{scale} is TRUE then scaling is done by dividing the (centered) columns of \code{x} by their standard deviations if \code{center} is TRUE, and the root mean square otherwise. If \code{scale} is FALSE, no scaling is done. } \value{ Returns a centered and/or scaled 'timeSeries' object. } \examples{ ## Load Series: x <- 100* LPP2005REC[, c("SBI", "SPI")] ## Scale and Center - X <- scale(x) hist(X[, 1], prob=TRUE) s <- seq(-3, 3, length=201) lines(s, dnorm(s), col="red") } \keyword{chron} timeSeries/man/base-t.Rd0000644000176000001440000000074612620124746014643 0ustar ripleyusers\name{t} \title{timeSeries Transpose} \alias{t,timeSeries-method} \description{ Returns the transpose of a 'timeSeries' object. } \usage{ \S4method{t}{timeSeries}(x) } \arguments{ \item{x}{ a 'timeSeries' object. } } \value{ Returns a matrix object. } \examples{ ## Dummy timeSeries with NAs entries data <- matrix(1:24, ncol = 2) s <- timeSeries(data, timeCalendar()) s ## Transpose 'timeSeries' - t(s) } \keyword{chron} timeSeries/man/methods-plot.Rd0000644000176000001440000001766612620124746016120 0ustar ripleyusers\name{plot-methods} \title{Plot a Time Series} \alias{plot} \alias{plot,timeSeries-method} \alias{lines,timeSeries-method} \alias{points,timeSeries-method} \alias{pretty.timeSeries} \description{ Plots 'timeSeries' objects and add lines and points. } \usage{ \S4method{plot}{timeSeries}(x, y, FinCenter = NULL, plot.type = c("multiple", "single"), format = "auto", at = pretty(x), widths = 1, heights = 1, xy.labels, xy.lines, panel = lines, nc, yax.flip = FALSE, mar.multi = c(0, 5.1, 0, if (yax.flip) 5.1 else 2.1), oma.multi = c(6, 0, 5, 0), axes = TRUE, \ldots) \S4method{lines}{timeSeries}(x, FinCenter = NULL, \dots) \S4method{points}{timeSeries}(x, FinCenter = NULL, \dots) \method{pretty}{timeSeries}(x, n=5, min.n=n\%/\%3, shrink.sml=0.75, high.u.bias=1.5, u5.bias=0.5+1.5*high.u.bias, eps.correct=0, \dots) } \details{ The original plotting function \code{plot} was build along R's plotting function \code{plot.ts} with an additional argument to tailor the position marks at user defined position specified by the argument \code{at}. We call this style or theme "ts". With Verison R 3.1 we have inroduced two new additionol plotting themes called "pretty" and "chick". They are becoming active when we set \code{at="pretty"} or \code{at="chic"}. Plot style or theme "pretty" is an extension of our original plotting function. Plot style or theme "chic" an implementation along the contributed packages \code{xts} and \code{PerformanceAnalytics} from the Chicago finance group members. "Chicago" gave the name to call the them \code{"chic"}. For both themes, "pretty" and "chic" additional arguments are passed through the \code{\dots} arguments. These are: \tabular{lll}{ \code{Argument:} \tab Default: \tab Description: \cr \code{type} \tab "l" \tab types pf plot \cr \code{col} \tab 1 \tab colors for lines and points \cr \code{pch} \tab 20 \tab plot symbol \cr \code{cex} \tab 1 \tab character and symbol scales \cr \code{lty} \tab 1 \tab line types \cr \code{lwd} \tab 2 \tab line widths \cr \code{cex.axes} \tab 1 \tab scale of axes \cr \code{cex.lab} \tab 1 \tab scale of labels \cr \code{cex.pch} \tab 1 \tab scale of plot symbols \cr \code{} \tab \tab \cr \code{grid} \tab TRUE \tab should grid lines plotted? \cr \code{frame.plot} \tab TRUE \tab should b box around the plot? \cr \code{axes} \tab TRUE \tab should be axes drawn on the plot? \cr \code{ann} \tab TRUE \tab should default annotations appear? } Concerning the plot elements, the length of these vectors has to be the same as the number of columns in the time series to be plotted. If their length is only one, then tey are repeated. There is an almost 70 pages vignette added to the package, with dozens of examples of tailored plots. Have a look in it. } \arguments{ \item{x, y }{ objects of class \code{timeSeries}. } \item{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{plot.type}{ for multivariate time series, should the series by plotted separately (with a common time axis) or on a single plot? } \item{format}{POSIX label format, e.g. "\%Y-\%m-\%d" or "\%F" for ISO-8601 standard date format. } \item{at}{ a \code{timeDate} object setting the plot label positions. If \code{at=pretty(x)}, the postitions are generated automatized calling the function \code{pretty}. Default option \code{at="auto"} selects 6 equal spaced time label positions. For the new plot themes set at="pretty" or at="chic". In this case additional arguments can be passed through the \code{\dots} arguments, see details. } \item{widths, heights}{ widths and heights for individual graphs, see \code{layout}. } \item{xy.labels}{ logical, indicating if \code{text()} labels should be used for an x-y plot, \_or\_ character, supplying a vector of labels to be used. The default is to label for up to 150 points, and not for more. } \item{xy.lines}{ logical, indicating if \code{lines} should be drawn for an x-y plot. Defaults to the value of \code{xy.labels} if that is logical, otherwise to \code{TRUE} } \item{panel}{ a \code{function(x, col, bg, pch, type, ...)} which gives the action to be carried out in each panel of the display for \code{plot.type="multiple"}. The default is \code{lines}. } \item{nc}{ the number of columns to use when \code{type="multiple"}. Defaults to 1 for up to 4 series, otherwise to 2. } \item{yax.flip}{ logical indicating if the y-axis (ticks and numbering) should flip from side 2 (left) to 4 (right) from series to series when \code{type="multiple"}. } \item{mar.multi, oma.multi}{ the (default) \code{par} settings for \code{plot.type="multiple"}. } \item{axes}{ logical indicating if x- and y- axes should be drawn. } \item{n}{ an integer giving the desired number of intervals. } \item{min.n}{ a nonnegative integer giving the minimal number of intervals. } \item{shrink.sml}{ a positive numeric by a which a default scale is shrunk in the case when range(x) is very small. } \item{high.u.bias}{ a non-negative numeric, typically > 1. Larger high.u.bias values favor larger units. } \item{u5.bias}{ a non-negative numeric multiplier favoring factor 5 over 2. } \item{eps.correct}{ an integer code, one of {0,1,2}. If non-0, a correction is made at the boundaries. } \item{\dots}{ additional graphical arguments, see \code{plot}, \code{plot.default} and \code{par}. } } \value{ Displays a plot or plot elements of an object of class 'timeSeries'. } \examples{ ## Load Swiss Pension Fund Benchmark Data - LPP <- LPP2005REC[1:12, 1:4] colnames(LPP) <- abbreviate(colnames(LPP), 2) finCenter(LPP) <- "GMT" ## Example Plot 1 - plot(LPP[, 1], type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") plot(LPP[, 1], at="auto", type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") ## Example Plot 2 - plot(LPP[, 1:2], type = "o", col = "steelblue", main = "LPP", xlab = "2005", ylab = "Return") ## Example Plot 3 - plot(LPP[, 1], LPP[, 2], type = "p", col = "steelblue", main = "LPP", xlab = "Return 1", ylab = "Return 2") ## Example Plot 4a, The Wrong Way to do it! - LPP <- as.timeSeries(data(LPP2005REC)) ZRH <- as.timeSeries(LPP[,"SPI"], zone = "Zurich", FinCenter = "Zurich") NYC <- as.timeSeries(LPP[,"LMI"], zone = "NewYork", FinCenter = "NewYork") finCenter(ZRH) finCenter(NYC) plot(ZRH, at="auto", type = "p", pch = 19, col = "blue") points(NYC, pch = 19, col = "red") ## Example Plot 4b, Convert NYC to Zurich Time - finCenter(ZRH) <- "Zurich" finCenter(NYC) <- "Zurich" at <- unique(round(time(ZRH))) plot(ZRH, type = "p", pch = 19, col = "blue", format = "\%b \%d", at = at, xlab = paste(ZRH@FinCenter, "local Time"), main = ZRH@FinCenter) points(NYC, pch = 19, col = "red") ## Example 4c, Force Everything to GMT Using "FinCenter" Argument - finCenter(ZRH) <- "Zurich" finCenter(NYC) <- "NewYork" at <- unique(round(time(ZRH))) plot(ZRH, type = "p", pch = 19, col = "blue", format = "\%b \%d", at = at, FinCenter = "GMT", xlab = "GMT", main = "ZRH - GMT") points(NYC, FinCenter = "GMT", pch = 19, col = "red") } \keyword{chron} timeSeries/man/fin-returns.Rd0000644000176000001440000000420212620124746015733 0ustar ripleyusers\name{returns} \title{Financial Returns} \alias{returns} \alias{returns,ANY-method} \alias{returns,timeSeries-method} \alias{returns0} \alias{returnSeries} \alias{getReturns} \description{ Compute financial returns from prices or indexes. } \usage{ returns(x, \dots) returns0(x, \dots) \S4method{returns}{ANY}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, \dots) \S4method{returns}{timeSeries}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, na.rm = TRUE, trim = TRUE, \dots) getReturns(\dots) returnSeries(\dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \item{method}{ a character string. Which method should be used to compute the returns, "continuous", "discrete", or "compound", "simple". The second pair of methods is a synonyme for the first two methods. } \item{na.rm}{ a logical value. Should NAs be removed? By Default \code{TRUE}. } \item{trim}{ a logical value. Should the time series be trimmed? By Default \code{TRUE}. } \item{\dots}{ arguments to be passed. } } \value{ all functions return an object of class \code{timeSeries}. \code{returns0} returns am untrimmed series with the first row of returns set to zero(s). } \note{ The functions \code{returnSeries}, \code{getReturns}, are synonymes for the function \code{returns}. We do not recommend to use these functions. } \examples{ ## Load Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) X = MSFT[1:10, 1:4] X ## Continuous Returns - returns(X) returns0(X) ## Discrete Returns: returns(X, method = "discrete") ## Don't trim: returns(X, trim = FALSE) ## Use Percentage Values: returns(X, percentage = TRUE, trim = FALSE) } \keyword{chron} timeSeries/man/methods-base.Rd0000644000176000001440000000057212620124746016040 0ustar ripleyusers\name{base-methods} \title{Methods for 'timeSeries' object} \docType{methods} \alias{mean,timeSeries-method} \alias{summary,timeSeries-method} \description{ Methods for function in Package `base' for \code{timeSeries} object. } \section{Methods}{ \describe{ \item{x = "timeSeries"}{a \code{timeSeries} object.} }} \examples{ ## None - } \keyword{chron} timeSeries/man/methods-mathOps.Rd0000644000176000001440000000711112620124746016535 0ustar ripleyusers\name{math} \alias{math} \alias{Ops,vector,timeSeries-method} \alias{Ops,array,timeSeries-method} \alias{Ops,ts,timeSeries-method} \alias{Ops,timeSeries,vector-method} \alias{Ops,timeSeries,array-method} \alias{Ops,timeSeries,ts-method} \alias{Ops,timeSeries,timeSeries-method} \alias{-,timeSeries,missing-method} \alias{+,timeSeries,missing-method} \alias{cummax,timeSeries-method} \alias{cummin,timeSeries-method} \alias{cumprod,timeSeries-method} \alias{cumsum,timeSeries-method} \alias{Math,timeSeries-method} \alias{Math2,timeSeries-method} \alias{Summary,timeSeries-method} \alias{trunc,timeSeries-method} \alias{log,timeSeries-method} \alias{\%*\%,timeSeries,vector-method} \alias{\%*\%,timeSeries,ANY-method} \alias{\%*\%,ANY,timeSeries-method} %\alias{diff,timeSeries-method} %\alias{scale,timeSeries-method} \alias{quantile,timeSeries-method} \alias{diff.timeSeries} %\alias{scale.timeSeries} \alias{quantile.timeSeries} \title{Mathematical Time Series Operations} \description{ Functions and methods dealing with mathematical 'timeSeries' operations. } \details{ The math functions include:\cr \tabular{ll}{ \code{Ops-method} \tab Group 'Ops' methods for a 'timeSeries' object \cr \code{Math-method} \tab Group 'Math' methods for a 'timeSeries' object \cr \code{Math2-method} \tab Group 'Math2' methods for a 'timeSeries' object \cr \code{Summary-method} \tab Group 'Summary' methods for a 'timeSeries' object \cr %\code{diff} \tab Differences a 'timeSeries' object, \cr %\code{scale} \tab Centers and/or scales a 'timeSeries' object, \cr \code{quantile} \tab Returns quantiles of an univariate 'timeSeries'. } } \usage{ % \S4method{Ops}{timeSeries}(e1, e2) % \S4method{Math}{timeSeries}(x, ...) % \S4method{Math2}{timeSeries}(x, digits) % \S4method{Summary}{timeSeries}(x, ..., na.rm = FALSE) % \S4method{diff}{timeSeries}(x, lag = 1, diff = 1, trim = FALSE, pad = NA, \dots) % \S4method{scale}{timeSeries}(x, center = TRUE, scale = TRUE) \S4method{quantile}{timeSeries}(x, \dots) } \arguments{ % \item{center, scale}{ % [scale] - \cr % either a logical value or a numeric vector of length equal to % the number of columns of \code{x}. % } % \item{diff}{ % an integer indicating the order of the difference. By default 1. % } % \item{digits} { % number of digits to be used in 'round' or 'signif'. % } % \item{e1, e2}{ % [Ops] - \cr % two objects of class \code{timeSeries}. % } % \item{lag}{ % an integer indicating which lag to use. By default 1. % } % \item{na.rm}{ % logical: should missing values be removed? % } % \item{pad}{ % [diffSeries] - \cr % which value should get the padded values? By default \code{NA}. % Another choice often used would be zero. % } % \item{trim}{ % a logical value. By default \code{TRUE}, the first missing % observation in the return series will be removed. % } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments to be passed. } } \value{ Returns the value from a mathematical or logical operation operating on objects of class 'timeSeries[], or the value computed by a mathematical function. } \examples{ ## Create an Artificial timeSeries Object - setRmetricsOptions(myFinCenter = "GMT") charvec = timeCalendar() set.seed(4711) data = matrix(exp(cumsum(rnorm(12, sd = 0.1)))) TS = timeSeries(data, charvec, units = "TS") TS ## Mathematical Operations: | +/- * ^ ... - TS^2 TS[2:4] OR = returns(TS) OR OR > 0 } \keyword{chron} \keyword{methods} timeSeries/man/timeSeries-isUnivariate.Rd0000644000176000001440000000212112620124746020227 0ustar ripleyusers\name{isUnivariate} \title{Checks if a Time Series is Univariate} \alias{isUnivariate} \alias{isMultivariate} \description{ Checks if a time series o bject or any other rectangular object is univariate or multivariate. } \usage{ isUnivariate(x) isMultivariate(x) } \arguments{ \item{x}{ an object of class \code{timeSeries} or any other rectangular object. } } \value{ \code{isUnivariate}\cr \code{isMultivariate}\cr \cr return a logical depending if the test is true or not. } \details{ A rectangular object \code{x} is considered to be univariate if the function \code{NCOL(x)} returns one, and is considered to be multivariate if \code{NCOL(x)} returns a value bigger than one. } \examples{ ## Load Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) Open = MSFT[, "Open"] ## Is the timeSeries Univariate - isUnivariate(MSFT) isUnivariate(Open) ## Is the timeSeries Multivariate - isMultivariate(MSFT) isMultivariate(Open) } \keyword{chron} timeSeries/man/timeSeries.Rd0000644000176000001440000001320512620124746015573 0ustar ripleyusers\name{TimeSeriesClass} \title{timeSeries Class} \alias{TimeSeriesClass} \alias{timeSeries} \alias{timeSeries,ANY,ANY-method} \alias{timeSeries,ANY,missing-method} \alias{timeSeries,ANY,timeDate-method} \alias{timeSeries,matrix,ANY-method} \alias{timeSeries,matrix,missing-method} \alias{timeSeries,matrix,timeDate-method} \alias{timeSeries,missing,ANY-method} \alias{timeSeries,missing,missing-method} \alias{timeSeries,missing,timeDate-method} \alias{timeSeries,matrix,numeric-method} \alias{index_timeSeries} \alias{time_timeSeries} \alias{timeSeries-class} \alias{index_timeSeries-class} \alias{time_timeSeries-class} \alias{initialize,timeSeries-method} \alias{seriesData} \description{ Functions to generate and modify 'timeSeries' objects: \tabular{ll}{ \code{timeSeries} \tab Creates a 'timeSeries' object from scratch.} Data Slot and classification of 'timeSeries' objects: \tabular{ll}{ \code{seriesData} \tab Extracts data slot from a 'timeSeries'. } } \usage{ timeSeries(data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, \dots) seriesData(object) } \arguments{ \item{charvec}{ a character vector of dates and times or any objects which can be coerced to a \code{timeDate} object. } \item{data}{ a \code{matrix} object or any objects which can be coereced to a matrix. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{format}{ the format specification of the input character vector, \cr [as.timeSeries] - \cr a character string with the format in POSIX notation to be passed to the time series object. } \item{object}{ [is][seriesData][seriesPositions][show][summary] - an object of class \code{timeSeries}. } \item{recordIDs}{ a data frame which can be used for record identification information. \cr [print] - \cr a logical value. Should the \code{recordIDs} printed together with the data matrix and time series positions? } \item{title}{ an optional title string, if not specified the inputs data name is deparsed. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{\dots}{ arguments passed to other methods. } } \value{ \code{timeSeries}\cr returns a S4 object of class \code{timeSeries}. \cr \code{seriesData}\cr \cr extracts the \code{@.Data} slot from a \code{timeSeries} object and is equivalent to \code{as.amtrix}. } \details{ \bold{Generation of Time Series Objects:} \cr We have defined a \code{timeSeries} class which is in many aspects similar to the S-Plus class with the same name, but has also some important differences. The class has seven Slots, the 'Data' slot which holds the time series data in matrix form, the 'position' slot which holds the time/date as a character vector, the 'format' and 'FinCenter' slots which are the same as for the 'timeDate' object, the 'units' slot which holds the column names of the data matrix, and a 'title' and a 'documentation' slot which hold descriptive character strings. Date and time is managed in the same way as for \code{timeDate} objects. } \note{ These functions were written for Rmetrics users using R and Rmetrics under Microsoft's Windows operating system where timze zones, daylight saving times and holiday calendars are insuffeciently supported. } \examples{ ## Load Microsoft Data - # Microsoft Data: setRmetricsOptions(myFinCenter = "GMT") data(MSFT) head(MSFT) ## Create a timeSeries Object, The Direct Way ... Close <- MSFT[, 5] head(Close) ## Create a timeSeries Object from Scratch - data <- as.matrix(MSFT[, 4]) charvec <- rownames(MSFT) Close <- timeSeries(data, charvec, units = "Close") head(Close) c(start(Close), end(Close)) ## Cut out April Data from 2001 - tsApril01 <- window(Close, "2001-04-01", "2001-04-30") tsApril01 ## Compute Continuous Returns - returns(tsApril01) ## Compute Discrete Returns - returns(tsApril01, type = "discrete") ## Compute Discrete Returns, Don't trim - returns(tsApril01, trim = FALSE) ## Compute Discrete Returns, Use Percentage Values - tsRet <- returns(tsApril01, percentage = TRUE, trim = FALSE) tsRet ## Aggregate Weekly - GoodFriday(2001) to <- timeSequence(from = "2001-04-11", length.out = 3, by = "week") from <- to - 6*24*3600 from to applySeries(tsRet, from, to, FUN = sum) ## Create large timeSeries objects with different 'charvec' object classes - # charvec is a 'timeDate' object head(timeSeries(1:1e6L, timeSequence(length.out = 1e6L, by = "sec"))) head(timeSeries(1:1e6L, seq(Sys.timeDate(), length.out = 1e6L, by = "sec"))) # 'charvec' is a 'POSIXt' object head(timeSeries(1:1e6L, seq(Sys.time(), length.out = 1e6L, by = "sec"))) # 'charvec' is a 'numeric' object head(timeSeries(1:1e6L, 1:1e6L)) } \keyword{chron} timeSeries/man/fin-wealth.Rd0000644000176000001440000000117712620124746015525 0ustar ripleyusers\name{wealth} \title{Conversion of an index to wealth} \alias{index2wealth} \description{ Converts an index series to a wealth series normalizing the starting value to one. } \usage{ index2wealth(x) } \arguments{ \item{x}{ an object of class 'timeSeries'. } } \value{ returns a time series object of the same class as the input argument \code{x} normalizing the starting value to one. } \examples{ ## Load MSFT Open Prices - INDEX <- MSFT[1:20, 1] INDEX ## Compute Wealth Normalized to 100 - 100 * index2wealth(INDEX) } \keyword{chron} timeSeries/man/base-start.Rd0000644000176000001440000000155712620124746015536 0ustar ripleyusers\name{start} \alias{start,timeSeries-method} \alias{start.timeSeries} \alias{end,timeSeries-method} \alias{end.timeSeries} \title{Start and End of a 'timeSeries'} \description{ Returns start and/or end time stamps of a 'timeSeries' object. } \usage{ \S4method{start}{timeSeries}(x, \dots) \S4method{end}{timeSeries}(x, \dots) } \arguments{ \item{x}{ an uni- or multivariate \code{timeSeries} object. } \item{\dots}{ optional arguments passed to other methods. } } \value{ returns a \code{timeSeries} object. } \examples{ ## Create Dummy timeSeries - tS <- dummySeries()[, 1] tS ## Return Start and end Time Stamp - c(start(tS), end(tS)) range(time(tS)) } \keyword{chron} timeSeries/man/fin-monthly.Rd0000644000176000001440000000601312620124746015725 0ustar ripleyusers\name{monthly} \alias{monthly} \alias{countMonthlyRecords} \alias{rollMonthlyWindows} \alias{rollMonthlySeries} \title{Special Monthly Series} \description{ Functions and methods dealing with special monthly 'timeSeries' objects. } \details{ The function \code{countMonthlyRecords} computes a 'timeSeries' that holds the number of monthly counts of the records. The function \code{rollMonthlyWindows} computes start and end dates for rolling time windows. The function \code{rollMonthlySeries} computes a static over rolling periods defined by the function \code{rollMonthlyWindows}. } \usage{ countMonthlyRecords(x) rollMonthlyWindows(x, period = "12m", by = "1m") rollMonthlySeries(x, period = "12m", by = "1m", FUN, \dots) } \arguments{ \item{x}{ a 'timeSeries' object. } \item{period}{ a character string specifying the rollling period composed by the length of the period and its unit. As examples: \code{"3m"} represents quarterly shifts, and \code{"6m"}, ]code{"12m"}, and \code{"24m"} semi-annual, annual and bi-annual shifts. To determine the proper start of the series is in the responsibility of the user. } \item{by}{ a character string specifying the rolling shift composed by the length of the shift and its unit. As examples: \code{"1m"} represents monthly shifts, \code{"3m"} represents quarterly shifts, and \code{"6m"} semi-annual shifts. To determine the proper start of the series is in the responsibility of the user. } \item{FUN}{ the function for the statistic to be applied. For example in the case of aggregation use\code{colAvgs}. } \item{\dots}{ arguments passed to the function \code{FUN}. } } \value{ The function \code{countMonthlyRecords} returns a 'timeSeries' object. The function \code{rollMonthlyWindows} returns a list with two named 'tomeDate' entries: \code{$from} and \code{to}. An attribute \code{"control"} is added which keeps the \code{start} and \code{end} dates of the series. The function \code{rollMonthlySeries} computes the statistics defined by the function \code{FUN} over a rolling window internally computed by the function \code{rollMonthlyWindows}. Note, the periods may be overlapping, may be dense, or even may have gaps. } \examples{ ## Load Microsoft Daily Data Set: x <- MSFT ## Count Monthly Records - counts <- countMonthlyRecords(x) counts ## Quaterly Non-Overlapping Time Periods - windows <- rollMonthlyWindows(counts[-1, ], period = "3m", by = "3m") windows ## Nicely Reprint Results as a data.frame - data.frame(cbind(FROM=format(windows$from), TO=format(windows$to))) ## Compute the average number of monthly trading days per quarter - rollMonthlySeries(counts[-1, ], period = "3m", by = "3m", FUN=mean) } \keyword{chron} timeSeries/man/methods-stats.Rd0000644000176000001440000000404412620124746016262 0ustar ripleyusers\name{timeSeries-method-stats} \docType{methods} \alias{sd-methods} \alias{var-methods} \alias{cov-methods} \alias{cor-methods} \alias{dcauchy-methods} \alias{dnorm-methods} \alias{dt-methods} \alias{sd,timeSeries-method} \alias{var,timeSeries-method} \alias{cov,timeSeries-method} \alias{cor,timeSeries-method} \alias{dcauchy,timeSeries-method} \alias{dnorm,timeSeries-method} \alias{dt,timeSeries-method} \title{Time Series Correlations} \description{ S4 methods of stats package for \code{timeSeries} objects. \cr \tabular{ll}{ \code{cov} \tab Computes Covariance from a 'timeSeries' object, \cr \code{cor} \tab Computes Correlations from a 'timeSeries' object. \cr \code{dcauchy} \tab ... \cr \code{dnorm} \tab ... \cr \code{dt} \tab ... \cr } } \usage{ \S4method{cov}{timeSeries}(x, y = NULL, use = "all.obs", method = c("pearson", "kendall", "spearman")) \S4method{cor}{timeSeries}(x, y = NULL, use = "all.obs", method = c("pearson", "kendall", "spearman")) } \arguments{ \item{method}{ a character string indicating which correlation coefficient (or covariance) is to be computed. One of \code{"pearson"} (default), \code{"kendall"}, or \code{"spearman"}, can be abbreviated. } \item{use}{ an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings \code{"all.obs"}, \code{"complete.obs"} or \code{"pairwise.complete.obs"}. } \item{x}{ an univariate object of class \code{timeSeries}. } \item{y}{ NULL (default) or a \code{timeSeries} object with compatible dimensions to \code{x}. The default is equivalent to y = x (but more efficient). } } \value{ returns the covariance or correlation matrix. } \examples{ ## Load Microsoft Data Set - data(MSFT) X = MSFT[, 1:4] X = 100 * returns(X) ## Compute Covariance Matrix - cov(X[, "Open"], X[, "Close"]) cov(X) } \keyword{methods} \keyword{chron} timeSeries/man/methods-is.Rd0000644000176000001440000000160012620124746015532 0ustar ripleyusers\name{is.timeSeries} \title{timeSeries Class, Coercion and Transformation} \alias{is.timeSeries} \alias{is.signalSeries} \alias{is.na,timeSeries-method} \alias{is.unsorted,timeSeries-method} \description{ \code{is.timeSeries} tests if its argument is a \code{timeSeries}. \code{is.timeSeries} tests if series has no timestamps. } \usage{ is.timeSeries(x) is.signalSeries(x) } \arguments{ \item{x}{ an object of class 'timeSeries'. } } \value{ Returns \code{TRUE} or \code{FALSE} depending on whether its argument is an object of class 'timeSeries' or not. } \examples{ ## Create an Artificial timeSeries Object - setRmetricsOptions(myFinCenter = "GMT") charvec <- timeCalendar() data <- matrix(rnorm(12)) TS <- timeSeries(data, charvec, units = "RAND") TS ## Test for timeSeries - is.timeSeries(TS) } \keyword{chron} timeSeries/man/statistics-orderStatistics.Rd0000644000176000001440000000131212620124746021034 0ustar ripleyusers\name{orderStatistics} \title{order Statistics} \alias{orderStatistics} \description{ Computes order statistic of a 'timeSeries'. } \usage{ orderStatistics(x) } \arguments{ \item{x}{ an univariate 'timeSeries' object. } } \value{ Function \code{orderStatistics} returns the order statistic of an univariate 'timeSeries' object. The output is an object of class 'list'. } \examples{ ## Load Swiss Pension Fund Benchmark Data - setRmetricsOptions(myFinCenter = "GMT") X <- LPP2005REC[, "SPI"] colnames(X) ## Compute 1\% Order Statistics - N <- round(0.01*nrow(X)) N OS <- orderStatistics(X)[[1]] OS[1:N, ] } \keyword{chron} timeSeries/man/stats-filter.Rd0000644000176000001440000000105112620124746016077 0ustar ripleyusers \name{filter} \title{Linear Filtering on a Time Series} \alias{filter,timeSeries-method} \description{ Applies linear filtering to a univariate 'timeSeries'. } \value{ A 'timeSeries' object without missing values. } \examples{ ## Creata a Dummy Signal 'timeSeries' - data <- matrix(rnorm(100), ncol = 2) s <- timeSeries(data, units=c("A", "B")) head(s) ## Filter the series - f <- filter(s, rep(1, 3)) head(f) ## Plot and Compare the first series - plot(cbind(s[, 1], f[, 1]), plot.type="s") } timeSeries/man/base-apply.Rd0000644000176000001440000001121712620124746015520 0ustar ripleyusers\name{apply} \title{Apply Functions Over Time Series Periods} \alias{fapply} \alias{applySeries} \alias{apply,timeSeries-method} \description{ Applies a function to a 'timeSeries' object over time peridos of arbitrary positons and lengths. } \usage{ fapply(x, from, to, FUN, \dots) 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, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{from, to}{ starting date and end date as timeDate objects. Note, \code{to} must be time ordered after \code{from}. If \code{from} and \code{to} are missing in function \code{fapply} they are set by default to \code{from=start(x)}, and \code{to=end(x)}. } \item{FUN}{ the function to be applied. For the function \code{applySeries} the default setting is \code{FUN=colMeans}. } \item{by}{ a character value either \code{"monthly"} or \code{"quarterly"} used in the function \code{applySeries}. The default value is \code{"monthly"}. Only operative when both arguments \code{from} and \code{to} have their default values \code{NULL}. In this case the function \code{FUN} will be applied to monthly or quarterly periods. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{format}{ the format specification of the input character vector in POSIX notation. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{FinCenter}{ a character value with the the location of the financial center named as "continent/city", or "city". } \item{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. } \item{title}{ an optional title string, if not specified the inputs data name is deparsed. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{\dots}{ arguments passed to other methods. } } \details{ Like \code{apply} applies a function to the margins of an array, the function \code{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 \code{'timeSeries'}. The function \code{fapply} inputs a \code{timeSeries} object, and if \code{from} and \code{to} are missing, they take the start and end time stamps of the series as default falues. The function then behaves like \code{apply} on the column margin. Note, the function \code{fapply} can be used repetitive in the following sense: If \code{from} and \code{to} are two \code{timeDate} vectors of equal length then for each period spanned by the elelemts of the two vectors the function \code{FUN} will be applied to each period. The resulting time stamps, are the time stamps of the \code{to} vector. Note, the periods can be regular or irregelar, and they can even overlap. The function \code{fapply} calls the more general function \code{applySeries} which also offers, to create automatical monthly and quarterly periods. } \examples{ ## 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) ## Count Trading Days per Month - colCounts <- function(x) rep(NROW(x), times = NCOL(x)) applySeries(LPP, FUN = colCounts, by = "monthly") ## Alternative Use - fapply(LPP, from, to, FUN = colSums) } \keyword{chron} timeSeries/man/fin-spreads.Rd0000644000176000001440000000336112620124746015677 0ustar ripleyusers\name{spreads} \title{Spreads and Mid Quotes} \alias{spreads} \alias{midquotes} \alias{spreadSeries} \alias{midquoteSeries} \description{ Compute spreads and midquotes from price streams. } \usage{ spreads(x, which = c("Bid", "Ask"), tickSize = NULL) midquotes(x, which = c("Bid", "Ask")) midquoteSeries(\dots) spreadSeries(\dots) } \arguments{ \item{tickSize}{ the default is NULL to simply compute price changes in original price levels. If ticksize is supplied, the price changes will be divided by the value of \code{inTicksOfSize} to compute price changes in ticks. } \item{which}{ a vector with two character strings naming the column names of the time series from which to compute the mid quotes and spreads. By default these are bid and ask prices with column names \code{c("Bid", "Ask")}. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments to be passed. } } \value{ all functions return an object of class \code{timeSeries}. } \note{ The functions \code{returnSeries}, \code{getReturns}, \code{midquoteSeries}, \code{spreadSeries} are synonymes for \code{returns}, \code{midquotes}, and \code{spreads}. } \examples{ ## Load the Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") data(MSFT) X = MSFT[1:10, ] head(X) ## Compute Open/Close Midquotes - X.MID <- midquotes(X, which = c("Close", "Open")) colnames(X.MID) <- "X.MID" X.MID ## Compute Open/Close Spreads - X.SPREAD <- spreads(X, which = c("Close", "Open")) colnames(X.SPREAD) <- "X.SPREAD" X.SPREAD } \keyword{chron} timeSeries/man/stats-lag.Rd0000644000176000001440000000234012620124746015357 0ustar ripleyusers\name{lag} \title{Lag a Time Series} \alias{lag} \alias{lag,timeSeries-method} \alias{lag.timeSeries} \description{ Compute a lagged version of a 'timeSeries' object. } \usage{ \S4method{lag}{timeSeries}(x, k = 1, trim = FALSE, units = NULL, \dots) } \arguments{ \item{k}{ [lagSeries] - \cr an integer value. The number of lags (in units of observations). By default 1. } \item{trim}{ a logical value. By default \code{TRUE}, the first missing observation in the return series will be removed. } \item{units}{ an optional character string, which allows to overwrite the current column names of a \code{timeSeries} object. By default \code{NULL} which means that the column names are selected automatically. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments passed to other methods. } } \value{ returns a lagged S4 object of class 'timeSeries'. } \examples{ ## Load Micsrosoft Data Set - x = MSFT[1:20, "Open"] ## Lag the timeSeries Object: lag(x, k = -1:1) } \keyword{chron} timeSeries/man/stats-window.Rd0000644000176000001440000000062112620124746016123 0ustar ripleyusers\name{window} \title{window} \alias{window} \description{ Extracts a part from a 'timeSeries Object } \examples{ ## Load LPP Benchmark Returns - x <- LPP2005REC[, 7:9] range(time(x)) ## Extract Data for January 2006 - window(x, "2006-01-01", "2006-01-31") } \keyword{chron} timeSeries/man/utils-description.Rd0000644000176000001440000000042612620124746017144 0ustar ripleyusers\name{description} \alias{description} \title{Creates Date and User Information} \description{ Creates and returns a data and user string. } \usage{ description() } \examples{ ## Show Default Description String - description() } \keyword{programming} timeSeries/man/utils-structure.Rd0000644000176000001440000000126512620124746016663 0ustar ripleyusers\name{str-methods} \title{timeSeries Object Structure} \alias{str} \alias{str,timeSeries-method} \description{ Compactly display the structure of a 'timeSeries' Object. } \usage{ \S4method{str}{timeSeries}(object, \dots) } \arguments{ \item{object}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments passed to other methods. } } \value{ returns a str report for an object of class \code{timeSeries}. } \examples{ ## Load Microsoft Data Set - data(MSFT) X <- MSFT[1:12, 1:4] colnames(X) <- abbreviate(colnames(X), 4) ## Display Structure - str(X) } \keyword{chron} timeSeries/man/base-cbind.Rd0000644000176000001440000000375612620124746015463 0ustar ripleyusers\name{bind} \title{Bind two timeSeries objects} %\alias{merge} \alias{cbind} \alias{rbind} \alias{cbind2} \alias{rbind2} % \alias{cbind,timeSeries-method} % \alias{rbind,timeSeries-method} \alias{cbind.timeSeries} \alias{rbind.timeSeries} % \alias{c.timeSeries} %\alias{merge,ANY,timeSeries-method} %\alias{merge,timeSeries,ANY-method} %\alias{merge,timeSeries,missing-method} %\alias{merge,timeSeries,numeric-method} %\alias{merge,timeSeries,matrix-method} %\alias{merge,timeSeries,timeSeries-method} %\alias{merge,matrix,timeSeries-method} %\alias{merge,numeric,timeSeries-method} %\alias{merge.timeSeries} \alias{cbind2,ANY,timeSeries-method} \alias{cbind2,timeSeries,ANY-method} \alias{cbind2,timeSeries,missing-method} \alias{cbind2,timeSeries,timeSeries-method} \alias{rbind2,ANY,timeSeries-method} \alias{rbind2,timeSeries,ANY-method} \alias{rbind2,timeSeries,missing-method} \alias{rbind2,timeSeries,timeSeries-method} \description{ Binds two 'timeSeries' objects either by column or by row. } % \usage{ % \S4method{merge}{timeSeries,timeSeries}(x, y, ...) % \S4method{cbind2}{timeSeries,timeSeries}(x, y) % \S4method{rbind2}{timeSeries,timeSeries}(x, y) % \S4method{cbind2}{timeSeries,timeSeries}(x, y) % } % \arguments{ % % \item{units}{ % % an optional character string, which allows to overwrite the % % current column names of a \code{timeSeries} object. By default % % \code{NULL} which means that the column names are selected % % automatically. % % } % \item{x, y}{ % two objects of class \code{timeSeries}. % } % % \item{\dots}{ % % arguments passed to returned timeSeries object. % % } % } \value{ returns a S4 object of class \code{timeDate}. } \examples{ ## Load Microsoft Data Set - x <- MSFT[1:12, ] x ## Bind Columnwise - X <- cbind(x[, "Open"], returns(x[, "Open"])) colnames(X) <- c("Open", "Return") X ## Bind Rowwise - Y <- rbind(x[1:3, "Open"], x[10:12, "Open"]) Y } \keyword{chron} timeSeries/man/statistics-colCumsums.Rd0000644000176000001440000000314612620124746020007 0ustar ripleyusers\name{colCum} \title{Cumulated Column Statistics} \alias{colCum} \alias{colCummaxs} \alias{colCummins} \alias{colCumprods} \alias{colCumreturns} \alias{colCumsums} \alias{colCummaxs,matrix-method} \alias{colCummaxs,timeSeries-method} \alias{colCummins,matrix-method} \alias{colCummins,timeSeries-method} \alias{colCumprods,matrix-method} \alias{colCumprods,timeSeries-method} \alias{colCumreturns,matrix-method} \alias{colCumreturns,timeSeries-method} \alias{colCumsums,matrix-method} \alias{colCumsums,timeSeries-method} \description{ Functions to compute cumulative column statistics. } \usage{ \S4method{colCumsums}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCummaxs}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCummins}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCumprods}{timeSeries}(x, na.rm = FALSE, \dots) \S4method{colCumreturns}{timeSeries}(x, method = c("geometric", "simple"), na.rm = FALSE, \dots) } \arguments{ \item{method}{ a character string to indicate if geometric (TRUE) or simple (FALSE) returns should be computed. } \item{na.rm}{ a logical. Should missing values be removed? } \item{x}{ a time series, may be an object of class \code{"matrix"}, or \code{"timeSeries"}. } \item{\dots}{ arguments to be passed. } } \value{ all functions return an S4 object of class \code{timeSeries}. } \examples{ ## Simulated Return Data - x = matrix(rnorm(24), ncol = 2) ## Cumulative Sums Column by Column - colCumsums(x) } \keyword{univar} timeSeries/man/fin-turnpoints.Rd0000644000176000001440000000704412620124746016465 0ustar ripleyusers\name{turns} \title{Turning Points of a Time Series} \alias{turns} \alias{turnsStats} \description{ Extracts and analyzes turn points of an univariate \code{timeSeries} object. } \usage{ turns(x, \dots) turnsStats(x, doplot = TRUE) } \arguments{ \item{x}{ an univariate 'timeSeries' object of financial indices or prices. } \item{\dots}{ optional arguments passed to the function \code{na.omit}. } \item{doplot}{ a logical flag, should the results be plotted? By default TRUE. } } \details{ The function \code{turns} determines the number and the position of extrema (turning points, either peaks or pits) in a regular time series. The function \code{turnsStats} calculates the quantity of information associated to the observations in this series, according to Kendall's information theory. The functions are borrowed from the contributed R package \code{pastecs} and made ready for working together with univariate \code{timeSeries} objects. You need not to load the R package \code{pastecs}, the code parts we need here are builtin in the \code{timeSeries} package. We have renamed the function \code{turnpoints} to \code{turns} to distinguish between the original function in the contributed R package \code{pastecs} and our Rmetrics function wrapper. For further details please consult the help page from the contributed R package \code{pastecs}. } \value{ \code{turns}\cr returns an object of class \code{timeSeries}. \code{turnsStats}\cr returns an object of class \code{turnpoints} with the following entries:\cr \code{data} - The dataset to which the calculation is done.\cr \code{n} - The number of observations.\cr \code{points} - The value of the points in the series, after elimination of ex-aequos.\cr \code{pos} - The position of the points on the time scale in the series (including ex-aequos).\cr \code{exaequos} - Location of exaequos (1), or not (0).\cr \code{nturns} - Total number of tunring points in the whole time series.\cr \code{firstispeak} - Is the first turning point a peak (TRUE), or not (FALSE).\cr \code{peaks} - Logical vector. Location of the peaks in the time series without ex-aequos.\cr \code{pits} - Logical vector. Location of the pits in the time series without ex-aequos.\cr \code{tppos} - Position of the turning points in the initial series (with ex-aequos).\cr \code{proba} - Probability to find a turning point at this location.\cr \code{info} - Quantity of information associated with this point.\cr } \author{ Frederic Ibanez and Philippe Grosjean for code from the contributed R package \code{pastecs} and Rmetrics for the function wrapper. } \references{ Ibanez, F., 1982, Sur une nouvelle application de la theorie de l'information a la description des series chronologiques planctoniques. J. Exp. Mar. Biol. Ecol., 4, 619--632 Kendall, M.G., 1976, Time Series, 2nd ed. Charles Griffin and Co, London. } \examples{ ## Load Swiss Equities Series - SPI.RET <- LPP2005REC[, "SPI"] head(SPI.RET) ## Cumulate and Smooth the Series - SPI <- smoothLowess(cumulated(SPI.RET), f=0.05) plot(SPI) ## Plot Turn Points Series - SPI.SMOOTH <- SPI[, 2] tP <- turns(SPI.SMOOTH) plot(tP) ## Compute Statistics - turnsStats(SPI.SMOOTH) } \keyword{chron} timeSeries/man/timeSeries-slotTime.Rd0000644000176000001440000000215112620124746017367 0ustar ripleyusers\name{time} \title{Get and Set Time stamps of a 'timeSeries'} \alias{time} \alias{time<-} \alias{time,timeSeries-method} \alias{time.timeSeries} \alias{time<-.timeSeries} \alias{sample,timeSeries-method} \alias{getTime} \alias{setTime<-} \description{ Functions and methods extracting and modifying positions of 'timeSeries' objects. } \usage{ getTime(x) setTime(x) <- value \S4method{time}{timeSeries}(x, \dots) \method{time}{timeSeries}(x) <- value } \arguments{ \item{value}{ a valid value for the component of \code{time(x)}. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ optional arguments passed to other methods. } } \value{ Returns a 'timeDate' object. } \examples{ ## Create Dummy timeSeries - X <- timeSeries(matrix(rnorm(24), 12), timeCalendar()) ## Return Series Positions - getTime(X) time(X) ## Add / Subtract one Day from X setTime(X) <- time(X) - 24*3600 # sec X time(X) <- time(X) + 24*3600 # sec X } \keyword{chron} timeSeries/man/stats-na.omit.Rd0000644000176000001440000001214112620124746016161 0ustar ripleyusers\name{na} \title{Handling Missing Time Series Values} \alias{na} \alias{na.omit} \alias{na.omit,timeSeries-method} \alias{na.omit.timeSeries} \alias{removeNA} \alias{substituteNA} \alias{interpNA} \description{ Functions for handling missing values in 'timeSeries' objects } \details{ Functions for handling missing values in 'timeSeries' objects and in objects which can be transformed into a vector or a two dimensional matrix. \cr The functions are listed by topic. \cr \tabular{ll}{ \code{na.omit} \tab Handles NAs, \cr \code{removeNA} \tab Removes NAs from a matrix object, \cr \code{substituteNA} \tab substitute NAs by zero, the column mean or median, \cr \code{interpNA} \tab interpolates NAs using R's "approx" function. } \bold{Missing Values in Price and Index Series:} Applied to \code{timeSeries} objects the function \code{removeNA} just removes rows with NAs from the series. For an interpolation of time series points one can use the function \code{interpNA}. Three different methods of interpolation are offered: \code{"linear"} does a linear interpolation, \code{"before"} uses the previous value, and \code{"after"} uses the following value. Note, that the interpolation is done on the index scale and not on the time scale. \bold{Missing Values in Return Series:} For return series the function \code{substituteNA} may be useful. The function allows to fill missing values either by \code{method="zeros"}, the \code{method="mean"} or the \code{method="median"} value of the appropriate columns. } \usage{ \S4method{na.omit}{timeSeries}(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), \dots) removeNA(x, \dots) substituteNA(x, type = c("zeros", "mean", "median"), \dots) interpNA(x, method = c("linear", "before", "after"), \dots) } \arguments{ \item{interp, type}{ [nna.omit][substituteNA] - \cr Three alternative methods are provided to remove NAs from the data: \code{type="zeros"} replaces the missing values by zeros, \code{type="mean"} replaces the missing values by the column mean, \code{type="median"} replaces the missing values by the the column median. } \item{method}{ [na.omit] - \cr Specifies the method how to handle NAs. One of the applied vector strings: \cr \code{method="s"} na.rm = FALSE, skip, i.e. do nothing, \code{method="r"} remove NAs, \code{method="z"} substitute NAs by zeros, \code{method="ir"} interpolate NAs and remove NAs at the beginning and end of the series, \code{method="iz"} interpolate NAs and substitute NAs at the beginning and end of the series, \code{method="ie"} interpolate NAs and extrapolate NAs at the beginning and end of the series, [interpNA] - \cr Specifies the method how to interpolate the matrix column by column. One of the applied vector strings: \code{method="linear"}, \code{method="before"} or \code{method="after"}.\cr For the interpolation the function \code{approx} is used. } \item{object}{ an object of class("timeSeries"). } \item{x}{ a numeric matrix, or any other object which can be transformed into a matrix through \code{x = as.matrix(x, ...)}. If \code{x} is a vector, it will be transformed into a one-dimensional matrix. } \item{\dots}{ arguments to be passed to the function \code{as.matrix}. } } \note{ The functions \code{removeNA}, \code{substituteNA} and \code{interpNA} are older implementations. Please use in all cases if possible the new function \code{na.omit}. When dealing with daily data sets, there exists another function \code{alignDaily Series} which can handle missing data in un-aligned calendarical 'timeSeries' objects. } \references{ Troyanskaya O., Cantor M., Sherlock G., Brown P., Hastie T., Tibshirani R., Botstein D., Altman R.B., (2001); \emph{Missing Value Estimation Methods for DNA microarrays} Bioinformatics 17, 520--525. } \examples{ ## Create a Matrix - X <- matrix(rnorm(100), ncol = 5) ## Replace a Single NA Inside - X[3, 5] <- NA ## Replace Three in a Row Inside - X[17, 2:4] <- c(NA, NA, NA) ## Replace Three in a Column Inside - X[13:15, 4] <- c(NA, NA, NA) ## Replace Two at the Right Border - X[11:12, 5] <- c(NA, NA) ## Replace One in the Lower Left Corner - X[20, 1] <- NA print(X) ## Remove Rows with NAs - removeNA(X) ## Subsitute NA's by Zeros or Column Means - substituteNA(X, type = "zeros") substituteNA(X, type = "mean") ## Interpolate NA's Linearily - interpNA(X, method = "linear") # Note the corner missing value cannot be interpolated! ## Take Previous Values in a Column - interpNA(X, method = "before") # Also here, the corner value is excluded } \keyword{math} timeSeries/man/fin-cumulated.Rd0000644000176000001440000000317512620124746016224 0ustar ripleyusers\name{cumulated} \title{Cumulated Time Series from Returns} \alias{cumulated} \alias{cumulated.default} \description{ Computes a cumulated financial 'timeSeries', e.g. prices or indexes, from financial returns. } \usage{ cumulated(x, \dots) \method{cumulated}{default}(x, method = c("continuous", "discrete", "compound", "simple"), percentage = FALSE, \dots) } \arguments{ \item{method}{ a character string naming the method how the returns were computed. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments to be passed. } } \details{ Note, the function \code{cumulated} assumes as input discrete returns from a price or index series. Only then the cumulatrd series agrees with the original price or index series. The first values of the cumulated series cannot be computed, it is assumed that the series is indexed to 1. } \value{ Returns a 'timeSeries' object of the same class as the input argument \code{x}. } \examples{ ## Use the Microsofts' Close Prices Indexed to 1 - MSFT.CL <- MSFT[, "Close"] MSFT.CL <- MSFT.CL/MSFT[[1, "Close"]] head(MSFT.CL) ## Compute Discrete Return - MSFT.RET <- returns(MSFT.CL, method = "discrete") ## Cumulated Series and Compare - MSFT.CUM <- cumulated(MSFT.RET, method = "discrete") head(cbind(MSFT.CL, MSFT.CUM)) } \keyword{chron} timeSeries/man/statistics-smoothLowess.Rd0000644000176000001440000000503612620124746020363 0ustar ripleyusers\name{smooth} \title{Smoothes Time Series Objects} \alias{smoothLowess} \alias{smoothSpline} \alias{smoothSupsmu} \description{ Smoothes a 'timeSeries' object. } \usage{ smoothLowess(x, f = 0.5, \dots) smoothSpline(x, spar = NULL, \dots) smoothSupsmu(x, bass = 5, \dots) } \arguments{ \item{x}{ an univariate 'timeSeries' object. } \item{f}{ the lowess smoother span. This gives the proportion of points in the plot which influence the smooth at each value. Larger values give more smoothness. } \item{spar}{ smoothing parameter, typically (but not necessarily) in (0,1]. By default \code{NULL}, i.e. the value will be automatically selected. } \item{bass}{ controls the smoothness of the fitted curve. Values of up to 10 indicate increasing smoothness. } \item{\dots}{ optional arguments to be passed to the underlying smoothers. } } \details{ The functions \code{smoothLowess}, \code{smoothSpline}, \code{smoothSupsmu} allow to smooth \code{timeSerie} object. The are interfaces to the function \code{lowess}, \code{supmsu}. and \code{smooth.spline} in R's \code{stats} package. The \code{\dots} arguments allow to pass optional arguments to the underlying \code{stats} functions and tailor the smoothing process. We refer to the manual pages of these functions for a proper setting of these options. } \value{ returns a bivariate 'timeSeries' object, the first column holds the original time series data, the second the smoothed series. } \author{ The R core team for the underlying smoother functions. } \examples{ ## Use Close from MSFT's Price Series - head(MSFT) MSFT.CLOSE <- MSFT[, "Close"] head(MSFT.CLOSE) ## Plot Original and Smoothed Series by Lowess - MSFT.LOWESS <- smoothLowess(MSFT.CLOSE, f = 0.1) head(MSFT.LOWESS) plot(MSFT.LOWESS) title(main = "Close - Lowess Smoothed") ## Plot Original and Smoothed Series by Splines - MSFT.SPLINE <- smoothSpline(MSFT.CLOSE, spar = 0.4) head(MSFT.SPLINE) plot(MSFT.SPLINE) title(main = "Close - Spline Smoothed") ## Plot Original and Smoothed Series by Supsmu - MSFT.SUPSMU <- smoothSupsmu(MSFT.CLOSE) head(MSFT.SUPSMU) plot(MSFT.SUPSMU) title(main = "Close - Spline Smoothed") } \keyword{chron} timeSeries/man/base-merge.Rd0000644000176000001440000000247012620124746015473 0ustar ripleyusers\name{merge} \title{Merges two 'timeSeries' objects} \alias{merge} \alias{merge,ANY,timeSeries-method} \alias{merge,timeSeries,ANY-method} \alias{merge,timeSeries,missing-method} \alias{merge,timeSeries,numeric-method} \alias{merge,timeSeries,matrix-method} \alias{merge,timeSeries,timeSeries-method} \alias{merge,matrix,timeSeries-method} \alias{merge,numeric,timeSeries-method} \description{ Merges several object types with 'timeSeries' objects. The number of rows must match. } \value{ Returns a 'timeSeries' object of two merged time series. } \details{ The following combinations are supported: \tabular{ll}{ \code{timeSeries} \tab ANY \cr \code{timeSeries} \tab missing \cr \code{timeSeries} \tab numeric \cr \code{timeSeries} \tab matrix \cr \code{timeSeries} \tab timeSeries } } \examples{ ## Load Series - x <- MSFT[1:12, ] ## Merge 'timeSeries' with missing Object - merge(x) ## Merge 'timeSeries' with numeric Object - y <- rnorm(12) class(y) merge(x, y) ## Merge 'timeSeries' with matrix Object - y <- matrix(rnorm(24), ncol=2) class(y) merge(x, y) ## Merge 'timeSeries' with matrix Object - y <- timeSeries(data=rnorm(12), charvec=time(x)) class(y) merge(x, y) } \keyword{chron} timeSeries/man/fin-runlengths.Rd0000644000176000001440000000121712620124746016425 0ustar ripleyusers\name{runlengths} \title{Runlengths of a Time Series} \alias{runlengths} \description{ Computes runlengths of an univariate 'timeSeries' object. } \usage{ runlengths(x, \dots) } \arguments{ \item{x}{ an univariate time series of class 'timeSeries'. } \item{\dots}{ arguments to be passed. } } \value{ returns an object of class \code{timeSeries}. } \examples{ ## random time series - set.seed(4711) x <- rnorm(12) tS <- timeSeries(data=x, charvec=timeCalendar(), units="x") tS ## return runlengths - runlengths(tS) } \keyword{chron} timeSeries/man/data-examples.Rd0000644000176000001440000000137512620124746016214 0ustar ripleyusers\name{TimeSeriesData} \alias{TimeSeriesData} \alias{LPP2005REC} \alias{MSFT} \alias{USDCHF} \title{Time Series Data Sets} \description{ Three data sets used in example files. The data sets are: \tabular{ll}{ \code{LPP2005REC} \tab Swiss pension fund assets returns benchmark, \cr \code{MSFT} \tab Daily Microsoft OHLC prices and volume, \cr \code{USDCHF} \tab USD CHF intraday foreign exchange xchange rates.} } \examples{ ## Plot LPP2005 Example Data Set - data(LPP2005REC) plot(LPP2005REC, type = "l") ## Plot MSFT Example Data Set - data(MSFT) plot(MSFT[, 1:4], type = "l") plot(MSFT[, 5], type = "h") ## Plot USDCHF Example Data Set - # plot(USDCHF) } \keyword{datasets} timeSeries/man/00timeSeries-package.Rd0000644000176000001440000002243012620124746017324 0ustar ripleyusers\name{timeSeries-package} \alias{timeSeries-package} \docType{package} \title{Utilities and Tools Package} \description{ Package of time series tools and utilities. } \details{ \tabular{ll}{ Package: \tab timeSeries\cr Type: \tab Package\cr Version: \tab see description file\cr Date: \tab 2011\cr License: \tab GPL Version 2 or later\cr Copyright: \tab (c) 1999-2014 Rmetrics Association\cr URL: \tab \url{http://www.rmetrics.org} } } \section{timeSeries - S4 timeSeries Class}{ \tabular{ll}{ \code{timeSeries} \tab Creates a 'timeSeries' from scratch\cr \code{getDataPart, series} \tab ... \cr \code{getUnits} \tab Extracts the time serie units \cr \code{getTime, time} \tab Extracts the positions of timestamps \cr \code{use: slot} \tab Extracts the format of the timestamp \cr \code{getFinCenter, finCenter} \tab Extracts the financial center \cr \code{use: slot} \tab Extracts the record IDs \cr \code{getTitle} \tab Extracts the title \cr \code{use: slot} \tab Extracts the documentation } } \section{Base Time Series Functions}{ \tabular{ll}{ \code{apply} \tab Applies a function to blocks of a 'timeSeries' \cr \code{attach} \tab Attaches a 'timeSeries' to the search path \cr \code{cbind} \tab Combines columns of two 'timeSeries' objects \cr \code{ rbind} \tab Combines rows of two 'timeSeries' objects \cr %\code{comment} \tab ? ... \cr \code{diff} \tab Returns differences of a 'timeSeries' object \cr \code{dim} \tab returns dimensions of a 'timeSeries' object \cr \code{merge} \tab Merges two 'timeSeries' objects \cr \code{rank} \tab Returns sample ranks of a 'timeSeries' object \cr \code{rev} \tab Reverts a 'timeSeries' object \cr \code{sample} \tab Resamples a 'timeSeries' object \cr \code{scale} \tab Scales a 'timeSeries' object \cr \code{sort} \tab Sorts a 'timeSeries' object \cr \code{start} \tab Returns start date/time of a 'timeSeries' \cr \code{ end} \tab Returns end date/time of a 'timeSeries' \cr \code{t} \tab Returns the transpose of a 'timeSeries' object } } \section{Subsetting 'timeSeries' Objects}{ \tabular{ll}{ \code{.subset_} \tab Subsets 'timeSeries' objects \cr \code{.findIndex} \tab Index search in a 'timeSeries' object \cr \code{[} \tab Subsets a 'timeSeries' object \cr \code{[<-]} \tab Assigns values to a subset \cr \code{$} \tab Subsets a 'timeSeries' by column names \cr \code{$<-} \tab Replaces Subset by column names \cr \code{t} \tab Returns the transpose of a 'timeSeries' \cr \code{head} \tab Returns the head of a 'timeSeries' \cr \code{ tail} \tab Returns the tail of a time Series \cr \code{na.omit} \tab Handles NAs in a timeSeries object \cr \code{ removeNA} \tab removes NAs from a matrix object \cr \code{ substituteNA} \tab substitutes NAs by zero, column mean or median \cr \code{ interpNA} \tab interpolates NAs using R's "approx" function } } \section{Mathematical Operation}{ \tabular{ll}{ \code{Ops.timeSeries} \tab S4: Arith method for a 'timeSeries' object \cr \code{abs} \tab Returns absolute values of a 'timeSeries' object \cr \code{sqrt} \tab Returns square root of a 'timeSeries' object \cr \code{exp} \tab Returns the exponential values of a 'timeSeries' object \cr \code{log} \tab Returns the logarithm of a 'timeSeries' object \cr \code{sign} \tab Returns the signs of a 'timeSeries' object \cr \code{diff} \tab Differences a 'timeSeries' object \cr \code{scale} \tab Centers and/or scales a 'timeSeries' object \cr \code{quantile} \tab Returns quantiles of an univariate 'timeSeries'} } \section{Methods}{ \tabular{ll}{ \code{as.timeSeries} \tab Defines method for a 'timeSeries' \cr \code{as.*.default} \tab Returns the input \cr \code{as.*.ts} \tab Transforma a 'ts' object into a 'timeSeries' \cr \code{as.*.data.frame} \tab Transforms a 'data.frame' intp a 'timeSeries \cr \code{as.*.character} \tab Loads and transforms from a demo file \cr \code{as.*.zoo} \tab Transforms a 'zoo' object into a 'timeSeries' \cr \code{as.vector.*} \tab Converts univariate timeSeries to vector \cr \code{as.matrix.*} \tab Converts timeSeries to matrix \cr \code{as.numeric.*} \tab Converts timeSeries to numeric \cr \code{as.data.frame.*} \tab Converts timeSeries to data.frame \cr \code{as.ts.*} \tab Converts timeSeries to ts \cr \code{as.logical.*} \tab Converts timeSeries to logical \cr %\code{comment} \tab ? ... \cr \code{is.timeSeries} \tab Tests for a 'timeSeries' object \cr \code{plot} \tab Displays a X-Y 'timeSeries' Plot \cr \code{lines} \tab Adds connected line segments to a plot \cr \code{points} \tab Adds Points to a plot \cr \code{show} \tab Prints a 'timeSeries oobject} } \section{Financial time series functions}{ \tabular{ll}{ \code{align} \tab Aligns a 'timeSeries' to time stamps \cr \code{cumulated} \tab Computes cumulated series from a returns \cr \code{alignDailySeries} \tab Aligns a 'timeSeries' to calendarical dates \cr \code{ rollDailySeries} \tab Rolls a 'timeSeries daily\cr \code{drawdowns} \tab Computes series of drawdowns from financial returns \cr \code{ drawdownsStats} \tab Computes drawdowns statistics \cr \code{durations} \tab Computes durations from a financial time series \cr \code{countMonthlyRecords} \tab Counts monthly records in a 'timeSeries' \cr \code{ rollMonthlyWindows} \tab Rolls Monthly windows \cr \code{ rollMonthlySeries} \tab Rolls a 'timeSeries' monthly \cr \code{endOfPeriodSeries} \tab Returns end of periodical series \cr \code{ endOfPeriodStats} \tab Returns end of period statistics \cr \code{ endOfPeriodBenchmarks} \tab Returns period benchmarks \cr \code{returns} \tab Computes returns from prices or indexes \cr \code{ returns0} \tab Computes untrimmed returns from prices or indexes \cr \code{runlengths} \tab Computes run lenghts of a 'timeSeries' \cr \code{smooth} \tab Smoothes a 'timeSeries' \cr \code{splits} \tab Detects 'timeSeries' splits by outlier detection \cr \code{spreads} \tab Computes spreads from a price/index stream \cr \code{turns} \tab Computes turning points in a 'timeSeries' object \cr \code{ turnsStats} \tab Computes turning points statistics } } \section{Statistics Time Series functions}{ \tabular{ll}{ \code{colCumsums} \tab Computes cumulated column sums of a 'timeSeries' \cr \code{ colCummaxs} \tab Computes cumulated maximum of a 'timeSeries' \cr \code{ colCummins} \tab Computes cumulated minimum of a 'timeSeries' \cr \code{ colCumprods} \tab Computes cumulated pruduct values by column \cr \code{ colCumreturns} \tab Computes cumulated returns by column \cr \code{colSums} \tab Computes sums of all values in each column \cr \code{ colMeans} \tab Computes means of all values in each column \cr \code{ colSds} \tab Computes standard deviations of all values in each column \cr \code{ colVars} \tab Computes variances of all values in each column \cr \code{ colSkewness} \tab Computes skewness of all values in each column \cr \code{ colKurtosis} \tab Computes kurtosis of all values in each column \cr \code{ colMaxs} \tab Computes maxima of all values in each column \cr \code{ colMins} \tab Computes minima of all values in each column \cr \code{ colProds} \tab Computes products of all values in each column \cr \code{ colStats} \tab Computes statistics of all values in each column \cr \code{orderColnames} \tab Returns ordered column names of a 'timeSeries' \cr \code{ sortColnames} \tab Returns alphabetically sorted column names \cr \code{ sampleColnames} \tab Returns sampled column names of a 'timeSeries' \cr \code{ pcaColnames} \tab Returns PCA correlation ordered column names \cr \code{ hclustColnames} \tab Returns hierarchically clustered columnames \cr \code{ statsColnames} \tab Returns statisticall rearrange columnames \cr \code{orderStatistics} \tab Computes order statistics of a 'timeSeries' object \cr \code{rollMean} \tab Computes rolling means of a 'timeSeries' object \cr \code{ rollMin} \tab Computes rolling minima of a 'timeSeries' object \cr \code{ rollMax} \tab Computes rolling maxima of a 'timeSeries' object \cr \code{ rollMedian} \tab Computes rolling medians of a 'timeSeries' object \cr \code{ rollStats} \tab Computes rolling statistics of a 'timeSeries' objectcr \cr \code{rowCumsums} \tab Computes cumulated column sums of a 'timeSeries' \cr \code{smoothLowess} \tab Smoothes a series with lowess function \cr \code{ smoothSupsmu} \tab Smoothes a series with supsmu function \cr \code{ smoothSpline} \tab Smoothes a series with smooth.spline function } } \section{Misc Functions}{ \tabular{ll}{ \code{dummyDailySeries} \tab Creates a dummy daily 'timeSeries' object \cr \code{isMonthly} \tab Decides if the series consists of monthly records \cr %\code{Description} \tab Creates default description string \cr \code{getArgs} \tab Extracts arguments from a S4 method } } \keyword{package} timeSeries/man/statistics-orderColnames.Rd0000644000176000001440000000765412620124746020462 0ustar ripleyusers\name{orderColnames} \title{Reorder Column Names of a Time Series} \alias{orderColnames} \alias{sortColnames} \alias{sampleColnames} \alias{statsColnames} \alias{pcaColnames} \alias{hclustColnames} \description{ Functions and methods dealing with the rearrangement of column names of 'timeSeries' objects. \cr \tabular{ll}{ \code{orderColnames} \tab Returns ordered column names of a time Series, \cr \code{sortColnames} \tab Returns sorted column names of a time Series, \cr \code{sampleColnames} \tab Returns sampled column names of a time Series, \cr \code{statsColnames} \tab Returns statistically rearranged column names, \cr \code{pcaColnames} \tab Returns PCA correlation ordered column names, \cr \code{hclustColnames} \tab Returns hierarchical clustered column names. } } \usage{ orderColnames(x, \dots) sortColnames(x, \dots) sampleColnames(x, \dots) statsColnames(x, FUN = colMeans, \dots) pcaColnames(x, robust = FALSE, \dots) hclustColnames(x, method = c("euclidean", "complete"), \dots) } \arguments{ \item{FUN}{ a character string indicating which statistical function should be applied. By default statistical ordering operates on the column means of the time series. } \item{method}{ a character string with two elements. The first determines the choice of the distance measure, see \code{dist}, and the second determines the choice of the agglomeration method, see \code{hclust}. } \item{robust}{ a logical flag which indicates if robust correlations should be used. } \item{x}{ an object of class \code{timesSeries} or any other rectangular object which can be transformed by the function \code{as.matrix} into a numeric matrix. } \item{\dots}{ further arguments to be passed, see details. } } \details{ \bold{Statistically Motivated Rearrangement} The function \code{statsColnames} rearranges the column names according to a statical measure. These measure must operate on the columns of the time series and return a vector of values which can be sorted. Typical functions ar those listed in in help page \code{colStats} but one can also crete his own functions which compute for example risk or any other statistical measure. The \code{\dots} argument allows to pass additional arguments to the underlying function \code{FUN}.\cr \bold{PCA Ordering of the Correlation Matrix} The function \code{pcaColnames} rearranges the column names according to the PCA ordered correlation matrix. The argument \code{robust} allsows to select between the use of the standard \code{cor} and computation of robust correlations using the function \code{covMcd} from contributed R package \code{robustbase}. The \code{\dots} argument allows to pass additional arguments to the two underlying functions \code{cor} or \code{covMcd}. E.g. adding \code{method="kendall"} to the argument list calculates Kendall's rank correlations instead the default which calculates Person's correlations.\cr \bold{Ordering by Hierarchical Clustering} The function \code{pcaColnames} uses the hierarchical clustering approach \code{hclust} to rearrange the column names of the time series. } \value{ returns a vector of character string, the rearranged column names. } \examples{ ## Load Swiss Pension Fund Benchmark Data - data <- LPP2005REC[,1:6] ## Abbreviate Column Names - colnames(data) ## Sort Alphabetically - sortColnames(data) ## Sort by Column Names by Hierarchical Clustering - hclustColnames(data) head(data[, hclustColnames(data)]) } \keyword{chron} timeSeries/man/fin-align.Rd0000644000176000001440000000226412620124746015331 0ustar ripleyusers\name{align-methods} \docType{methods} \alias{align} \alias{align-methods} \alias{align,timeSeries-method} \title{timeSeries Class, Functions and Methods} \description{ Aligns a 'timeSeries' Object. } \usage{ \S4method{align}{timeSeries}(x, by = "1d", offset = "0s", method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, ...) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{by}{ a character string denoting the period } \item{offset}{ a character string denoting the offset } \item{method}{ a character string denoting the alignment approach. } \item{include.weekends}{ a logical flag, should weekend be included. } \item{\ldots}{ Further arguments to be passed to the interpolating function. } } \value{ Returns an aligned S4 'timeSeries' object. } \examples{ ## Use MSFT and Compute Sample Size - dim(MSFT) ## Align the Series - MSFT.AL <- align(MSFT) ## Show the Size of the Aligned Series - dim(MSFT.AL) } \keyword{methods} \keyword{chron} timeSeries/man/statistics-colSums.Rd0000644000176000001440000000460212620124746017300 0ustar ripleyusers\name{colStats} \alias{colStats} \alias{colSums,timeSeries-method} \alias{colMeans,timeSeries-method} \alias{colSds} \alias{colVars} \alias{colSkewness} \alias{colKurtosis} \alias{colMaxs} \alias{colMins} \alias{colProds} \alias{colQuantiles} \alias{colAvgs} \alias{colStdevs} % \alias{mean.timeSeries} % \alias{var.timeSeries} \title{Column Statistics} \description{ A collection and description of functions to compute column statistical properties of financial and economic time series data. \cr The functions are: \tabular{ll}{ \code{colStats}\tab calculates column statistics, \cr \code{colSums} \tab calculates column sums, \cr \code{colMeans} \tab calculates column means, \cr \code{colSds} \tab calculates column standard deviations, \cr \code{colVars} \tab calculates column variances, \cr \code{colSkewness} \tab calculates column skewness, \cr \code{colKurtosis} \tab calculates column kurtosis, \cr \code{colMaxs} \tab calculates maximum values in each column, \cr \code{colMins} \tab calculates minimum values in each column, \cr \code{colProds} \tab computes product of all values in each column, \cr \code{colQuantiles} \tab computes quantiles of each column. } } \usage{ colStats(x, FUN, \dots) colSds(x, \dots) colVars(x, \dots) colSkewness(x, \dots) colKurtosis(x, \dots) colMaxs(x, \dots) colMins(x, \dots) colProds(x, \dots) colQuantiles(x, prob = 0.05, \dots) colStdevs(x, \dots) colAvgs(x, \dots) % \method{mean}{timeSeries}(x, \dots) % \method{var}{timeSeries}(x, \dots) } \arguments{ \item{FUN}{ a function name. The statistical function to be applied. } \item{prob}{ a numeric value, the probability with value in [0,1]. } \item{x}{ a rectangular object which can be transformed into a matrix by the function \code{as.matrix}. } \item{\dots}{ arguments to be passed. } } \value{ the functions return a numeric vector of the statistics. } \seealso{ \code{link{rowStats}}. } \examples{ ## Simulated Return Data in Matrix Form - x = matrix(rnorm(252), ncol = 2) ## Mean Columnwise Statistics - colStats(x, FUN = mean) ## Quantiles Column by Column - colQuantiles(x, prob = 0.10, type = 1) } \keyword{univar} timeSeries/man/statistics-rowCumsums.Rd0000644000176000001440000000155312620124746020041 0ustar ripleyusers\name{rowCum} \title{Cumulated Column Statistics} \alias{rowCum} \alias{rowCumsums} \alias{rowCumsums,ANY-method} \alias{rowCumsums,timeSeries-method} \description{ Compute cumulative row Statistics. } \usage{ \S4method{rowCumsums}{ANY}(x, na.rm = FALSE, \dots) \S4method{rowCumsums}{timeSeries}(x, na.rm = FALSE, \dots) } \arguments{ \item{na.rm}{ a logical. Should missing values be removed? } \item{x}{ a time series, may be an object of class \code{"matrix"} or \code{"timeSeries"}. } \item{\dots}{ arguments to be passed. } } \value{ all functions return an S4 object of class \code{timeSeries}. } \examples{ ## Simulated Monthly Return Data - X = matrix(rnorm(24), ncol = 2) ## Compute cumulated Sums - rowCumsums(X) } \keyword{univar} timeSeries/man/fin-drawdowns.Rd0000644000176000001440000000413112620124746016242 0ustar ripleyusers\name{drawdowns} \title{Calculations of Drawdowns} \alias{drawdowns} \alias{drawdownsStats} \description{ Compute series of drawdowns from financial returns and calculate drawdown statisitcs. } \usage{ drawdowns(x, \dots) drawdownsStats(x, \dots) } \arguments{ \item{x}{ a 'timeSeries' object of financial returns. Note, drawdowns can be calculated from an uni- or multivariate time deries object, statistics can only be computed from an univariate time series object. } \item{\dots}{ optional arguments passed to the function \code{na.omit}. } } \value{ \code{drawdowns}\cr returns an object of class 'timeSeries'. \code{drawdownsStats}\cr returns an object of class 'data.frame' with the following entries:\cr \code{"drawdown"} - the depth of the drawdown, \cr \code{"from"} - the start date, \cr \code{"trough"} - the trough period, \cr \code{"to"} - the end date, \cr \code{"length"} - the length in number of records, \cr \code{"peaktrough"} - the peak trough, and , \cr \code{"recovery"} - the recovery length in number of records. } \details{ The code in the core of the function \code{drawdownsStats} was was borrowed from the package \code{PerformanceAnalytics} authored by Peter Carl and Sankalp Upadhyay. } \author{ Peter Carl and Sankalp Upadhyay for code from the contributed R package \code{PerformanceAnalytics} used in the function \code{drawdownsStats}. } \examples{ ## Use Swiss Pension Fund Data Set of Returns - head(LPP2005REC) SPI <- LPP2005REC[, "SPI"] head(SPI) ## Plot Drawdowns - dd = drawdowns(LPP2005REC[, "SPI"], main = "Drawdowns") plot(dd) dd = drawdowns(LPP2005REC[, 1:6], main = "Drawdowns") plot(dd) ## Compute Drawdowns Statistics - ddStats <- drawdownsStats(SPI) class(ddStats) ddStats ## Note, Only Univariate Series are allowd - ddStats <- try(drawdownsStats(LPP2005REC)) class(ddStats) } \keyword{chron} timeSeries/man/methods-comment.Rd0000644000176000001440000000123212620124746016562 0ustar ripleyusers\name{comment} \alias{comment,timeSeries-method} \alias{comment<-,timeSeries-method} \title{comment for timeSeries objects} \description{ Print or assign new comment to a \code{timeSeries} object. } \usage{ \S4method{comment}{timeSeries}(x) \S4method{comment}{timeSeries}(x) <- value } \arguments{ \item{x}{ a \code{timeSeries} object. } \item{value}{ a character string - the comment. } } \examples{ ## Get Description from timeSeries - comment(LPP2005REC) ## Add User to comment - comment(LPP2005REC) <- paste(comment(LPP2005REC), "by User Rmetrics") comment(LPP2005REC) } \keyword{chron} timeSeries/man/stats-model.frame.Rd0000644000176000001440000000310712620124746017007 0ustar ripleyusers\name{model.frame} \title{Model Frames for Time Series Objects} \alias{model.frame} \alias{model.frame.default,ANY,timeSeries-method} \description{ Allow to work with model frames for 'timeSeries' objects. } % \usage{ % model.frame(formula, data, ...) % } % \arguments{ % \item{formula}{ % a model formula object. % } % \item{data}{ % an object of class \code{timeSeries}. % } % \item{\dots}{ % arguments passed to the function \code{stats::model.frame}. % } % } \value{ Returns an object of class 'timeSeries. } \details{ The function \code{model.frame} is a generic function which returns in the R-ststs framework by default a data.frame with the variables needed to use \code{formula} and any \code{...} arguments. In contrast to this the method returns an object of class \code{timeSeries} when the argument data was not a \code{data.frame} but also an object of class 'timeSeries'. } \note{ This function is preliminary and untested. } \seealso{ \code{\link{model.frame}}. } \examples{ ## Load Microsoft Data - setRmetricsOptions(myFinCenter = "GMT") X <- MSFT[1:12, ] ## Extract High's and Low's: DATA <- model.frame( ~ High + Low, data = X) class(DATA) as.timeSeries(DATA) ## Extract Open Prices and their log10's: base <- 10 Open <- model.frame(Open ~ log(Open, base = `base`), data = X) colnames(Open) <- c("X", "log10(X)") class(Open) as.timeSeries(Open) } \keyword{chron} timeSeries/man/timeSeries-slotFinCenter.Rd0000644000176000001440000000166112620124746020353 0ustar ripleyusers\name{finCenter} \title{Get and Set Financial Center of a 'timeSeries'} \alias{getFinCenter} \alias{setFinCenter<-} \alias{finCenter,timeSeries-method} \alias{finCenter<-,timeSeries-method} \description{ Print or assign new financial center to a 'timeSeries' object. } \usage{ getFinCenter(x) setFinCenter(x) <- value \S4method{finCenter}{timeSeries}(x) \S4method{finCenter}{timeSeries}(x) <- value } \arguments{ \item{x}{ a 'timeSeries' object. } \item{value}{ a character with the the location of the financial center named as "continent/city". } } \seealso{listFinCenter} \examples{ ## An artificial timeSeries Object - tS <- dummySeries() tS ## Print Financial Center - finCenter(tS) getFinCenter(tS) ## Assign New Financial Center - finCenter(tS) <- "Zurich" tS setFinCenter(tS) <- "New_York" tS } \keyword{programming}