timeSeries/0000755000176200001440000000000014673766373012411 5ustar liggesuserstimeSeries/tests/0000755000176200001440000000000014263246022013526 5ustar liggesuserstimeSeries/tests/doRUnit.R0000644000176200001440000000164214263246022015240 0ustar liggesusers#### 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.csv0000644000176200001440000003146414263246022015773 0ustar liggesusers"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 "6";"2000-10-04";56.375;56.5625;54.5;55.4375;68226700 "7";"2000-10-05";55.5;57.25;55.25;55.375;40549700 "8";"2000-10-06";55.8125;56.75;54.75;55.5625;30897000 "9";"2000-10-09";55.625;55.75;53;54.1875;29161800 "10";"2000-10-10";53.9375;55.5625;53.8125;54.5625;31033100 "11";"2000-10-11";54;56.9375;54;55.75;50602900 "12";"2000-10-12";56.3125;56.875;53.8125;54.375;45109800 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timeSeries/R/0000755000176200001440000000000014672042711012570 5ustar liggesuserstimeSeries/R/base-merge.R0000644000176200001440000001376214321370314014725 0ustar liggesusers# # 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: # 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, ...)) merge.timeSeries) ## # 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/stats-filter.R0000644000176200001440000000572614263246022015343 0ustar liggesusers# # 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/statistics-smoothLowess.R0000644000176200001440000001507314263246022017614 0ustar liggesusers# # 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/timeSeries.R0000644000176200001440000002551114263246022015025 0ustar liggesusers# # 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/stats-aggregate.R0000644000176200001440000001350714650724114016003 0ustar liggesusers# # 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 ################################################################################ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method 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 (by timedate): if (is.unsorted(x)) x <- sort(x) # Sort and remove duplicated 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 } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## 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/base-applySeries.R0000644000176200001440000002626114321331264016125 0ustar liggesusers# # 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: (now removed) 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 (!inherits(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 ... # 2022-10-07 GNB : apparently not, commenting out # .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 ... ## removed by GNB ## ## .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 (!inherits(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/stats-window.R0000644000176200001440000000756614650724114015374 0ustar liggesusers# # 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 ################################################################################ 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 } ## (2024-01-05) GNB: stop making window() S4 ## setMethod("window", "timeSeries", ## function(x, start, end, ...) .window.timeSeries(x, start, end, ...)) ############################################################################### ## 2023-05-26 removed this cut method ## (it is not compatible with the purpose of the generic function cut) ## ## .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/statistics-orderStatistics.R0000644000176200001440000000301614263246022020266 0ustar liggesusers# # 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$ix], units = nm)}, as.list(x), colnames(x), SIMPLIFY = FALSE) } ################################################################################ timeSeries/R/base-rank.R0000644000176200001440000000377014263246022014562 0ustar liggesusers# # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # A copy of the GNU General Public License is available at # ../../COPYING ################################################################################ # FUNCTION: COLUMN STATISTICS IN FUTILITIES: # rank,timeSeries Returns sample ranks of a 'timeSeries' object ################################################################################ setMethod("rank", "timeSeries", function(x, na.last = TRUE, ties.method = eval(formals(rank)$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/base-attach.R0000644000176200001440000000265614263246022015075 0ustar liggesusers# # 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/AllClass.R0000644000176200001440000001272514263246022014415 0ustar liggesusers# # 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/fin-cumulated.R0000644000176200001440000000573214263246022015454 0ustar liggesusers# # 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/timeSeries-slotUnits.R0000644000176200001440000000344614263246022017032 0ustar liggesusers# # 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/base-dim.R0000644000176200001440000002022414322333342014367 0ustar liggesusers# # 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(dummyMonthlySeries() ~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/fin-periodical.R0000644000176200001440000001442614320521273015602 0ustar liggesusers# # 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 } # ------------------------------------------------------------------------------ ## 2022-10-09 :TODO: GNB: this function is unfinished and returns NULL. ## It should be removed or completed. ## I don't know what it is supposed to do. 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: ## GNB: stats? invisible() } ################################################################################ timeSeries/R/timeSeries-signalCounts.R0000644000176200001440000000267614263246022017503 0ustar liggesusers# # 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/utils-old2new.R0000644000176200001440000000524414263246022015425 0ustar liggesusers# # 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/methods-plot2.R0000644000176200001440000004700414322333453015417 0ustar liggesusers# # 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=dummyMonthlySeries(); 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() # 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) # axes = TRUE # ... <- NULL .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(...) minor.ticks <- dots$minor.ticks %||% "auto" type <- dots$type %||% "l" col <- dots[["col"]] %||% { 1:NCOL(x) } pch <- dots$pch %||% 20 cex <- dots$cex %||% 1 lty <- dots$lty %||% 1 lwd <- dots[["lwd"]] %||% 1 grid <- dots$grid %||% TRUE col.grid <- dots$col.grid %||% "darkgrey" lwd.grid <- dots$lwd.grid %||% 1 frame.plot <- dots$frame.plot %||% TRUE ann <- dots$ann %||% TRUE cex.axis <- dots$cex.axis %||% 1 cex.lab <- dots$cex.lab %||% 1 cex.pch <- dots$cex.pch %||% 1 log <- dots$log %||% "" equilogs <- dots$equilogs %||% TRUE main <- dots$main %||% "" xlab <- dots$xlab %||% "" ylab <- dots$ylab %||% { cn <- colnames(x) if(length(cn) > 1 && (plot.type == "single" || plot.type == "s")) "Values" else cn } xax <- dots[["xax"]] %||% FALSE xaxs <- dots$xaxs %||% "r" yaxs <- dots$yaxs %||% "r" # Continue ... if (minor.ticks == "auto") minor.ticks <- .periodicity2(x)$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" # 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)) if (length(ylab) == 1) ylab <- rep(ylab, times=NCOL(x)) TIME <- time(x) if (is.integer(TIME)) { X <- TIME AT <- "counts" } else { X <- as.POSIXct(TIME) } Y <- series(x) if (AT == "pretty") { at <- pretty(x) } if (AT == "chic" ) { ep <- .axTicksByTime2(x, format=FORMAT) at <- TIME[ep] } # SINGLE PLOT: if (plot.type == "single" || plot.type == "s") { # All curves in one Frame: ylim <- dots$ylim %||% range(Y, na.rm=TRUE) xlim <- dots$xlim # even if it is NULL plot(X, Y[,1], type= "n", xlim = xlim, ylim = ylim, axes = FALSE, main = "", xlab = "", ylab = "", log=log, xaxs=xaxs, yaxs=yaxs) 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 = ylab[1], cex.lab = cex.lab) } if (axes) { # Y - Axis: axis(2, cex.axis = cex.axis) } if (axes || xax) { # 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, 1, 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) } } } if (frame.plot) { box("plot") } if(grid) { if (!(AT %in% c("pretty","chic"))) at <- axTicks(1) abline(v = at, lty = 3, col = col.grid, lwd = lwd.grid) grid(NA, NULL, lty = 3, col = col.grid, lwd = lwd.grid, equilogs=equilogs) } 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 (axes || xax) { if (do.xax) { if (AT == "counts") { axis(1, cex.axis = 1.2 * cex.axis) at <- axTicks(1) } 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, 1, 0), cex.axis = 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 = ylab[i], side = y.side, line = 3, cex = cex.lab) if (do.xax) mtext(xlab, side = 1, line = 3, cex = cex.lab) if (i==1) { cex.main <- if (is.null(dots$cex.lab)) par("cex.main") else cex.lab mtext(main, side = 3, line = 3, cex = cex.main, font = par("font.main"), col = par("col.main")) } } if(grid) { abline(v = at, lty = 3, col = col.grid, lwd = lwd.grid) grid(NA, NULL, lty = 3, col = col.grid, lwd = lwd.grid, equilogs=equilogs) } } # end of nser loop return(invisible()) } # SCATTER PLOT: if (!is.null(y)) { stopifnot (isUnivariate(x)) stopifnot (isUnivariate(y)) plot(series(x), series(y), xlab="", ylab="", col=col, pch=pch, cex=cex) return(invisible()) } } ############################################################################### # Test function for xts-plot-like axis positions and labels. .xtsPlot <- function(x, y=NULL, type = "l", ann = TRUE, axes = TRUE, major.ticks = 'auto', minor.ticks = TRUE, major.format = TRUE, grid = TRUE, box = TRUE, ...) { # A function written by Diethelm Wuertz # Descroption: # A simple example to test the xts functions to generate # nice axis positions and Lebels # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); xtsPlot(x) # Settings: # time.scale <- periodicity2(x)$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$x[ep], labels=names(ep), las=1, lwd=1, mgp=c(3,2,0)) axis(2) } # ADD BOX: box() } # ----------------------------------------------------------------------------- # Borrowed from ... # Package: xts # Title: eXtensible Time Series # Version: 0.9-7 # Date: 2013-06-26 # Author: Jeffrey A. Ryan, Joshua M. Ulrich # Maintainer: Jeffrey A. Ryan # License: GPL (>= 2) # URL: http://r-forge.r-project.org/projects/xts/ # Packaged: 2014-01-02 18:00:13 UTC; ripley # NeedsCompilation: yes # Repository: CRAN # Date/Publication: 2014-01-02 19:18:28 .axTicksByTime2 <- function( x, ticks.on='auto', k=1, labels=TRUE, format.labels=TRUE, ends=TRUE, gt = 2, lt = 30, format = "auto") { # A modified function borrowed from the xts-package # Arguments: # x - a 'timeSerie' Object # Example: # x <- 100 * cumulated(LPP2005REC[, 2]); .axTicksByTime2(x) tick.opts <- c( "years", "months", "weeks", "days", "hours", "minutes", "seconds") tick.k.opts <- c( 10, 5, 2, 1, 6, 1, 1, 1, 4, 2, 1, 30, 15, 1, 1) if (ticks.on %in% tick.opts) { cl <- ticks.on[1] ck <- k } else { tick.opts <- paste(rep(tick.opts, c(4, 2, 1, 1, 3, 3, 1)), tick.k.opts) is <- structure(rep(0,length(tick.opts)), .Names = tick.opts) for(i in 1:length(tick.opts)) { y <- strsplit(tick.opts[i], ' ')[[1]] ep <- .endpoints2(x, y[1], as.numeric(y[2])) is[i] <- length(ep) -1 if(is[i] > lt) break } nms <- rev(names(is)[which(is > gt & is < lt)])[1] cl <- strsplit(nms, " ")[[1]][1] ck <- as.numeric(strsplit(nms, " ")[[1]][2]) } ep <- if (is.null(cl)) NULL else .endpoints2(x, cl, ck) if(ends) ep <- ep + c(rep(1,length(ep)-1),0) if (labels) { if(is.logical(format.labels) || is.character(format.labels)) { # format by level of time detail, and platform unix <- ifelse(.Platform$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) { units <- "days" scale <- "daily" label <- "day" } else if (p <= 604800) { units <- "weeks" scale <- "weekly" label <- "week" } else if (p <= 2678400) { units <- "months" scale <- "monthly" label <- "month" } else if (p <= 7948800) { units <- "quarter" 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/base-diff.R0000644000176200001440000000641114650724114014535 0ustar liggesusers# # 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 } # ----------------------------------------------------------------------------- ## 2024-01-06 GNB: removed the S4 method ## ## 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-na.omit.R0000644000176200001440000002540114650724114015416 0ustar liggesusers# # 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. ################################################################################ ## 2023-05-28 GNB: added argument 'FUN' ## 2024-06-01 GNB: renamed .na.omit.timeSeries to na.omit.timeSeries; ## dropped the S4 method na.omit.timeSeries <- function(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), FUN, ...) { # Description # Handles NAs in timeSeries objects # Details: # 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) if(!missing(FUN)) { # GNB FUN <- match.fun(FUN) data <- object@.Data data <- apply(data, 2, function(z) { z[is.na(z)] = FUN(z, na.rm = TRUE) z }) object@.Data <- data 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"), FUN, ...) ## .na.omit.timeSeries(object, method, interp, FUN, ...)) ## # 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: .Deprecated("na.omit", package = "timeSeries", msg = c("'removeNA' is deprecated.\n", "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB 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: .Deprecated("na.omit", msg = c("'substituteNA' is deprecated.\n", "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB 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: .Deprecated("na.omit", msg = c("'interpNA' is deprecated.\n", "Use 'na.omit' instead.", " See help('na.omit.timeSeries').\n")) # GNB 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/utils-head.R0000644000176200001440000000701114650724114014751 0ustar liggesusers# # 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 ################################################################################ setGeneric("head") setGeneric("tail") 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, ...) } # ------------------------------------------------------------------------------ ##' @title Returns the tail of a 'timeSeries' object ##' ##' @param x a 'timeSeries' object. ##' @param 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. ##' @param recordIDs a logical flag, should the record identification ##' be shown? By default FALSE. ##' @param ... ##' ##' @return ##' Returns the tail of an object of class 'timeSeries'. ##' ## Martin Maechler: if("keepnums" %in% names(formals(tail.matrix))) ## R-devel (2020-01) ## refactored somewhat by GNB; *TODO:* is a similar thing needed for head.timeSeries? tail.timeSeries <- if(getRversion() >= "4.0.0") { function(x, n = 6, recordIDs = FALSE, ...) { if (recordIDs && nrow(x) == nrow(x@recordIDs)) cbind(tail.matrix(x, n = n, keepnums = FALSE, ...), tail(x@recordIDs, n = n, keepnums = FALSE, ...)) else tail.matrix(x, n = n, keepnums = FALSE, ...) } } else { function(x, n = 6, recordIDs = FALSE, ...) { 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, ...) } } ## (2024-01-05) GNB: stop making head() and tail() S4 ## setMethod("head", "timeSeries", head.timeSeries) ## setMethod("tail", "timeSeries", tail.timeSeries) ################################################################################ timeSeries/R/timeSeries-readSeries.R0000644000176200001440000001026014263246022017104 0ustar liggesusers# # 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 == ";" && inherits(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/fin-spreads.R0000644000176200001440000000513414263246022015126 0ustar liggesusers# # 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/utils-description.R0000644000176200001440000000252714263246022016377 0ustar liggesusers # 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-summary.R0000644000176200001440000000604514437130426015325 0ustar liggesusers# # 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 ## Author: Georgi N. Boshnakov ## setMethod("summary", c(object = "timeSeries"), ## function(object, alwaysNA = TRUE, ...){ ## start = as.character(start(object)) ## end = as.character(end(object)) ## ## ## stats <- cbind( ## ## "Min." = colMins(object), ## ## "1st Qu." = colQuantiles(object, prob = 0.25, type = 1), ## ## "Median" = colQuantiles(object, prob = 0.50, type = 1), ## ## "3rd Qu." = colQuantiles(object, prob = 0.75, type = 1), ## ## "Max." = colMaxs(object) ## ## ## , check.names = FALSE ## ## ) ## ## stats <- summary(as.matrix(object)) ## ## attr(stats, "start") <- start ## attr(stats, "end") <- end ## attr(stats, "nobs") <- nrow(object) ## attr(stats, "Format") <- object@format ## attr(stats, "FinCenter") <- object@FinCenter ## ## class(stats) <- c("timeSeries_summary", class("stats")) ## stats ## }) summary.timeSeries <- function(object, ...) { ## stats <- cbind( ## "Min." = colMins(object), ## "1st Qu." = colQuantiles(object, prob = 0.25, type = 1), ## "Median" = colQuantiles(object, prob = 0.50, type = 1), ## "3rd Qu." = colQuantiles(object, prob = 0.75, type = 1), ## "Max." = colMaxs(object) ## ## , check.names = FALSE ## ) stats <- summary(as.matrix(object)) attr(stats, "start") <- as.character(start(object)) attr(stats, "end") <- as.character(end(object)) attr(stats, "nobs") <- nrow(object) attr(stats, "Format") <- object@format attr(stats, "FinCenter") <- object@FinCenter class(stats) <- c("timeSeries_summary", class("stats")) stats } print.timeSeries_summary <- function(x, quote = FALSE, ...) { cat("Start Record:", attr(x, "start") , "\n") cat("End Record: ", attr(x, "end") , "\n") cat("Observations:", attr(x, "nobs") , "\n") cat("Format: ", attr(x, "Format") , "\n") cat("FinCenter: ", attr(x, "FinCenter"), "\n") cat("\n") class(x) <- class(x)[-1] attr(x, "start") <- attr(x, "end") <- attr(x, "nobs") <- attr(x, "Format") <- attr(x, "FinCenter") <- NULL print(x, quote = quote, ...) invisible(x) } timeSeries/R/statistics-rowCumsums.R0000644000176200001440000000330014263246022017260 0ustar liggesusers# # 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/timeSeries-isOHLC.R0000644000176200001440000000432714263246022016106 0ustar liggesusers# # 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-returns.R0000644000176200001440000001144214650724114015171 0ustar liggesusers# # 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(...) ## } # ----------------------------------------------------------------------------- ## TODO: remove when fTrading is updated, see note in file NAMESPACE getReturns <- function(...) { # A function implemented by Diethelm Wuertz # Description: # Computes returns # FUNCTION: # .Deprecated("returns", "timeSeries") # Return Value: returns(...) } ############################################################################### timeSeries/R/zzz.R0000644000176200001440000000201614263246022013544 0ustar liggesusers# # 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. (R -2.9.0) setOldClass("difftime") } ################################################################################ timeSeries/R/methods-show.R0000644000176200001440000001175214650724114015342 0ustar liggesusers# # 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,timeSeries ################################################################################ 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 } ) # ------------------------------------------------------------------------------ ## GNB: streamlined somewhat the calls and removed the S4 method 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/fin-wealth.R0000644000176200001440000000237214263246022014752 0ustar liggesusers# # 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/fin-align.R0000644000176200001440000001077114263246022014562 0ustar liggesusers# # 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/fin-runlengths.R0000644000176200001440000000422014263246022015651 0ustar liggesusers # 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/stats-lag.R0000644000176200001440000000766114650724114014624 0ustar liggesusers# # 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 ################################################################################ ## GNB: made it an S3 method and removed the S4 one ## setMethod("lag" , "timeSeries", 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/timeSeries-isUnivariate.R0000644000176200001440000000314214263246022017462 0ustar liggesusers# # 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/fin-monthly.R0000644000176200001440000001316514263246022015162 0ustar liggesusers# # 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/statistics-rollMean.R0000644000176200001440000003030714263246022016654 0ustar liggesusers # 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/methods-is.R0000644000176200001440000001271014650724114014770 0ustar liggesusers# # 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 object then we have problem # with the function quantile... setMethod("is.na", "timeSeries", function(x) setDataPart(x, is.na(getDataPart(x))) ) ## 2024-01-12 GNB: ## TODO: create method for anyNA? ## The default anyNA works fine (it calls is.na), ## but a timeSeries method might gain some efficiency/ # ------------------------------------------------------------------------------ # 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) #} ## ## 2024-01-11 GNB: the notes below need consolidation, I wrote them as I worked on this. ## ## 1. is.unsorted is internal generic, though I didn't find in NEWS R- x.x notes about it ## changing its status as internal generic. ## ## 2. changed the method to S3 ## setMethod("is.unsorted", "timeSeries", ## function(x, na.rm = FALSE, strictly = FALSE) ## callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) ## ## 3. since is.unsorted is primitive, it may be better to define an S4 method but the ## implicit generic created by setMethod has the wrong signature and creates a new ## function which needs to be exported. So, an explicit setGeneric to limit the ## dispatch only to 'x', see the example above by a previous maintainer. ## I haven't tried that. ## ## 2024-01-12 GNB: ## ## This works: is.unsorted.timeSeries <- function(x, na.rm = FALSE, strictly = FALSE) { is.unsorted(x@positions, na.rm = na.rm, strictly = strictly) } ## (GNB: cont.) ## (in the sense that it is dispatched) but it gives wrong results if there are NA's in ## the data. Indeed we have ## ## > base::is.unsorted ## function (x, na.rm = FALSE, strictly = FALSE) ## { ## if (length(x) <= 1L) ## return(FALSE) ## if (!na.rm && anyNA(x)) ## return(NA) ## if (na.rm && any(ii <- is.na(x))) ## x <- x[!ii] ## .Internal(is.unsorted(x, na.rm, strictly)) ## } ## ## the internal function is internal generic but before calling that, 'base::is.unsorted' ## handles the 'NA' cases. The time series method for is.na() works on the data, and so we ## get wrong result if there are NA's in the time series data. ## ## The S4 method creates a new generic with default 'base::is.unsorted' so there is no ## problem there. ## ## The core problem in my (GNB) opinion is that the timeSeries methods for is.unsorted and ## sort() have different semantics from that of the method for is.na (the former work on ## the time stamps, while the latter works on the data). ## ## So, returning the S4 method. Notice that we do not try to keep is.unsorted internal but ## rather export the generic so that it is calledand the time series method kiks in for ## timeSeries objects and avoids the above NA problem. ## ## Note that we keep also the S3 method, so that if the S4 generic is not visible, the ## result will be correct at least when there are no NA's in the data. setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE, strictly = FALSE) callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) # if (getRversion() < "2.8.0") # { # setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE) # callGeneric(x@positions, na.rm = na.rm)) # } else { # setMethod("is.unsorted", "timeSeries", function(x, na.rm = FALSE, strictly = FALSE) # callGeneric(x@positions, na.rm = na.rm, strictly = strictly)) # } ################################################################################ timeSeries/R/statistics-colSums.R0000644000176200001440000000772714433671346016554 0ustar liggesusers # 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 ################################################################################ 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, ...) } timeSeries/R/fin-drawdowns.R0000644000176200001440000001525014263246022015475 0ustar liggesusers# # 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/methods-as.R0000644000176200001440000002612714672042711014767 0ustar liggesusers# # 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)) # ------------------------------------------------------------------------------ ## 2024-01-11 GNB: streamlined and removed the S4 method 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 = dummyMonthlySeries(); as.ts(x) # # x = timeSeries(seq(12), timeSequence(by = "month", length.out = 12)) # as.ts(x) # # x = dummyMonthlySeries()[c(3,6,9,12),]; as.ts(x) # x = dummyMonthlySeries()[c(2,5,8,11),]; as.ts(x) # x = dummyMonthlySeries()[c(1,4,7,10),]; as.ts(x) # # x = dummyMonthlySeries()[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 <- c(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 } setAs("timeSeries", "ts", function(from) as.ts(from)) ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## 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, ...) # ------------------------------------------------------------------------------ # 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/timeSeries-slotSeries.R0000644000176200001440000001467314434372166017177 0ustar liggesusers# # 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' ################################################################################ # ------------------------------------------------------------------------------ ## 2022-10-08 GNB: ## ## TODO: In principle, we could just not define 'coredata<-' generic and do: ## ## "coredata<-" <- "series<-" ## ## but this doesn't seem desirable since 'coredata<-' may be exported by ## other packages, too (e.g., zoo). Maybe, do it the other way round: ## define the methods for 'coredata<-' and do ## ## "series<-" <- "coredata<-" ## ## This may have the analogous problem since other packages may rely on a ## generic 'series<-'. Admittedly, this is far less likely. ## 2023-05-27 GNB: renaming from .series_assign <- 'coredata<-.timeSeries' <- 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: ## 2023-05-27 GNB: added this when converted the two S4 methods into a ## single S3 one. if(class(value)[1] != "matrix") value <- as.matrix(value) # 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) } # ------------------------------------------------------------------------------ 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"), `coredata<-.timeSeries`) ################################################################################ # COREDATA SYNONYM ## GNB: replacing the S4 generic coredata and its method with an S3 method, ## which is exported and registered directly as a method for zoo::coredata. ## ## The S4 coredata() in 'timeSeries' was not exported although is method ## was 'seen' by 'zoo::coredata' when zoo was attached. I suspect that that ## was by chance, not as a design in the S3/S4 methods handling in R. Of ## course, coredata() was only visible when zoo was attached (or xts which ## exports it). ## 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 ## }) coredata.timeSeries <- function(x) as.matrix(x) # ------------------------------------------------------------------------------ ## GNB: replacing the S4 generic 'coredata<-' and its methods with an S3 method, ## which is exported and registered directly as a method for zoo::coredata<-. ## ## Note that although the S4 methods were seen when zoo was loaded, they ## didn't work properly since they dispatch on two arguments, while the ## function is S3. ## 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"), ## .series_assign ) timeSeries/R/timeSeries-slotFinCenter.R0000644000176200001440000000610714263246022017602 0ustar liggesusers # # 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-plot.R0000644000176200001440000004014514650724114015336 0ustar liggesusers# # 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 ################################################################################ ## 2024-01-11 GNB: streamlined S4/S3 combination ## ## Keeping .plot.timeSeries for now since cached plot methods for timeSeries in fGarch, ## fBasics built with previous version of timeSeries cause test failure in fExtremes ## because .plot.timeSeries is not found! Took me some time to figure this out. ## ## To remove .plot.timeSeries will need to rebuild at least some of the above packages with ## this version of timeSeries. Alternatively, new releases of those packages could require ## this version of timeSeries. Or maybe ask CRAN to rebuild those packages with this ## version of R? Indeed, this is the case. Actually, it seems not necessary to rebuild ## fExtremes (which is good, I don't manage it), it looks like the imported offending ## method comes from fGarch (which defines plot methods and exports them. ## ## To summarize: keep .plot.timeSeries for now, remove it when the above packages are ## updated and/or rebuilt with timeSeries > 4032.108. Preferably, updated versions of those ## packages would require timeSeries > 4032.108. .plot.timeSeries <- 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, ...)) setMethod("plot", "timeSeries", plot.timeSeries) ## # 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) && identical(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()) } # ------------------------------------------------------------------------------ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method 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) } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## 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, ...) # ------------------------------------------------------------------------------ ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method 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) } ## GNB: 2024-01-11 GNB: streamlined and removed the S4 method ## 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-turnpoints.R0000644000176200001440000002363214263246022015715 0ustar liggesusers# # 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/aaa-utils.R0000644000176200001440000000135114263246022014570 0ustar liggesusers## Copyright (C) 2020 Martin Maechler ## ## 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 ## https://www.R-project.org/Licenses/ ## Not my idea .. but really nice : `%||%` <- function (L, R) if (is.null(L)) R else L timeSeries/R/statistics-colCumsums.R0000644000176200001440000001750114270257546017251 0ustar liggesusers# # 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, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ 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, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ 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, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ 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, ...){ ## GB: split to debug #2121; was: ## setDataPart(x, callGeneric(getDataPart(x), na.rm = na.rm, ...)) ## 2022-07-27 bug #2121 ## can't delegate omitting NA to the matrix method since we need the time ## info after na.omit() here (to set the time properly if(na.rm) x <- na.omit(x) m = getDataPart(x) wrk = callGeneric(m, na.rm = FALSE, ...) # note: na.rm = FALSE res <- setDataPart(x, wrk) res }) # ------------------------------------------------------------------------------ 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/timeSeries-isRegular.R0000644000176200001440000000353314650724114016763 0ustar liggesusers # 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))) ## GNB: made the method S3 ## setMethod("frequency", "timeSeries", function(x, ...) callGeneric(time(x), ...)) frequency.timeSeries <- function(x, ...) { frequency(time(x), ...) } ################################################################################ timeSeries/R/base-apply.R0000644000176200001440000000606614322357566014771 0ustar liggesusers# # 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 Diethelm Wuertz and Yohan Chalabi # Description: # Apply Functions Over 'Array' timeSeries' Margins # Arguments: # X - an array, including a matrix. => GNB: actually the code throws error if X # is not 'timeSeries'. # 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/stats-model.frame.R0000644000176200001440000000670514263246022016245 0ustar liggesusers# # 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/utils-structure.R0000644000176200001440000000453714650724114016122 0ustar liggesusers# # 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, ...)) ################################################################################ timeSeries/R/aaa-Deprecated.R0000644000176200001440000000377214436616227015513 0ustar liggesusers# # 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 ################################################################################ ## removed on 2023-06-03 ## ## .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/statistics-orderColnames.R0000644000176200001440000001304114263246022017674 0ustar liggesusers# # 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/methods-mathOps.R0000644000176200001440000001542514650724114015776 0ustar liggesusers# # 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 quantiles of a timeSeries object # median,timeSeries Samples median 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 } ) ## 2023-05-31 GNB: making these work column-wise and return 'timeSeries' ## # ------------------------------------------------------------------------------ ## 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))) .cum_fun <- function(x, FUN){ wrk <- apply(getDataPart(x), 2, FUN) if (NROW(x) == 1) wrk <- matrix(wrk, nrow = 1, dimnames = dimnames(x)) x@.Data <- wrk x } setMethod("cummax", "timeSeries", function(x) .cum_fun(x, cummax)) setMethod("cummin", "timeSeries", function(x) .cum_fun(x, cummin)) setMethod("cumprod", "timeSeries", function(x) .cum_fun(x, cumprod)) setMethod("cumsum", "timeSeries", function(x) .cum_fun(x, cumsum)) # ------------------------------------------------------------------------------ ## setMethod("diff", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ ## setMethod("scale", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) # ------------------------------------------------------------------------------ ## GNB: make an S3 method, drop the S4 one ## ## setMethod("quantile", "timeSeries", function(x, ...) { ## x <- getDataPart(x) ## callGeneric() ## }) quantile.timeSeries <- function(x, ...) { x <- getDataPart(x) NextMethod("quantile") } # ------------------------------------------------------------------------------ ## GNB: make an S3 method, drop the S4 one ## ## setMethod("median", "timeSeries", function(x, na.rm, ...) { ## x <- getDataPart(x) ## callGeneric(x, na.rm=na.rm) ## }) median.timeSeries <- function(x, na.rm = FALSE, ...) { x <- getDataPart(x) NextMethod("median") } ################################################################################ timeSeries/R/base-sample.R0000644000176200001440000000211714263246022015102 0ustar liggesusers# # 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/base-scale.R0000644000176200001440000000312614321371041014704 0ustar liggesusers# # 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/timeSeries-getDataPart.R0000644000176200001440000000527714263246022017232 0ustar liggesusers# # 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/fin-durations.R0000644000176200001440000000457714263246022015507 0ustar liggesusers# # 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/methods-comment.R0000644000176200001440000000266614263246022016025 0ustar liggesusers# # 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/AllGeneric.R0000644000176200001440000000666614434360630014735 0ustar liggesusers# # 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") ## 2023-05-27 GNB: making non-generic, see comments in timeSeries-slotSeries.R ## ## 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/stats-na.contiguous.R0000644000176200001440000000500114650724114016637 0ustar liggesusers# # 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 ################################################################################ ## 2024-01-06 GNB: converted the S4 method to an S3 method and removed the S4 one. na.contiguous.timeSeries <- function(object, ...) { # A function imlemented by Diethelm Wuertz and Yohan Chalabi # fixed by GNB for the case with tied stretches one of whom starts at # the beginning of the series, see comments below. # 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) tt <- c(0, tt) # GNB, see my bug report to R-devel for stats::na.contiguous from # 2023-06-02 and the discussion there (see # https://stat.ethz.ch/pipermail/r-devel/2023-June/082642.html) # The fix is my proposed way to patch that. ln <- sapply(0:max(tt), function(i) sum(tt == i)) seg <- (seq_along(ln)[ln == max(ln)])[1L] - 1 keep <- (tt == seg) keep <- keep[-1] # GNB, see above comment 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/timeSeries-dummy.R0000644000176200001440000000530114434622223016152 0ustar liggesusers# # 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: # dummyMonthlySeries 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. dummyMonthlySeries <- 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()), ...) } ## dummySeries <- function(...) { # GNB ## .Deprecated("dummyMonthlySeries") ## dummyMonthlySeries(...) ## } # ------------------------------------------------------------------------------ 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/base-sort.R0000644000176200001440000000351414650724114014615 0ustar liggesusers# # 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 ## 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/timeSeries-slotDocumentation.R0000644000176200001440000000550614322333513020536 0ustar liggesusers# # 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(inherits(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 <- dummyMonthlySeries(); getAttributes(obj) # setAttributes(obj) <- list(mat=matrix(1:4, ncol=2)); getAttributes(obj) # getAttributes(obj)$mat[[1]] # FUNCTION: # Check Arguments: stopifnot(inherits(obj, "timeSeries") , is.list(value) , length(value) == 1) # 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/base-rev.R0000644000176200001440000000172514650724114014424 0ustar liggesusers# # 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 ################################################################################ ## GNB: removed the S4 method rev.timeSeries <- function(x) x[NROW(x):1,] ## setMethod("rev", "timeSeries", rev.timeSeries) timeSeries/R/timeSeries-slotTime.R0000644000176200001440000001054214650724114016624 0ustar liggesusers# # 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<- ################################################################################ ## GNB: swapped the definitions of .time.timeSeries and time.timeSeries ## in preparation to drop the former ## .time.timeSeries <- 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", time.timeSeries) # ------------------------------------------------------------------------------ `time<-` <- function(x, value) { UseMethod("time<-") } # ------------------------------------------------------------------------------ `time<-.default` <- function(x, value) { # A function implemented by Georgi Boshnakov zoo::time(x) <- value x } `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 } ############################################################################### 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-start.R0000644000176200001440000000401414650724114014757 0ustar liggesusers# # 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", start.timeSeries) ## # 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/fin-splits.R0000644000176200001440000000726014263246022015005 0ustar liggesusers # # 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/base-t.R0000644000176200001440000000177314263246022014073 0ustar liggesusers # # 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/fin-daily.R0000644000176200001440000002014314263246022014564 0ustar liggesusers# # 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/base-subsetting.R0000644000176200001440000005436214434676524016037 0ustar liggesusers# # 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 return NULL. ## ## GNB: 'more than one match' is in the sense that 'name' has a match in ## both colnames(x) and names(x@recordIDs). Note that 'name is of ## length 1, the matches are of length one and there is no problem in ## the 'if'. 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/base-cbind.R0000644000176200001440000002575614321332643014716 0ustar liggesusers # 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 (inherits(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 an S4 class ## # 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 (inherits(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/vignettes/0000755000176200001440000000000014673542235014406 5ustar liggesuserstimeSeries/vignettes/timeSeriesPlot.Rnw0000644000176200001440000015272614650724115020055 0ustar liggesusers%\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*{} Two 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 8.00 387.82 311.37 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 Tf 8.00 0.00 -0.00 8.00 350.06 110.69 Tm (æ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 109.86 Tm (ç) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 122.06 101.13 Tm (è) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.06 100.28 Tm (é) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.06 100.28 Tm (ê) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 236.06 100.28 Tm (ë) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 273.62 100.30 Tm (ì) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 311.62 100.26 Tm (í) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 349.62 100.22 Tm (î) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 387.62 100.26 Tm (ï) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 123.60 94.10 Tm (ð) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 160.28 91.90 Tm (ñ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 198.50 90.86 Tm (ò) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 234.86 90.77 Tm (ó) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 272.86 90.55 Tm (ô) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 310.86 90.76 Tm (õ) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 350.06 91.56 Tm (ö) Tj ET BT /F6 1 Tf 8.00 0.00 -0.00 8.00 388.06 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~”ý£3°¼ýÄÀ~×øàÑo [ü§CmòÓˆè[NIŸ9=)̰QÿÑÚºÏCúLÐgräµ >%mDrZVò̈ͼ£"±3“'¥ÏœìŒ ]!šuú/C·HKšž¬×—Hc›™é9Ðþ¿õž”š”™FÛÉIYI§ÌL _hl—#»d¥d¥&Gté8*ezrôÈä™)É™Ñþ‰&OÊŠèÖ~rú¤Ð )-+)|!ŠèΰÚ6ØäÿeøßH²Bná5Â]«Œ¤I%MŒ£Q‡ÿzëÐmP³ÎeS`þn~âéò¿´ÃóŒ—1timeSeries/ChangeLog0000644000176200001440000006433014266302774014156 0ustar liggesusers2020-01-24 Martin Maechler * DESCRIPTION (Version): 3062.100 ; using Authors@R * R/utils-head.R: tail.matrix() in R-devel uses 'keepnums'. * R/timeSeries-readSeries.R (et all): do *NOT* use `if(class(.) == "timeSeries")` but use `inherits(*, "...")` !! * R/methods-plot.R (.plotTimeSeries): fix wrong logic in `if(.. && at == "auto")` when `at` can be a vector (!) * R/aaa-utils.R (`%||%`): utility, to be used extensively in * R/methods-plot2.R (.xtplot.timeSeries). 2015-11-09 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/NAMESPACE0000644000176200001440000002030114673537717013622 0ustar liggesusers################################################ ## import name space ################################################ ## MM: Importing all is almost surely a waste [FIXME!] import("methods") # , show importFrom("grDevices", col2rgb, rgb , xy.coords) importFrom("stats", approx, approxfun, spline, splinefun, var, cor, dist, hclust, qt, rnorm, runif, runmed, ts, is.mts, deltat, ## for these, we provide methods: start, end, time, window, lag, filter, frequency, aggregate, as.ts, median, quantile, na.contiguous, na.omit ) importFrom("graphics", # Note: lines, plot, points imported from timeDate axTicks, axis, axis.POSIXct, box, grid, layout, mtext, par, plot.default, plot.new, plot.window, segments, text, title ) importFrom("utils", head.matrix, tail.matrix, read.table, .DollarNames, # as we provide an S3 method ## provide methods for these : head, tail, str ) importFrom("timeDate", abline, # the S4 generic from here, not "graphics" (graphics::abline is not generic) ##--- For these we provide and export methods : plot, # base lines, points, # graphics isDaily, isMonthly, isQuarterly, isRegular, align, ##---- end of generics for which we define/export methods getRmetricsOptions, setRmetricsOptions, finCenter, "finCenter<-", as.timeDate, atoms, dayOfWeek, isWeekday, timeCalendar, timeDate, timeSequence, timeFirstDayInMonth, timeFirstDayInQuarter, timeLastDayInMonth, timeLastDayInQuarter, kurtosis, skewness ) importMethodsFrom("timeDate", "+", "-", "Ops", "[" ) ################################################ ## S3 methods ################################################ 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") # timeSeries method was removed on 2023-05-26 S3method("diff", "timeSeries") S3method("end", "timeSeries") S3method("getUnits", default) S3method("head", "timeSeries") S3method("lag", "timeSeries") S3method("lines", "timeSeries") S3method("median", "timeSeries") S3method("merge", "timeSeries") S3method("na.contiguous", "timeSeries") S3method("na.omit", "timeSeries") S3method("plot", "timeSeries") S3method("points", "timeSeries") S3method("pretty", "timeSeries") S3method("print", timeSeries) S3method("print", timeSeries_summary) S3method("rbind", "timeSeries") S3method("rev", "timeSeries") S3method("scale", "timeSeries") S3method("sort", "timeSeries") S3method("start", "timeSeries") S3method("summary", "timeSeries") S3method("str", "timeSeries") S3method("tail", "timeSeries") S3method("window", "timeSeries") S3method("time", "timeSeries") S3method("frequency", "timeSeries") S3method("is.unsorted", "timeSeries") S3method("time<-", "timeSeries") S3method("time<-", default) if(getRversion() >= "3.6.0") { # GNB ## we ensure that when zoo is loaded zoo::`time<-` gets the 'timeSeries' ## method. This works nicely, if 'zoo' is attached after 'timeSeries' (or ## if 'timeSeries' is loaded but not attached). It may seem that if ## 'timeSeries' is attached after 'zoo', zoo::`time<-` will be masked, so ## 'time(x) <- value' will see only the methods for the timeSeries version ## of 'time<-'. But this is not a problem since the default method for ## timeSeries::`time<-` calls zoo::`time<-` S3method(zoo::`time<-`, "timeSeries") S3method(zoo::coredata, "timeSeries") S3method(zoo::'coredata<-', "timeSeries") } ################################################ ## S4 classes ################################################ exportClasses("index_timeSeries", "timeSeries", "time_timeSeries" ) exportMethods( "$", "$<-", "+", "-", "Ops", "[", ## from timeDate "align", "finCenter", "finCenter<-", "isDaily", "isMonthly", "isQuarterly", "isRegular", ## from methods "cbind2", "rbind2", "coerce", "getDataPart", "setDataPart", # not generic in methods "initialize", "show", ## primitive R functions (internally S3 and S4 generic) "cummax", "cummin", "cumprod", "cumsum", "dim", "dim<-", "dimnames", "dimnames<-", "names", "names<-", ## stats ## "aggregate", ## "as.ts", "filter", # not generic in stats; let it stay S4 here ## graphics ## "lines", "points", ## from base "apply", # not generic in base "as.data.frame", "as.list", "as.matrix", "attach", # not generic in base "colMeans", "colSums", # not generic in base "colnames", "colnames<-", "rownames", "rownames<-", # not generic in base "comment", "comment<-", # not generic in base # "cut", # timeSeries method was removed on 2023-05-26 "is.unsorted", # internal generic in base (but seemingly only S3, not S4) "merge", # the S4 methods are for x and y, so S4 is essential "plot", "rank", # not generic in base "sample", # not generic in base "t" ) ################################################ ## functions ################################################ export( ".colorwheelPalette", # used in the vignette "alignDailySeries", "applySeries", "as.timeSeries", "colCummaxs", "colCummins", "colCumprods", "colCumreturns", "colCumsums", "colKurtosis", "colMaxs", "colMins", "colProds", "colQuantiles", "colSds", "colSkewness", "colStats", "colVars", "coredata.timeSeries", "coredata<-.timeSeries", "countMonthlyRecords", "cumulated", "daily2monthly", "daily2weekly", "description", "drawdowns", "drawdownsStats", "dummyDailySeries", "dummyMonthlySeries", "durations", "endOfPeriodBenchmarks", "endOfPeriodSeries", "endOfPeriodStats", "fapply", "getAttributes", "getFinCenter", "getReturns", # several uses in the tests in 'fTrading' # I corrected the devel version of 'fTrading'. # TODO: remove when fTrading is updated on CRAN. "getTime", "getUnits", "getUnits.default", "hclustColnames", "index2wealth", "interpNA", "is.signalSeries", "is.timeSeries", "isMultivariate", "isUnivariate", "midquotes", "orderColnames", "orderStatistics", "outlier", "pcaColnames", "readSeries", "removeNA", "returns", "returns0", "rollDailySeries", "rollMax", "rollMean", "rollMedian", "rollMin", "rollMonthlySeries", "rollMonthlyWindows", "rollStats", "rowCumsums", "runlengths", "sampleColnames", "series", "series<-", "setAttributes<-", "setFinCenter<-", "setTime<-", "setUnits<-", "smoothLowess", "smoothSpline", "smoothSupsmu", "sortColnames", "splits", "spreads", "statsColnames", "substituteNA", "time", "time<-", "timeSeries", "turns", "turnsStats", "head", # 2024-01-05 GNB: were in exportMethods but no longer S4 generic "tail", "str", "start", "end", "median", "quantile", "na.contiguous", ## "sort", # from 'base', no need to export ## "rev", ## "print", ## "diff", ## "is.na", # primitive ## "frequency", "aggregate", "as.ts", # stats, reexport ## graphics "lines", "points", # graphics, reexport "na.omit", "lag", "window" ) timeSeries/NEWS.md0000644000176200001440000001763114673541461013504 0ustar liggesusers## timeSeries 4041.111 - now 'timeSeries' depends on 'timeDate' version 4041.110 or later. If you have an older version of 'timeSeries' and it stops working after updating 'timeDate', install the latest version of 'timeSeries'. The incompatibility was necessitated by the need to fix a bug. ## timeSeries 4041.110 - this is a technical update, there are no user visible changes (there was an explicit call to `timeDate::months` in the code, but the upcoming v4041.110 of timeDate doesn't export `months` anymore). ## timeSeries 4032.109 - deprecated function `returnSeries` is now defunct, use `returns` instead. - a number of generic functions from base R now get only S3 methods for 'timeSeries' objects. Previously they were turned into S4 generics with S4 methods. - streamlined timeSeries methods for a number of functions. Left only S3 methods or only S4 methods were suitable. - consolidated the NAMESPACE. ## timeSeries 4032.108 - fixed 'Lost braces; missing escapes or markup?' NOTE from CRAN. ## timeSeries 4031.107 - refactored the 'timeSeries' methods for `head` and `tail`. - fixed a bug in the 'timeSeries' method for `stats::na.contiguous`, which caused the wrong stretch to be returned in the case of tied longest stretches one of whom starts at the beginning of the series. Similar bug was present in `stats::na.contiguous.default`, see my bug report to R-devel from 2023-06-02 and the discussion there (https://stat.ethz.ch/pipermail/r-devel/2023-June/082642.html) - removed deprecated functions `spreadSeries`, `midquoteSeries`, and `durationSeries`. Use `spreads`, `midquotes`, and `durations`, respectively. - removed deprecated function `colStdevs`, use `colSds()` instead. - removed deprecated function `.description`, use `description()` instead. - removed deprecated 'timeSeries' method for function `cut()`, use `window()` instead. The method was not compatible with the generic function `cut()`. Now applying `cut(x)` on a 'timeSeries' object `x` will work on the underlying time series data. - replaced the S4 methods for `zoo::coredata` and `zoo::'coredata<-'`. The ones for `zoo::'coredata<-'` were not working at all, since `zoo::'coredata<-'` is an S3 generic and the methods dispatch on two arguments. It is also a mistery why the methods for the unexported S4 generics in 'timeSeries' were associated with the corresponding 'zoo' generics. If `zoo` is not attached, the calls need to be prefixed with `zoo::` or, alternatively, since the new methods are exported, they can be called directly as `coredata.timeSeries()` and ``coredata.'timeSeries<-'() <- value`. - added a default method for `time<-` to improve its interaction with 'zoo'. - added 'zoo' to 'Suggests:'. - removed the deprecated `dummySeries`, use `dummyMonthlySeries` instead. - added argument `FUN` to the `timeSeries` method for `na.omit` to allow it to compute replacement values using functions, such as `mean`, `median`, or user defined. - formally deprecated `removeNA`, `interpNA`, and `substituteNA`. These had been informally deprecated in the documentation for a long time. - the help page for `orderStatistics` erroneously claimed that the input should be an univariate `timeSeries` object, while it is explicitly written to cover the multivariate case. - moved package 'methods' back to 'Depends' to avoid subtle problems when 'methods' is loaded but not attached. For example, it seems that 'Math' methods for 'structure' are not seen for `cummin` and other `cumXXX` functions, when called on time series objects (the other math functions work ok). - `cumsum`, `cumprod`, `cummin`, and `cummax` now work on the columns of the 'timeSeries' object and keep its class and other attributes. This is a breaking change since previously the return value was numeric vector, the result of applying the base R functions to the data part of the object. This was not particularly useful, especilly for multivariate time series. With this change all functions from the S4 `Math` group return 'timeSeries' when their argument is 'timeSeries' object. - stopped exporting some internal functions that were accidentally used by other packages (after those packages were updated on CRAN). - Numerous improvements to the documentation and further changes in the code. ## timeSeries 4030.106 - removed UTF8 characters from NAMESPACE (fixes CRAN warning to that effect). ## timeSeries 4021.105 - updated and significantly improved the documentation. - class `timeSeries` now has a dedicated summary method. Previously it was falling back to the method for matrices. - `colCumsums`, `colCummaxs`, `colCummins`, and `colCumprods` no longer throw error for `timeSeries` objects when called with `na.rm = TRUE`. Fixes bug #2121 reported by Shane Haas. - corrected USDCHF dataset. The year information was wrong (the data started from year 8295). The bug had been introduced in version 2100.84 when the dataset file was converted from a `usdchf.csv` to `USDCHF.rda`. `USDCHF@documentation` contains a short note about this change. Also changed the FinCenter to Zurich (neither the documentation nor the csv file contain FinCenter information). - the original source file `msft.dat.csv` of the `MSFT` data is included now as `inst/extdata/msft.csv` (note the different name). The file had been removed in v2100.84. Note that there is a file ``msft.dat.csv` in `test/` but it is a modified and abbreviated version of the original file. - `dummySeries` has been renamed to the more expressive `dummyMonthlySeries`. The old name is still available but is deprecated. - The functions `returnSeries` and `getReturns` are no longer exported and will be removed in the near future. They are synonyms for the function `returns` and their use was discouraged for many years. Just use `returns`. - function `cut` is now formally deprecated. Use `window` instead. - deprecated function `seriesData` is now defunct. Use `as.matrix()` instead. - deprecated function `seriesPositions` is now defunct. Use `time()` instead. - deprecated function `newPositions<-` is now defunct. Use `time<-` instead. - deprecated function `colAvgs` is now defunct. Use `colMeans()` instead. - deprecated function `colStdevs` is now defunct. Use `colSds()` instead. ### Technical changes - stopped exporting (almost) all functions whose names start with a '.'. Historically, the package was exporting all functions, including those start with a '.'. This should be of no concern for users since these functions were not documented but the developers of some Rmetrics packages where using such functions. - the additional arguments of the S3 `timeSeries` method for `diff()` are now in its signature, which previously was `diff(x, ...)`. An intermediate function, `.diff.timeSeries`, was eliminated in the process. - the bodies of the methods of `series<-()` and `coredata<-` for signature `"matrix"` of value were identical. Now the body is a separate, unexported function, which is used as the definition of both of these methods. - eliminated `.merge.timeSeries` and other redundancy in the implementation of the `c("timeSeries", "timeSeries")` method. - eliminated `.rev.timeSeries` in the definition of the `rev` method. - eliminated `.scale.timeSeries` in the definition of the `scale` timeSeries method. - same as above for `.sort.timeSeries`. - eliminated `.start.timeSeries`and redundancy in the implementation of the `timeSeries` method. - eliminated `.end.timeSeries`and redundancy in the implementation of the `timeSeries` method. - the function `.applySeries` is now defunct. It was obsoleted long time ago and was exported for historical reasons only. Use `applySeries()` instead. ## timeSeries 4021.104 - new maintainer: Georgi Boshnakov. - moved package `methods` to `Imports`. - fixed CRAN NOTE `Escaped LaTeX specials: \_ \_` in `methods-plot.Rd`. ## timeSeries 3062.100 and older See file `ChangeLog`. timeSeries/inst/0000755000176200001440000000000014650725075013353 5ustar liggesuserstimeSeries/inst/README0000644000176200001440000000012014263246021014211 0ustar liggesusersintroduction of timeSeries package in the Rmetrics suite after svn revision 3319timeSeries/inst/THANKS0000644000176200001440000000000114263246021014242 0ustar liggesusers timeSeries/inst/pkgdown.yml0000644000176200001440000000033314650725075015546 0ustar liggesuserspandoc: 2.9.2.1 pkgdown: 2.0.7 pkgdown_sha: ~ articles: {} last_built: 2024-01-14T19:35Z urls: reference: https://geobosh.github.io/timeSeriesDoc/reference article: https://geobosh.github.io/timeSeriesDoc/articles timeSeries/inst/_pkgdown.yml0000644000176200001440000000721014650724113015676 0ustar liggesusersurl: https://geobosh.github.io/timeSeriesDoc/ deploy: install_metadata: true template: bootstrap: 5 search: exclude: ['news/index.html'] reference: - title: "Overview of package timeSeries" contents: - "timeSeries-package" - TimeSeriesData - title: "Create 'timeSeries' objects" contents: - timeSeries - readSeries - dummyMonthlySeries - dummyDailySeries - as.timeSeries - as.matrix.timeSeries - as.ts.timeSeries - as.data.frame.timeSeries - title: "Explore 'timeSeries' objects" contents: - plot - "lines,timeSeries-method" - "points,timeSeries-method" - pretty.timeSeries - "print.timeSeries" - str - is.timeSeries - is.signalSeries - isUnivariate - isMultivariate - isDaily.timeSeries - isMonthly.timeSeries - isQuarterly.timeSeries - isRegular - "is.na" - "is.unsorted,timeSeries-method" - title: "Subset 'timeSeries' objects" desc: > There are 'timeSeries' methods for subsetting operators, like '[' and '[<-', as well as functions and methods which broadly perform some kind of subsetting. contents: - window - TimeSeriesSubsettings - na.contiguous - na.omit - removeNA - substituteNA - interpNA - endOfPeriodSeries - endOfPeriodStats - endOfPeriodBenchmarks - title: "Aggregate and smooth 'timeSeries' objects" contents: - filter - smoothLowess - smoothSpline - smoothSupsmu - aggregate - align - alignDailySeries - daily2monthly - daily2weekly - fapply - applySeries - rollDailySeries - rollMonthlySeries - countMonthlyRecords - rollMonthlyWindows - title: "Manipulate 'timeSeries' objects" contents: - series - "series<-" - getFinCenter - "setFinCenter<-" - finCenter - "`finCenter<-`" - time - "time<-" - getUnits - "setUnits<-" - start - end - getAttributes - "setAttributes<-" - comment - "`comment<-`" - orderColnames - sortColnames - sampleColnames - statsColnames - pcaColnames - hclustColnames - title: "Transform 'timeSeries' objects" contents: - scale - diff - colCum - colCummaxs - colCummins - colCumprods - colCumreturns - colCumsums - rowCumsums - lag - sort - rev - runlengths - durations - rank - sample - math - title: "Financial computations on 'timeSeries' objects" contents: - returns - returns0 - cumulated - drawdowns - drawdownsStats - splits - spreads - midquotes - midquoteSeries - spreadSeries - index2wealth - title: "Compute statistics on timeSeries objects" contents: - "cov-methods" - "cor-methods" - colStats - colSds - colVars - colSkewness - colKurtosis - colMaxs - colMins - colProds - colQuantiles - turns - turnsStats - orderStatistics - rollStats - rollMean - rollMin - rollMax - rollMedian - title: "Combine time series" contents: - cbind - rbind - cbind2 - rbind2 - merge - title: "Mathematical operations on 'timeSeries'" contents: - math - t - title: "Other" contents: - "timeSeries-class" - dimnames - "DataPart,timeSeries-method" - description - attach - internals timeSeries/inst/extensionsTests/0000755000176200001440000000000014322333073016562 5ustar liggesuserstimeSeries/inst/extensionsTests/attributesExtension.R0000644000176200001440000002312714322333073022775 0ustar liggesusers # 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 <- dummyMonthlySeries(); 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/aggregateWrappers.R0000644000176200001440000002151214263246021022360 0ustar liggesusers 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.R0000644000176200001440000001722514322333051021526 0ustar liggesusers # 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 <- dummyMonthlySeries() getAttributes(obj1) setAttributes(obj1) <- list(series="obj1") getAttributes(obj1) obj2 <- dummyMonthlySeries() 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/chicPlots.R0000644000176200001440000002521314263246021020640 0ustar liggesusers 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/endpointsWrappers.R0000644000176200001440000001457114263246021022444 0ustar liggesusers 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/xtsWrappers.R0000644000176200001440000002451014263246021021251 0ustar liggesusers 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/doc/0000755000176200001440000000000014673542235014120 5ustar liggesuserstimeSeries/inst/doc/timeSeriesPlot.Rnw0000644000176200001440000015272614650724115017567 0ustar liggesusers%\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*{} Two 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.R0000644000176200001440000006274114673542235017225 0ustar liggesusers### 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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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.timeSeries.R0000644000176200001440000000327614263246021020746 0ustar liggesusers # 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.colCum.R0000644000176200001440000000370314322333240020046 0ustar liggesusers # 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 <- dummyMonthlySeries(format = "counts") colCumsums(ts) colCummaxs(ts) colCummins(ts) colCumprods(ts) colCumreturns(ts) # Time Series: ts <- dummyMonthlySeries() 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) ## 2022-07-27 GB: check fix for #2121 x=dummyMonthlySeries() x[1,2]=NA colCumsums(x, na.rm = TRUE) colCummaxs(x, na.rm = TRUE) colCummins(x, na.rm = TRUE) colCumprods(x, na.rm = TRUE) } ################################################################################ timeSeries/inst/unitTests/runit.NA.R0000644000176200001440000000653614263246021017135 0ustar liggesusers # 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.Omit.R0000644000176200001440000000346714263246021017547 0ustar liggesusers # 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.cor.R0000644000176200001440000000213514322333240017405 0ustar liggesusers # 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 = dummyMonthlySeries(format = "counts") tS cor(tS) cov(tS) # timeDate Series: tS = dummyMonthlySeries() tS cor(tS) cov(tS) } ################################################################################ timeSeries/inst/unitTests/runit.as.R0000644000176200001440000000253114322333240017225 0ustar liggesusers # 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 = dummyMonthlySeries() 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.apply.R0000644000176200001440000000243414263246021017755 0ustar liggesusers # 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.TimeSeriesData.R0000644000176200001440000002416514263246021021500 0ustar liggesusers # 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.methods-print.R0000644000176200001440000000162714263246021021430 0ustar liggesusers # 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.merge.R0000644000176200001440000000317614322333240017727 0ustar liggesusers # 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 <- dummyMonthlySeries()[,1] x y <- dummyMonthlySeries() 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.attach.R0000644000176200001440000000200214263246021020063 0ustar liggesusers # 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.durations.R0000644000176200001440000000235514322333240020636 0ustar liggesusers # 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(dummyMonthlySeries())[1:6, ]) tS durations(tS) durations(tS, trim = TRUE) durations(tS, trim = TRUE)/(24*3600) # Time Series: tS = sort(sample(dummyMonthlySeries(format = "counts"))[1:6, ]) tS # BUG !!! # durations(tS) # durations(tS, trim = TRUE) } ################################################################################ timeSeries/inst/unitTests/runit.subset.R0000644000176200001440000001244014322333000020121 0ustar liggesusers # 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 <- dummyMonthlySeries() 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.time.R0000644000176200001440000000162614263246021017570 0ustar liggesusers # 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/Makefile0000644000176200001440000000042414673542237017017 0ustar liggesusersPKG=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.aggregate.R0000644000176200001440000000447414263246021020564 0ustar liggesusers # 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.na.contiguous.R0000644000176200001440000000245214263246021021424 0ustar liggesusers # 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.returns.R0000644000176200001440000000163114263246021020330 0ustar liggesusers # 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.dim.R0000644000176200001440000000344514263246021017404 0ustar liggesusers # 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.align.R0000644000176200001440000000254714263246021017727 0ustar liggesusers # 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.methods-plot.R0000644000176200001440000000162614263246021021251 0ustar liggesusers # 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/runTests.R0000644000176200001440000000453314263246021017321 0ustar liggesuserspkg <- "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.methods-summary.R0000644000176200001440000000163114263246021021764 0ustar liggesusers # 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.cumulated.R0000644000176200001440000000213314322333240020603 0ustar liggesusers # 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 = dummyMonthlySeries(format = "counts") # problem with Fincenter cumulated(tS) # timeDate Series: tS = dummyMonthlySeries() cumulated(tS) } ################################################################################ timeSeries/inst/unitTests/runit.lag.R0000644000176200001440000000305214322333240017364 0ustar liggesusers # 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(dummyMonthlySeries(flormat = "counts"), 3)[, 1] tS lag(tS) lag(tS, k = -2:2) lag(tS, k = -2:2, trim = TRUE) tS = round(dummyMonthlySeries(), 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.monthly.R0000644000176200001440000000163114263246021020320 0ustar liggesusers # 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.colStats.R0000644000176200001440000000266114322333240020422 0ustar liggesusers # 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 = dummyMonthlySeries(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 = dummyMonthlySeries() 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.signalCounts.R0000644000176200001440000000255714263246021021307 0ustar liggesusers # 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/runit.drawdowns.R0000644000176200001440000000234614263246021020642 0ustar liggesusers # 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.TimeSeriesClass.R0000644000176200001440000002602014263246021021664 0ustar liggesusers # 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.model.frame.R0000644000176200001440000000163514263246021021023 0ustar liggesusers # 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.mathOps.R0000644000176200001440000000215314322333240020235 0ustar liggesusers # 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.mathOps <- function() { # RUnit Test: tS = dummyMonthlySeries(format = "counts") tS tS - 2 log(abs(tS)) diff(tS) scale(tS) tS = dummyMonthlySeries() tS tS - 2 log(abs(tS)) diff(tS) scale(tS) } ################################################################################ timeSeries/inst/unitTests/runit.TimeSeriesPositions.R0000644000176200001440000000513214263246021022607 0ustar liggesusers # 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.TimeSeriesCoercion.R0000644000176200001440000001554114263246021022366 0ustar liggesusers # 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.spreads.R0000644000176200001440000000200514322333240020257 0ustar liggesusers # 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 = dummyMonthlySeries(units = c("Bid", "Ask")) head(tS) midquotes(tS) spreads(tS) } ################################################################################ timeSeries/inst/unitTests/runit.periodical.R0000644000176200001440000000163414263246021020744 0ustar liggesusers # 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.rank.R0000644000176200001440000000162614263246021017565 0ustar liggesusers # 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.bind.R0000644000176200001440000000512314322333240017536 0ustar liggesusers # 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 <- dummyMonthlySeries() 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.order.R0000644000176200001440000000266214263246021017746 0ustar liggesusers # 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.rowCum.R0000644000176200001440000000163014263246021020101 0ustar liggesusers # 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/inst/COPYING0000644000176200001440000004310714263246021014400 0ustar liggesusers 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. (Some other Free Software Foundation software is covered by the GNU Library General Public License instead.) You can apply it to your programs, too. When we speak of free software, we are referring to freedom, not price. Our General Public Licenses are designed to make sure that you have the freedom to distribute copies of free software (and charge for this service if you wish), that you receive source code or can get it if you want it, that you can change the software or use pieces of it in new free programs; and that you know you can do these things. To protect your rights, we need to make restrictions that forbid anyone to deny you these rights or to ask you to surrender the rights. These restrictions translate to certain responsibilities for you if you distribute copies of the software, or if you modify it. For example, if you distribute copies of such a program, whether gratis or for a fee, you must give the recipients all the rights that you have. You must make sure that they, too, receive or can get the source code. And you must show them these terms so they know their rights. We protect your rights with two steps: (1) copyright the software, and (2) offer you this license which gives you legal permission to copy, distribute and/or modify the software. Also, for each author's protection and ours, we want to make certain that everyone understands that there is no warranty for this free software. If the software is modified by someone else and passed on, we want its recipients to know that what they have is not the original, so that any problems introduced by others will not reflect on the original authors' reputations. Finally, any free program is threatened constantly by software patents. We wish to avoid the danger that redistributors of a free program will individually obtain patent licenses, in effect making the program proprietary. To prevent this, we have made it clear that any patent must be licensed for everyone's free use or not licensed at all. The precise terms and conditions for copying, distribution and modification follow. GNU GENERAL PUBLIC LICENSE TERMS AND CONDITIONS FOR COPYING, DISTRIBUTION AND MODIFICATION 0. This License applies to any program or other work which contains a notice placed by the copyright holder saying it may be distributed under the terms of this General Public License. The "Program", below, refers to any such program or work, and a "work based on the Program" means either the Program or any derivative work under copyright law: that is to say, a work containing the Program or a portion of it, either verbatim or with modifications and/or translated into another language. (Hereinafter, translation is included without limitation in the term "modification".) Each licensee is addressed as "you". Activities other than copying, distribution and modification are not covered by this License; they are outside its scope. The act of running the Program is not restricted, and the output from the Program is covered only if its contents constitute a work based on the Program (independent of having been made by running the Program). Whether that is true depends on what the Program does. 1. You may copy and distribute verbatim copies of the Program's source code as you receive it, in any medium, provided that you conspicuously and appropriately publish on each copy an appropriate copyright notice and disclaimer of warranty; keep intact all the notices that refer to this License and to the absence of any warranty; and give any other recipients of the Program a copy of this License along with the Program. You may charge a fee for the physical act of transferring a copy, and you may at your option offer warranty protection in exchange for a fee. 2. You may modify your copy or copies of the Program or any portion of it, thus forming a work based on the Program, and copy and distribute such modifications or work under the terms of Section 1 above, provided that you also meet all of these conditions: a) You must cause the modified files to carry prominent notices stating that you changed the files and the date of any change. b) You must cause any work that you distribute or publish, that in whole or in part contains or is derived from the Program or any part thereof, to be licensed as a whole at no charge to all third parties under the terms of this License. c) If the modified program normally reads commands interactively when run, you must cause it, when started running for such interactive use in the most ordinary way, to print or display an announcement including an appropriate copyright notice and a notice that there is no warranty (or else, saying that you provide a warranty) and that users may redistribute the program under these conditions, and telling the user how to view a copy of this License. (Exception: if the Program itself is interactive but does not normally print such an announcement, your work based on the Program is not required to print an announcement.) These requirements apply to the modified work as a whole. If identifiable sections of that work are not derived from the Program, and can be reasonably considered independent and separate works in themselves, then this License, and its terms, do not apply to those sections when you distribute them as separate works. But when you distribute the same sections as part of a whole which is a work based on the Program, the distribution of the whole must be on the terms of this License, whose permissions for other licensees extend to the entire whole, and thus to each and every part regardless of who wrote it. Thus, it is not the intent of this section to claim rights or contest your rights to work written entirely by you; rather, the intent is to exercise the right to control the distribution of derivative or collective works based on the Program. In addition, mere aggregation of another work not based on the Program with the Program (or with a work based on the Program) on a volume of a storage or distribution medium does not bring the other work under the scope of this License. 3. You may copy and distribute the Program (or a work based on it, under Section 2) in object code or executable form under the terms of Sections 1 and 2 above provided that you also do one of the following: a) Accompany it with the complete corresponding machine-readable source code, which must be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, b) Accompany it with a written offer, valid for at least three years, to give any third party, for a charge no more than your cost of physically performing source distribution, a complete machine-readable copy of the corresponding source code, to be distributed under the terms of Sections 1 and 2 above on a medium customarily used for software interchange; or, c) Accompany it with the information you received as to the offer to distribute corresponding source code. (This alternative is allowed only for noncommercial distribution and only if you received the program in object code or executable form with such an offer, in accord with Subsection b above.) The source code for a work means the preferred form of the work for making modifications to it. For an executable work, complete source code means all the source code for all modules it contains, plus any associated interface definition files, plus the scripts used to control compilation and installation of the executable. However, as a special exception, the source code distributed need not include anything that is normally distributed (in either source or binary form) with the major components (compiler, kernel, and so on) of the operating system on which the executable runs, unless that component itself accompanies the executable. If distribution of executable or object code is made by offering access to copy from a designated place, then offering equivalent access to copy the source code from the same place counts as distribution of the source code, even though third parties are not compelled to copy the source along with the object code. 4. You may not copy, modify, sublicense, or distribute the Program except as expressly provided under this License. Any attempt otherwise to copy, modify, sublicense or distribute the Program is void, and will automatically terminate your rights under this License. However, parties who have received copies, or rights, from you under this License will not have their licenses terminated so long as such parties remain in full compliance. 5. You are not required to accept this License, since you have not signed it. However, nothing else grants you permission to modify or distribute the Program or its derivative works. These actions are prohibited by law if you do not accept this License. Therefore, by modifying or distributing the Program (or any work based on the Program), you indicate your acceptance of this License to do so, and all its terms and conditions for copying, distributing or modifying the Program or works based on it. 6. Each time you redistribute the Program (or any work based on the Program), the recipient automatically receives a license from the original licensor to copy, distribute or modify the Program subject to these terms and conditions. You may not impose any further restrictions on the recipients' exercise of the rights granted herein. You are not responsible for enforcing compliance by third parties to this License. 7. If, as a consequence of a court judgment or allegation of patent infringement or for any other reason (not limited to patent issues), conditions are imposed on you (whether by court order, agreement or otherwise) that contradict the conditions of this License, they do not excuse you from the conditions of this License. If you cannot distribute so as to satisfy simultaneously your obligations under this License and any other pertinent obligations, then as a consequence you may not distribute the Program at all. For example, if a patent license would not permit royalty-free redistribution of the Program by all those who receive copies directly or indirectly through you, then the only way you could satisfy both it and this License would be to refrain entirely from distribution of the Program. If any portion of this section is held invalid or unenforceable under any particular circumstance, the balance of the section is intended to apply and the section as a whole is intended to apply in other circumstances. It is not the purpose of this section to induce you to infringe any patents or other property right claims or to contest validity of any such claims; this section has the sole purpose of protecting the integrity of the free software distribution system, which is implemented by public license practices. Many people have made generous contributions to the wide range of software distributed through that system in reliance on consistent application of that system; it is up to the author/donor to decide if he or she is willing to distribute software through any other system and a licensee cannot impose that choice. This section is intended to make thoroughly clear what is believed to be a consequence of the rest of this License. 8. If the distribution and/or use of the Program is restricted in certain countries either by patents or by copyrighted interfaces, the original copyright holder who places the Program under this License may add an explicit geographical distribution limitation excluding those countries, so that distribution is permitted only in or among countries not thus excluded. In such case, this License incorporates the limitation as if written in the body of this License. 9. The Free Software Foundation may publish revised and/or new versions of the General Public License from time to time. Such new versions will be similar in spirit to the present version, but may differ in detail to address new problems or concerns. Each version is given a distinguishing version number. If the Program specifies a version number of this License which applies to it and "any later version", you have the option of following the terms and conditions either of that version or of any later version published by the Free Software Foundation. If the Program does not specify a version number of this License, you may choose any version ever published by the Free Software Foundation. 10. If you wish to incorporate parts of the Program into other free programs whose distribution conditions are different, write to the author to ask for permission. For software which is copyrighted by the Free Software Foundation, write to the Free Software Foundation; we sometimes make exceptions for this. Our decision will be guided by the two goals of preserving the free status of all derivatives of our free software and of promoting the sharing and reuse of software generally. NO WARRANTY 11. BECAUSE THE PROGRAM IS LICENSED FREE OF CHARGE, THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 12. IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MAY MODIFY AND/OR REDISTRIBUTE THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS), EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH DAMAGES. END OF TERMS AND CONDITIONS How to Apply These Terms to Your New Programs If you develop a new program, and you want it to be of the greatest possible use to the public, the best way to achieve this is to make it free software which everyone can redistribute and change under these terms. To do so, attach the following notices to the program. It is safest to attach them to the start of each source file to most effectively convey the exclusion of warranty; and each file should have at least the "copyright" line and a pointer to where the full notice is found. Copyright (C) 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. You should have received a copy of the GNU General Public License along with this program; if not, write to the Free Software Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307 USA Also add information on how to contact you by electronic and paper mail. 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. 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2001-05-22;69.45;70.35;69.18;70.31;41727800 2001-05-23;70.39;71.6;69.51;69.7;46818700 2001-05-24;69.94;71.78;69.27;71.72;40390800 2001-05-25;71.66;71.9;70.36;70.91;26373800 2001-05-29;70.8;71.75;70.05;70.34;35605400 2001-05-30;69.56;70.58;68.65;69.19;43250900 2001-05-31;69.49;70.38;68.4;69.18;35341300 2001-06-01;69.6;70.7;68.7;70.34;28793800 2001-06-04;70.55;71.02;69.8;70.78;21868300 2001-06-05;70.76;73.08;70.5;72.6;44727100 2001-06-06;72.89;73.48;71.55;72.36;40011400 2001-06-07;72.12;73.73;72.08;73.68;33480000 2001-06-08;73.7;73.75;72.05;73.19;25933500 2001-06-11;72.85;72.85;71.51;72.12;23672800 2001-06-12;71.02;72.41;70.81;72.08;33357300 2001-06-13;72.05;72.3;70.64;70.69;27651200 2001-06-14;70.22;70.55;68.4;68.9;35986200 2001-06-15;67.51;68.3;66.4;68.02;54177200 2001-06-18;67.95;67.96;66.01;66.88;28423400 2001-06-19;68.21;68.85;66.85;67.32;31728700 2001-06-20;67.14;69.59;67.1;69.41;32054200 2001-06-21;69.15;70.55;68.92;69.84;34801900 2001-06-22;70;70.61;68.58;68.83;25546000 2001-06-25;69.1;69.81;67.77;68.85;24607800 2001-06-26;67.82;70.21;67.7;70.14;31538500 2001-06-27;69.86;71.53;69.36;71.14;34599900 2001-06-28;71.55;76.15;70.53;72.74;64487800 2001-06-29;72.6;73.41;71.4;73;47141900 2001-07-02;72.05;73.15;70.15;70.6;36405100 2001-07-03;70.3;70.8;69.93;70.47;14018700 2001-07-05;70.22;70.72;68.44;68.51;24621300 2001-07-06;68.3;68.4;65.67;66.06;33733900 2001-07-09;66.2;66.91;65.04;65.69;33238300 2001-07-10;65.9;66.25;64.35;64.48;33281300 2001-07-11;64.21;66.75;64.2;66.5;36911300 2001-07-12;70.7;72.05;70.33;71.6;64039000 2001-07-13;71.4;72;70.94;71.34;29467300 2001-07-16;71.45;72.16;70.15;71.18;27995400 2001-07-17;70.66;72.01;70.14;71.82;31620500 2001-07-18;70.6;71.5;69.87;70.57;28795400 2001-07-19;71.22;73;71.22;72.57;38274700 2001-07-20;68.03;69.4;67.94;69.18;62101800 2001-07-23;69.24;69.24;66.35;67.09;39999700 2001-07-24;67;67.99;65.7;66.32;33765100 2001-07-25;66.26;67.52;65.61;67.48;37032700 2001-07-26;67.12;67.32;65.5;66.59;38987000 2001-07-27;66.05;66.25;65.05;65.47;32698000 2001-07-30;65.65;66.88;65.54;65.8;21098200 2001-07-31;66.01;67.39;65.85;66.19;29515800 2001-08-01;66.8;66.81;65.76;66.47;27839500 2001-08-02;67.21;67.54;66.26;67.45;27099200 2001-08-03;67.3;67.36;66;66.89;21630200 2001-08-06;66.53;67.12;65.68;66.13;13915800 2001-08-07;66.04;67.05;65.99;66.35;15673900 2001-08-08;66.51;67.24;64.49;64.86;27498200 2001-08-09;64.98;65.55;64.3;65.01;22768100 2001-08-10;64.77;65.86;62.9;65.52;25878200 2001-08-13;65.24;65.99;64.75;65.83;16337700 2001-08-14;65.75;66.09;64.45;64.69;18240600 2001-08-15;64.71;65.05;63.2;63.2;19751500 2001-08-16;62.84;64.71;62.7;64.62;21952800 2001-08-17;63.78;64.13;61.5;61.88;26117100 2001-08-20;61.66;62.75;61.1;62.7;24185600 2001-08-21;62.7;63.2;60.71;60.78;23555900 2001-08-22;61.13;61.15;59.08;60.66;39053600 2001-08-23;60.67;61.53;59;59.12;25906600 2001-08-24;59.6;62.28;59.23;62.05;31699500 2001-08-27;61.9;63.36;61.57;62.31;22281400 2001-08-28;62.34;62.95;60.58;60.74;23711400 2001-08-29;61.05;61.3;59.54;60.25;24085000 2001-08-30;59.04;59.66;56.52;56.94;48816000 2001-08-31;56.85;58.06;56.3;57.05;28950400 2001-09-04;57.19;59.08;56.07;56.1;33594600 2001-09-05;56.18;58.39;55.39;57.74;44735300 2001-09-06;56.56;58.39;55.9;56.02;56178400 2001-09-07;56.11;57.36;55.31;55.4;44931900 2001-09-10;54.92;57.95;54.7;57.58;42235900 2001-09-17;54.02;55.1;52.8;52.91;63751000 2001-09-18;53.41;55;53.17;54.32;41591300 2001-09-19;54.46;54.7;50.6;53.87;63475100 2001-09-20;52.35;52.61;50.67;50.76;58991600 2001-09-21;47.92;50.6;47.5;49.71;92488300 2001-09-24;50.65;52.45;49.87;52.01;42790100 2001-09-25;52.27;53;50.16;51.3;42470300 2001-09-26;51.51;51.8;49.55;50.27;29262200 2001-09-27;50.1;50.68;48;49.96;40595600timeSeries/inst/COPYRIGHTS0000644000176200001440000000770514263246021014767 0ustar liggesusers________________________________________________________________________________ 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/README.md0000644000176200001440000000331714436436525013662 0ustar liggesusers [![CRANStatusBadge](http://www.r-pkg.org/badges/version/timeSeries)](https://cran.r-project.org/package=timeSeries) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/timeSeries)](https://www.r-pkg.org/pkg/timeSeries) [![CRAN RStudio mirror downloads](https://cranlogs.r-pkg.org/badges/grand-total/timeSeries?color=blue)](https://r-pkg.org/pkg/timeSeries) The R package 'timeSeries' provides a time series class and tools for creation, import, manipulation, statistical and financial computations on time series. Package `timeSeries` is part of the Rmetrics suite of R packages and is developed on R-forge at [timeSeries](https://r-forge.r-project.org/scm/viewvc.php/pkg/timeSeries/?root=rmetrics). The root of Rmetrics is at [R-forge](https://r-forge.r-project.org/projects/rmetrics). # Installing timeSeries Install the [latest stable version](https://cran.r-project.org/package=timeSeries) of `timeSeries` from CRAN: install.packages("timeSeries") You can install the [development version](https://r-forge.r-project.org/scm/viewvc.php/pkg/timeSeries/?root=rmetrics) of `timeSeries` from R-forge: install.packages("timeSeries", repos = "http://R-Forge.R-project.org") To report bugs visit [Rmetrics](https://r-forge.r-project.org/projects/rmetrics/). # Documentation You can view the documentation of `timeSeries` at [timeSeriesDoc](https://geobosh.github.io/timeSeriesDoc/) (an web site produce with pkgdown) or download the [reference manual](https://cran.r-project.org/package=timeSeries/timeSeries.pdf) of the latest release from CRAN. There is also a large [vignette on plots](https://cran.r-project.org/package=timeSeries/vignettes/timeSeriesPlot.pdf). timeSeries/build/0000755000176200001440000000000014673542235013475 5ustar liggesuserstimeSeries/build/vignette.rds0000644000176200001440000000032614673542235016035 0ustar liggesusers‹‹àb```b`a’Ì@&³0`b fd`aàÒB%™¹©Á©E™©Å9ù%zAyåh*dAâ%™yé êµê þIY©É%ÅŒ+HICS!€n!ÐuÂ`yˆ: `aBRÏš—˜›Šn »KjAj^ HøvýŒÿÑ´px§V–çÁô ¨aƒªaqËÌI…Ù’Yç0¸¸A™ŒAènÀ0ÅýœEùåz0?ð‚b¤Hüt&ç$£{”+%±$Q/­¨än°M@ÓtimeSeries/man/0000755000176200001440000000000014673542237013153 5ustar liggesuserstimeSeries/man/base-cbind.Rd0000644000176200001440000000476414436342036015434 0ustar liggesusers\name{cbind} \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{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} \title{Bind 'timeSeries' objects by column or row} \description{ Binds \code{"timeSeries"} objects either by column or by row. } % \S4method{merge}{timeSeries,timeSeries}(x, y, ...) \usage{ \method{cbind}{timeSeries}(\dots, deparse.level = 1) \method{rbind}{timeSeries}(\dots, deparse.level = 1) \S4method{cbind2}{timeSeries,ANY}(x, y) ## other methods for 'cbind2' with the same arguments, see Details \S4method{rbind2}{timeSeries,ANY}(x, y) ## other methods for 'rbind2' with the same arguments, see Details } \arguments{ \item{x, y}{ objects, at least one of whom is of class \code{"timeSeries"}. } \item{\dots}{ further arguments to bind. } \item{deparse.level}{ see the documentation of \code{base::cbind}. } } \details{ These functions bind the objects by row \code{rXXX} or column (\code{cXXX}. \code{cbind} and \code{rbind} are S3 generics, so the \code{"timeSeries"} methods describe here are called only when the first argument is \code{"timeSeries"}. \code{cbind2} and \code{rbind2} are S4 generics which dispatch on the first two arguments. The \code{"timeSeries"} methods for these are invoked whenever at least one of the first two arguments is of class \code{"timeSeries"}. All functions can be called with more than two arguments. After the first two are merged, the result is merged with the third, and so on. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{merge}} for another way to merge \code{"timeSeries"} object column-wise. \code{\link[base]{rbind}} and \code{\link[base]{cbind}} from base R, \code{\link[methods]{rbind2}} and \code{\link[methods]{cbind2}} from package \code{"methods"}, } \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-colSums.Rd0000644000176200001440000000442714435106306017253 0ustar liggesusers\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} % removed, but leave the alias for now % \alias{mean.timeSeries} % \alias{var.timeSeries} \title{Column statistics} \description{ A collection of functions to compute column statistical properties of financial and economic time series data. } \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) } % colAvgs(x, \dots) % \method{mean}{timeSeries}(x, \dots) % \method{var}{timeSeries}(x, \dots) \arguments{ \item{x}{ a rectangular object which can be transformed into a matrix by the function \code{as.matrix}. } \item{FUN}{ a function name, the statistical function to be applied. } \item{prob}{ a numeric value in [0,1]. } \item{\dots}{ arguments to be passed. } } \details{ \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. } } \value{ each function returns a numeric vector of the statistics, one for each column } \seealso{ \code{\link{rollStats}} } \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/timeSeries-deprecated.Rd0000644000176200001440000000325114650724115017642 0ustar liggesusers\name{timeSeries-deprecated} \alias{seriesData} % removed \alias{removeNA} \alias{substituteNA} \alias{interpNA} \title{Deprecated functions in 'timeSeries' package} \usage{ removeNA(x, \dots) substituteNA(x, type = c("zeros", "mean", "median"), \dots) interpNA(x, method = c("linear", "before", "after"), \dots) } \arguments{ \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{method}{ for \code{interpNA}, how to interpolate the matrix column by column, see Section \sQuote{Details}. } \item{type}{ Three alternative methods are provided to remove NAs from the data: \code{type="zeros"} replaces the missing values with zeros, \code{type="mean"} replaces the missing values with the column mean, \code{type="median"} replaces the missing values with the column median. } \item{\dots}{ arguments to be passed to the function \code{as.matrix}. } } \description{ \tabular{ll}{ \code{seriesData} (removed) extracts data slot from a 'timeSeries'. use \code{\link{as.matrix}} instead. \cr % \item{object}{ % [is][seriesData][seriesPositions][show][summary] - % an object of class \code{timeSeries}. % } % \code{.time.timeSeries} \tab Extracts the time(s) from a % 'timeSeries', use \code{\link{time}} instead. \cr % \code{seriesPositions} \tab Extracts positions slot from a 'timeSeries', \cr % \code{newPositions<-} \tab Modifies positions of a 'timeSeries' object, \cr } } \keyword{internal} timeSeries/man/fin-monthly.Rd0000644000176200001440000000574014650724114015703 0ustar liggesusers\name{monthly} \alias{monthly} \alias{countMonthlyRecords} \alias{rollMonthlyWindows} \alias{rollMonthlySeries} \title{Special monthly series} \description{ Functions and methods dealing with special monthly \code{"timeSeries"} objects. } \usage{ rollMonthlyWindows(x, period = "12m", by = "1m") rollMonthlySeries(x, period = "12m", by = "1m", FUN, \dots) countMonthlyRecords(x) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{period,by}{ character strings specifying the rollling period composed by the length of the period and its unit. Examples: \code{"3m"}, \code{"6m"}, \code{"12m"}, and \code{"24m"} represent quarterly, semi-annual, annual and bi-annual shifts, respectively. It is the responsibility of the user to determine proper start of the series. } \item{FUN}{ the function for the statistic to be applied. For example, \code{colMean} in the case of aggregation. } \item{\dots}{ arguments passed to the function \code{FUN}. } } \details{ \code{rollMonthlySeries} computes the statistics defined by the function \code{FUN} over rolling windows, internally computed by the function \code{rollMonthlyWindows}. Note, the periods may be overlapping, may be dense, or even may have gaps. \code{countMonthlyRecords} computes a \code{"timeSeries"} that holds the number of records for each month, see examples. The dates are set to the end of the month. \code{rollMonthlyWindows} computes start and end dates for rolling time windows. Argument \code{period} specifies the length of the periods over which \code{FUN} is applied, while \code{by} gives the amount by which the window is shifted. Non-overlapping windows correspond to \code{by >= period}. } \value{ for \code{countMonthlyRecords} and \code{rollMonthlySeries}, a \code{"timeSeries"} object. for \code{rollMonthlyWindows}, a list with attribute \code{"control"} keeping the \code{start} and \code{end} dates of the series. The components of the list are: \item{from}{an object from class \code{"timeDate"}.} \item{to}{an object from class \code{"timeDate"}.} } \seealso{ \code{\link{isMonthly}}, \code{\link{isRegular}} } \examples{ ## load Microsoft daily dataset x <- MSFT ## count monthly records head(x) # 3 obs. for Sep 2000 counts <- countMonthlyRecords(x) counts ## diy computation of the counts diy <- rollMonthlySeries(x[ , 1], period = "1m", by = "1m", FUN = NROW) ## difference is only in some attributes (e.g. column names) all.equal(diy, counts) ## quaterly non-overlapping time periods windows <- rollMonthlyWindows(counts[-1, ], period = "3m", by = "3m") windows ## nicely print results as a data.frame, each row is a time window 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/stats-window.Rd0000644000176200001440000000171414650724115016100 0ustar liggesusers\name{window} \alias{window} \alias{window,timeSeries-method} \alias{window.timeSeries} \title{Methods for 'window' in package 'timeSeries'} \description{ Extract a part from a \code{"timeSeries"} object. } \usage{ \method{window}{timeSeries}(x, start, end, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{start, end}{ starting date and end date, \code{end} must be after \code{start}. } \item{\dots}{ arguments passed to other methods. } } \details{ \code{window} extracts the subset of the \code{"timeSeries"} object \code{x} observed between the times \code{start} and \code{end}. } \seealso{ \code{\link[=head.timeSeries]{head}}, \code{\link{outlier}} } \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/methods-is.Rd0000644000176200001440000000131514436112162015501 0ustar liggesusers\name{is.timeSeries} \alias{is.timeSeries} \alias{is.signalSeries} \title{Check if an object is from class 'timeSeries'} \description{ \code{is.timeSeries} tests if its argument is a \code{timeSeries}. \code{is.signalSeries} tests if series has no timestamps. } \usage{ is.timeSeries(x) is.signalSeries(x) } \arguments{ \item{x}{ an object. } } \value{ a logical value, \code{TRUE} or \code{FALSE}. } \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/00timeSeries-package.Rd0000644000176200001440000002711714650724114017303 0ustar liggesusers\name{timeSeries-package} \docType{package} \alias{timeSeries-package} \title{Utilities and tools package} \description{ Package \pkg{timeSeries} is part of the Rmetrics suit of R packages. It provides a class, \code{timeSeries}, particularly aimed at analysis of financial data, along with many methods, functions, and utilities for statistical and financial computations on time series. } \author{ Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (), Georgi N. Boshnakov [cre, aut] Maintainer: Georgi N. Boshnakov } \details{ The following sections have not been updated for some time. } \section{timeSeries - S4 'timeSeries' Class}{ % \code{getDataPart, series} \tab ... \cr \tabular{ll}{ \code{\link{timeSeries}} \tab Creates a \code{"timeSeries"} from scratch\cr \code{\link{series}}, coredata \tab Extracts the data \cr \code{\link{getUnits}} \tab Extracts the time serie units \cr \code{\link{time}} \tab Extracts the positions of timestamps \cr \code{x@format} \tab Extracts the format of the timestamp \cr \code{\link{finCenter}} \tab Extracts the financial center \cr \code{x@recordIDs} \tab Extracts the record IDs \cr \code{x@title} \tab Extracts the title \cr \code{x@documentation} \tab Extracts the documentation } } \section{Base Time Series Functions}{ % \code{comment} \tab ? ... \cr \tabular{ll}{ \code{\link[=apply,timeSeries-method]{apply}} \tab Applies a function to blocks of a \code{"timeSeries"} \cr \code{\link{cbind}} \tab Combines columns of two \code{"timeSeries"} objects \cr \code{\link{rbind}} \tab Combines rows of two \code{"timeSeries"} objects \cr \code{\link{diff}} \tab Returns differences of a \code{"timeSeries"} object \cr \code{\link[=dim,timeSeries-method]{dim}} \tab returns dimensions of a \code{"timeSeries"} object \cr \code{\link{merge}} \tab Merges two \code{"timeSeries"} objects \cr \code{\link[=rank,timeSeries-method]{rank}} \tab Returns sample ranks of a \code{"timeSeries"} object \cr \code{\link[=apply,timeSeries-method]{rev}} \tab Reverts a \code{"timeSeries"} object \cr \code{\link{sample}} \tab Resamples a \code{"timeSeries"} object \cr \code{\link{scale}} \tab Scales a \code{"timeSeries"} object \cr \code{\link[=sort.timeSeries]{sort}} \tab Sorts a \code{"timeSeries"} object \cr \code{\link[=start.timeSeries]{start}} \tab Returns start date/time of a \code{"timeSeries"} \cr \code{\link[=end.timeSeries]{end}} \tab Returns end date/time of a \code{"timeSeries"} \cr \code{\link[=apply,timeSeries-method]{end}} \tab Returns end date/time of a \code{"timeSeries"} \cr \code{\link[=t,timeSeries-method]{t}} \tab Returns the transpose of a \code{"timeSeries"} object \cr \code{\link[=attach,timeSeries-method]{attach}} \tab Attaches a \code{"timeSeries"} to the search path } } \section{Subsetting 'timeSeries' Objects}{ % \code{.subset_} \tab Subsets \code{"timeSeries"} objects \cr % \code{.findIndex} \tab Index search in a \code{"timeSeries"} object \cr \tabular{ll}{ \code{[} \tab Subsets a \code{"timeSeries"} object \cr \code{[<-} \tab Assigns values to a subset \cr \code{$} \tab Subsets a \code{"timeSeries"} by column names \cr \code{$<-} \tab Replaces subset by column names \cr \code{\link[=head.timeSeries]{head}} \tab Returns the head of a \code{"timeSeries"} \cr \code{\link[=tail.timeSeries]{tail}} \tab Returns the tail of a time Series \cr \code{\link{na.omit}} \tab Handles NAs in a \code{"timeSeries"} object \cr \code{\link{removeNA}} \tab removes NAs from a matrix object \cr \code{\link{substituteNA}} \tab substitutes NAs by zero, column mean or median \cr \code{\link{interpNA}} \tab interpolates NAs using R's "approx" function } } \section{Mathematical Operation}{ \tabular{ll}{ \code{\link[=Ops,timeSeries,timeSeries-method]{Ops}} \tab S4: Arith method for a \code{"timeSeries"} object \cr \code{\link[=Math,timeSeries-method]{Math}} \tab S4: Math method for a \code{"timeSeries"} object \cr \code{\link[=Math2,timeSeries-method]{Math2}} \tab S4: Maths method for a \code{"timeSeries"} object \cr \code{abs} \tab Returns absolute values of a \code{"timeSeries"} object \cr \code{sqrt} \tab Returns square root of a \code{"timeSeries"} object \cr \code{exp} \tab Returns the exponential values of a \code{"timeSeries"} object \cr \code{log} \tab Returns the logarithm of a \code{"timeSeries"} object \cr \code{sign} \tab Returns the signs of a \code{"timeSeries"} object \cr \code{\link{diff}} \tab Differences a \code{"timeSeries"} object \cr \code{\link{scale}} \tab Centers and/or scales a \code{"timeSeries"} object \cr \code{\link[=quantile.timeSeries]{quantile}} \tab Returns quantiles of an univariate \code{"timeSeries"}} } \section{Methods}{ \tabular{ll}{ \code{\link{as.timeSeries}} \tab Defines method for a \code{"timeSeries"} \cr \code{as.*.default} \tab Returns the input \cr \code{as.*.ts} \tab Transforma a 'ts' object into a \code{"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 \code{"timeSeries"} \cr \code{as.vector.*} \tab Converts univariate \code{"timeSeries"} to vector \cr \code{as.matrix.*} \tab Converts \code{"timeSeries"} to matrix \cr \code{as.numeric.*} \tab Converts \code{"timeSeries"} to numeric \cr \code{as.data.frame.*} \tab Converts \code{"timeSeries"} to data.frame \cr \code{as.ts.*} \tab Converts \code{"timeSeries"} to ts \cr \code{as.logical.*} \tab Converts \code{"timeSeries"} to logical \cr \code{\link{is.timeSeries}} \tab Tests for a \code{"timeSeries"} object \cr \code{\link[=plot,timeSeries-method]{plot}} \tab Displays a X-Y \code{"timeSeries"} Plot \cr \code{\link[=lines,timeSeries-method]{lines}} \tab Adds connected line segments to a plot \cr \code{\link[=points,timeSeries-method]{points}} \tab Adds Points to a plot \cr \code{\link[=show,timeSeries-method]{show}} \tab Prints a 'timeSeries oobject} } \section{Financial time series functions}{ \tabular{ll}{ \code{\link{align}} \tab Aligns a \code{"timeSeries"} to time stamps \cr \code{\link{cumulated}} \tab Computes cumulated series from a returns \cr \code{\link{alignDailySeries}} \tab Aligns a \code{"timeSeries"} to calendarical dates \cr \code{\link{rollDailySeries}} \tab Rolls a 'timeSeries daily\cr \code{\link{drawdowns}} \tab Computes series of drawdowns from financial returns \cr \code{\link{drawdownsStats}} \tab Computes drawdowns statistics \cr \code{\link{durations}} \tab Computes durations from a financial time series \cr \code{\link{countMonthlyRecords}} \tab Counts monthly records in a \code{"timeSeries"} \cr \code{\link{rollMonthlyWindows}} \tab Rolls Monthly windows \cr \code{\link{rollMonthlySeries}} \tab Rolls a \code{"timeSeries"} monthly \cr \code{\link{endOfPeriodSeries}} \tab Returns end of periodical series \cr \code{\link{endOfPeriodStats}} \tab Returns end of period statistics \cr \code{\link{endOfPeriodBenchmarks}} \tab Returns period benchmarks \cr \code{\link{returns}} \tab Computes returns from prices or indexes \cr \code{\link{returns0}} \tab Computes untrimmed returns from prices or indexes \cr \code{\link{runlengths}} \tab Computes run lenghts of a \code{"timeSeries"} \cr \code{\link{smoothLowess}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{smoothSpline}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{smoothSupsmu}} \tab Smoothes a \code{"timeSeries"} \cr \code{\link{splits}} \tab Detects \code{"timeSeries"} splits by outlier detection \cr \code{\link{spreads}} \tab Computes spreads from a price/index stream \cr \code{\link{turns}} \tab Computes turning points in a \code{"timeSeries"} object \cr \code{\link{turnsStats}} \tab Computes turning points statistics } } \section{Statistics Time Series functions}{ \tabular{ll}{ \code{\link{colCumsums}} \tab Computes cumulated column sums of a \code{"timeSeries"} \cr \code{\link{colCummaxs}} \tab Computes cumulated maximum of a \code{"timeSeries"} \cr \code{\link{colCummins}} \tab Computes cumulated minimum of a \code{"timeSeries"} \cr \code{\link{colCumprods}} \tab Computes cumulated pruduct values by column \cr \code{\link{colCumreturns}} \tab Computes cumulated returns by column \cr \code{\link[=colSums,timeSeries-method]{colSums}} \tab Computes sums of all values in each column \cr \code{\link[=colMeans,timeSeries-method]{colMeans}} \tab Computes means of all values in each column \cr \code{\link{colSds}} \tab Computes standard deviations of all values in each column \cr \code{\link{colVars}} \tab Computes variances of all values in each column \cr \code{\link{colSkewness}} \tab Computes skewness of all values in each column \cr \code{\link{colKurtosis}} \tab Computes kurtosis of all values in each column \cr \code{\link{colMaxs}} \tab Computes maxima of all values in each column \cr \code{\link{colMins}} \tab Computes minima of all values in each column \cr \code{\link{colProds}} \tab Computes products of all values in each column \cr \code{\link{colStats}} \tab Computes statistics of all values in each column \cr \code{\link{orderColnames}} \tab Returns ordered column names of a \code{"timeSeries"} \cr \code{\link{sortColnames}} \tab Returns alphabetically sorted column names \cr \code{\link{sampleColnames}} \tab Returns sampled column names of a \code{"timeSeries"} \cr \code{\link{pcaColnames}} \tab Returns PCA correlation ordered column names \cr \code{\link{hclustColnames}} \tab Returns hierarchically clustered columnames \cr \code{\link{statsColnames}} \tab Returns statisticall rearrange columnames \cr \code{\link{orderStatistics}} \tab Computes order statistics of a \code{"timeSeries"} object \cr \code{\link{rollMean}} \tab Computes rolling means of a \code{"timeSeries"} object \cr \code{\link{rollMin}} \tab Computes rolling minima of a \code{"timeSeries"} object \cr \code{\link{rollMax}} \tab Computes rolling maxima of a \code{"timeSeries"} object \cr \code{\link{rollMedian}} \tab Computes rolling medians of a \code{"timeSeries"} object \cr \code{\link{rollStats}} \tab Computes rolling statistics of a \code{"timeSeries"} objectcr \cr \code{\link{rowCumsums}} \tab Computes cumulated column sums of a \code{"timeSeries"} \cr \code{\link{smoothLowess}} \tab Smoothes a series with lowess function \cr \code{\link{smoothSupsmu}} \tab Smoothes a series with supsmu function \cr \code{\link{smoothSpline}} \tab Smoothes a series with smooth.spline function } } \section{Misc Functions}{ \tabular{ll}{ \code{\link{dummyDailySeries}} \tab Creates a dummy daily \code{"timeSeries"} object \cr \code{\link[=isMonthly,timeSeries-method]{isMonthly}} \tab Decides if the series consists of monthly records \cr \code{\link[=isDaily,timeSeries-method]{isDaily}} \tab Decides if the series consists of daily records \cr \code{\link[=isQuarterly,timeSeries-method]{isQuarterly}} \tab Decides if the series consists of Quarterly records \cr \code{\link{description}} \tab Creates default description string % \code{\link{getArgs}} \tab Extracts arguments from a S4 method } } \keyword{package} \keyword{ts} timeSeries/man/base-attach.Rd0000644000176200001440000000350214436342653015613 0ustar liggesusers\name{attach} \alias{attach} \alias{attach,timeSeries-method} \title{Attach a 'timeSeries' to the search path} \description{ Attaches a \code{"timeSeries"} object to the search path. } \usage{ \S4method{attach}{timeSeries}(what, pos = 2, name = deparse(substitute(what)), warn.conflicts = TRUE) } \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}{ database to be attached. This may currently be a \code{"timeSeries"} object, a data.frame, a list, an R data file created with \code{save}, \code{NULL}, or an environment. See for details \code{help(attach, package = base)}. } } \value{ the environment, 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 \code{"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/fin-runlengths.Rd0000644000176200001440000000164614436440633016406 0ustar liggesusers\name{runlengths} \alias{runlengths} \title{Runlengths of a time series} \description{ Computes runlengths of an univariate \code{"timeSeries"} object. } \usage{ runlengths(x, \dots) } \arguments{ \item{x}{ an univariate time series of class \code{"timeSeries"}. } \item{\dots}{ arguments passed to the function \code{na.omit}. } } \details{ Runlengths are defined here as contiguous sequences of values having the same sign. Zeroes are treated as \code{NA}s. } \value{ 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) ## replace the middle value of the negative stretch of 3 values tS[5] <- NA ## the two negative values separated by NA are still one run runlengths(tS) } \keyword{chron} timeSeries/man/statistics-orderColnames.Rd0000644000176200001440000001023114435063651020416 0ustar liggesusers\name{orderColnames} \alias{orderColnames} \alias{sortColnames} \alias{sampleColnames} \alias{statsColnames} \alias{pcaColnames} \alias{hclustColnames} \title{Reorder column names of a time series} \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{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{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{\link[stats]{dist}}, and the second determines the choice of the agglomeration method, see \code{\link[stats]{hclust}}. } \item{robust}{ a logical flag which indicates if robust correlations should be used. } \item{\dots}{ further arguments to be passed to the underlying functions doing the main work, see section \sQuote{Details}. } } \details{ These functions reorder the column names of a \code{"timeSeries"} object according to some statistical measure. \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 help page \code{colStats} but custom functions can be used that compute for example risk or any other statistical measure. The \code{\dots} argument allows to pass additional arguments to the underlying function \code{FUN}. \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{ for \code{orderColnames}, an integer vector representing the permutaion that will sort the column names, for the other functions, a character vector giving 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/stats-na.contiguous.Rd0000644000176200001440000000264614650724114017371 0ustar liggesusers\name{na.contiguous} \alias{na.contiguous} \alias{na.contiguous.timeSeries} \alias{is.na} \alias{is.na,timeSeries-method} \title{Find longest contiguous stretch of non-NAs or check for NAs} \description{ Find the longest consecutive stretch of non-missing values in a \code{"timeSeries"} object. In the event of a tie, the first such stretch. Also, \code{"timeSeries"} method for \code{is.na}. } \usage{ \method{na.contiguous}{timeSeries}(object, ...) \S4method{is.na}{timeSeries}(x) } \arguments{ \item{object,x}{ a \code{"timeSeries"} object. } \item{\dots}{ further arguments passed to other methods. } } \value{ for the \code{na.contiguous} method, a \code{"timeSeries"} object without missing values, for the \code{is.na} method, a \code{"timeSeries"} object whose data part is a logical matrix of the same dimension as in \code{x} indicating if the corresponding values are \code{NA} or not. } \examples{ ## Dummy 'timeSeries' containing NAs \dontshow{set.seed(2023)} data <- matrix(sample(c(1:20, rep(NA,4))), ncol = 2) s <- timeSeries(data, timeCalendar(2023)) is.na(s) ## Find the longest consecutive non-missing values na.contiguous(s) ## tied longest stretches: 1:3, 6:9 and 10:12 x <- c(1:3, NA, NA, 6:8, NA, 10:12) ## should return the 1st one na.contiguous(x) # correct for R > 4.3.0 na.contiguous(timeSeries(x)) # correct for timeSeries version > 4030.106 } timeSeries/man/statistics-rowCumsums.Rd0000644000176200001440000000161114436050534020004 0ustar liggesusers\name{rowCum} \alias{rowCum} \alias{rowCumsums} \alias{rowCumsums,ANY-method} \alias{rowCumsums,timeSeries-method} \title{Cumulative row statistics} \description{ Compute cumulative row statistics. } \usage{ \S4method{rowCumsums}{ANY}(x, na.rm = FALSE, \dots) \S4method{rowCumsums}{timeSeries}(x, na.rm = FALSE, \dots) } \arguments{ \item{x}{ a time series, may be an object of class \code{"matrix"} or \code{"timeSeries"}. } \item{na.rm}{ a logical. Should missing values be removed? } \item{\dots}{ arguments to be passed. } } \value{ for the default method, a matrix, for the \code{"timeSeries"} method, an S4 object of class \code{"timeSeries"}. } \seealso{ \code{\link[=colCum]{colCumXXX}} } \examples{ ## Simulated Monthly Return Data - X = matrix(rnorm(24), ncol = 2) ## Compute cumulated Sums - rowCumsums(X) } \keyword{univar} timeSeries/man/fin-dummy.Rd0000644000176200001440000000260214436046541015341 0ustar liggesusers\name{dummyTimeSeries} \alias{dummyTimeSeries} \alias{dummyDailySeries} \alias{dummyMonthlySeries} \alias{dummySeries} % deprecated; now removed \title{Create dummy time series} \description{ Create dummy daily and monthly time series for examples and exploration. } \usage{ dummyDailySeries(x = rnorm(365), units = NULL, zone = "", FinCenter = "") dummyMonthlySeries(\dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \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{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{...}{ optional arguments passed to \code{timeSeries}. } } \details{ \code{dummyDailySeries} creates a \code{timeSeries} object with dummy daily dates from a numeric matrix with daily records of unknown dates. \code{dummyMonthlySeries} creates a dummy monthly \code{"timeSeries"} object. } \value{ a \code{"timeSeries"} object } \examples{ dd <- dummyDailySeries() head(dd) tail(dd) dummyMonthlySeries(y = 2022) } \keyword{chron} \keyword{ts} timeSeries/man/statistics-colCumsums.Rd0000644000176200001440000000433414436326462017765 0ustar liggesusers\name{colCum} \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} \title{Cumulated column statistics} \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{x}{ a time series, may be an object of class \code{"matrix"}, or \code{"timeSeries"}. } \item{na.rm}{ a logical. Should missing values be removed? } \item{method}{ a character string to indicate if geometric (\code{TRUE}) or simple (\code{FALSE}) returns should be computed. } \item{\dots}{ arguments to be passed. } } \details{ These functions compute the requested cumulative quantities columnwise to obtain a matrix of the same dimension as the data. The \code{"timeSeries"} methods replace the data part of the original object with the resulting matrix. The \code{"timeSeries"} methods for the \code{Math} group functions \code{cummin}, \code{cummax}, \code{cumsum}, and \code{cumprod}, work similarly but don't have the \code{na.rm} argument. } \value{ \code{"matrix"} for the default methods of all functions, \code{"timeSeries"} for the \code{"timeSeries"} methods } \seealso{ \code{\link{Math,timeSeries-method}}, \code{\link{rowCumsums}} } \examples{ ## simulate return data x <- matrix(rnorm(24), ncol = 2) X <- as.timeSeries(x) ## cumulative sums by column - class(colCumsums(x)) # "matrix" class(colCumsums(X)) # "timeSeries" colCumsums(X) } \keyword{univar} timeSeries/man/base-sample.Rd0000644000176200001440000000301214436343200015611 0ustar liggesusers\name{sample} \alias{sample} \alias{sample,timeSeries-method} \title{Resample 'timeSeries' objects} \description{ Takes a sample of the specified size from the elements of a \code{"timeSeries"}. } \usage{ \S4method{sample}{timeSeries}(x, size, replace = FALSE, prob = NULL) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{size}{ a non-negative integer giving the number of items to choose. } \item{replace}{ sample with replacement if \code{TRUE}, otherwise without replacement. } \item{prob}{ a vector of probability weights for obtaining the elements of the vector being sampled. } } \details{ The function takes a sample of size \code{size} from the elements of the time series with or without replacement depending on argument \code{replace}. The result is returned as a \code{"timeSeries"} object. For details about the arguments see the documentation of \code{base:sample}. } \value{ an object from class \code{"timeSeries"} } \seealso{ \code{\link[base]{sample}} (\code{sample} in base \R), \code{\link[timeDate]{sample}} (the \code{"timeDate"} method) } \examples{ ## Monthly Calendar Series - x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] \dontshow{set.seed(1234)} ## Resample the Series with respect to the time stamps - resampled <- sample(x) resampled is.unsorted(resampled) } \keyword{chron} timeSeries/man/statistics-orderStatistics.Rd0000644000176200001440000000164614436046244021021 0ustar liggesusers\name{orderStatistics} \alias{orderStatistics} \title{Order statistics} \description{ Computes the order statistics of a \code{"timeSeries"} object. } \usage{ orderStatistics(x) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } } \details{ \code{orderStatistics} computes the order statistics for each column of a \code{"timeSeries"} object. The output is a named list with the order statistics for each column in a separate component. } \value{ a named list, in which each component is an univariate \code{"timeSeries"} containing the order statistics of the corresponding column of the input time series. } \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/base-apply.Rd0000644000176200001440000001430214435656374015503 0ustar liggesusers\name{apply} \alias{apply} \alias{apply,timeSeries-method} \alias{fapply} \alias{applySeries} \alias{rollDailySeries} \title{Apply functions over time windows} \description{ Applies a function to a \code{"timeSeries"} object over regular or irregular time windows, possibly overlapping. } \usage{ \S4method{apply}{timeSeries}(X, MARGIN, FUN, \dots, simplify = TRUE) 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) rollDailySeries(x, period = "7d", FUN, \dots) } \arguments{ \item{x,X}{ an object of class \code{timeSeries}. } \item{MARGIN}{ a vector giving the subscripts which the function will be applied over, see base R's \code{\link[base]{apply}}. } \item{FUN}{ the function to be applied. For the function \code{applySeries} the default setting is \code{FUN = colMeans}. } \item{simplify}{ simplify the result? } \item{from, to}{ starting date and end date as \code{"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{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 input's data name is deparsed. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{period}{ 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{\dots}{ arguments passed to other methods. } } \details{ The \code{"timeSeries"} method for \code{apply} extracts the core data (a matrix) from \code{X} and calls \code{apply}, passing on all the remaining arguments. If the result is suitable, it converts it to \code{"timeSeries"}, otherwise returns it as is. \sQuote{Suitable} here means that it is a matrix or a vector (which is converted to a matrix) and the number of observations is the same as \code{X}. 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 \dQuote{f} in front of the function name) time series of class \code{"timeSeries"}. \code{applySeries} takes a \code{"timeSeries"} object as input and applies \code{FUN} to windows of \code{x}. The windows are specified by \code{from} and \code{to}, which need to have the same length. Then \code{from[i], to[i]} specifies the \code{i}-th window. If \code{time(x)} is a \code{"timeDate"} object, then \code{from} and \code{to} are converted to \code{"timeDate"} (if they are not already such objects), otherwise they are converted to integers. An alternative way to specify the window(s) on which \code{applySeries} operates is with argument \code{by}. It is used only if \code{from} and \code{to} are missing or \code{NULL}. \code{by = "monthly"} or \code{by = "quarterly"} applies \code{FUN} to the data for each year-month or year-quarter, respectively. By year-month we mean that there are separate windows for the months in different years. The resulting time stamps are the time stamps of the \code{to} vector. The periods can be regular or irregular, and they can even overlap. If \code{from = start(x)} and \code{to = end(x)}, then the function behaves like \code{apply} on the column margin. \code{fapply} is the same as \code{applySeries} (in fact, the former calls the latter), except that the defaults for \code{from} and \code{to} are \code{start(x)} and \code{end(x)}, respectively. (GNB: in addition, \code{fapply} throws error if \code{x} is a \sQuote{signal series}.) \code{rollDailySeries} rolls a daily 'timeSeries' on a given period. } \value{ for \code{rollDailySeries}, an object of class \code{"timeSeries"} with rolling values, computed from the function \code{FUN}. } \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) ## Alternative Use - fapply(LPP, from, to, FUN = colSums) ## Count Trading Days per Month - colCounts <- function(x) rep(NROW(x), times = NCOL(x)) applySeries(LPP, FUN = colCounts, by = "monthly") ## TODO: examples for rollDailySeries() } \keyword{chron} \keyword{ts} timeSeries/man/timeSeries-isUnivariate.Rd0000644000176200001440000000166214436147016020212 0ustar liggesusers\name{isUnivariate} \alias{isUnivariate} \alias{isMultivariate} \title{Checks if a time series is univariate} \description{ Checks if a time series object 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. } } \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. } \value{ a logical value } \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/stats-filter.Rd0000644000176200001440000000266514436343667016077 0ustar liggesusers\name{filter} \alias{filter} \alias{filter,timeSeries-method} \title{Linear filtering on a time series} \description{ Applies linear filtering to a univariate \code{"timeSeries"}. } \usage{ \S4method{filter}{timeSeries}(x, filter, method = c("convolution", "recursive"), sides = 2, circular = FALSE, init = NULL) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{filter}{ coefficients of the filter. } \item{method}{ \code{"convolution"} or \code{"recursive"}. } \item{sides,circular}{ for convolution filters only. Onesided if \code{sides = 1}, centred around lag 0 if \code{sides = 2}. Circular if \code{circular = TRUE.} } \item{init}{ for recursive filters only. Values before the start of the time series. } } \details{ \code{filter} is a generic function with default method \code{stats::filter}. The method for \code{"timeSeries"} is a wrapper for the latter. See \code{?stats::filter} for details about the arguments. } \value{ a \code{"timeSeries"} object } \seealso{ base R function \code{\link[stats]{filter}} } \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/fin-cumulated.Rd0000644000176200001440000000324014436327735016177 0ustar liggesusers\name{cumulated} \alias{cumulated} \alias{cumulated.default} \title{Cumulated time series from returns} \description{ Computes a cumulated financial \code{"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{x}{ an object of class \code{timeSeries}. } \item{method}{ a character string, the method for computation of returns. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \item{\dots}{ ignored by the default method. } } \details{ Note, the function \code{cumulated} assumes as input discrete returns from a price or index series. Only then the cumulated 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{ a \code{"timeSeries"} object } \seealso{ \code{\link{returns}}, %\code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \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/fin-returns.Rd0000644000176200001440000000460114436327673015721 0ustar liggesusers\name{returns} \alias{returns} \alias{returns,ANY-method} \alias{returns,timeSeries-method} \alias{returns0} \alias{returnSeries} \alias{getReturns} \title{Financial returns} \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) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{method}{ a character string. Which method should be used to compute the returns, one of "continuous", "discrete", or "compound", "simple". The second pair of methods is a synonym for the first two methods. } \item{percentage}{ a logical value. By default \code{FALSE}, if \code{TRUE} the series will be expressed in percentage changes. } \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{ an object of class \code{timeSeries}. \code{returns0} returns an untrimmed series with the first row of returns set to zero(s). } \note{ The functions \code{returnSeries} and \code{getReturns} will be removed in the near future. They are synonyms for the function \code{returns} and their use was discouraged for many years. Just use \code{returns}. The function \code{returnSeries} is no longer exported. \code{getReturns} is exported only because we are waiting for a package on CRAN to be updated. } \seealso{ %\code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \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/fin-wealth.Rd0000644000176200001440000000142614436330754015477 0ustar liggesusers\name{wealth} \alias{index2wealth} \title{Conversion of an index to wealth} \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. } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, %\code{\link{index2wealth}} } \examples{ ## Load MSFT Open Prices - INDEX <- MSFT[1:20, 1] INDEX ## Compute Wealth Normalized to 100 - 100 * index2wealth(INDEX) } \keyword{chron} timeSeries/man/timeSeries-class.Rd0000644000176200001440000004421514650724115016654 0ustar liggesusers\name{timeSeries-class} \Rdversion{1.1} \docType{class} \alias{timeSeries-class} % \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,missing-method} % \alias{[,timeSeries,missing,character-method} % \alias{[,timeSeries,missing,index_timeSeries-method} % \alias{[,timeSeries,missing,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,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,character,ANY-method} % \alias{[<-,timeSeries,character,missing-method} % \alias{[<-,timeSeries,timeDate,ANY-method} % \alias{[<-,timeSeries,timeDate,missing-method} % \alias{$,timeSeries-method} % \alias{$<-,timeSeries,ANY-method} % \alias{$<-,timeSeries,factor-method} % \alias{$<-,timeSeries,numeric-method} % \alias{aggregate,timeSeries-method} % \alias{align,timeSeries-method} % \alias{apply,timeSeries-method} % \alias{as.data.frame,timeSeries-method} % \alias{as.list,timeSeries-method} % \alias{as.matrix,timeSeries-method} % \alias{as.ts,timeSeries-method} % \alias{attach,timeSeries-method} % \alias{cbind2,ANY,timeSeries-method} % \alias{cbind2,timeSeries,ANY-method} % \alias{cbind2,timeSeries,missing-method} % \alias{cbind2,timeSeries,timeSeries-method} % \alias{coerce,ANY,timeSeries-method} % \alias{coerce,character,timeSeries-method} % \alias{coerce,data.frame,timeSeries-method} % \alias{coerce,timeSeries,data.frame-method} % \alias{coerce,timeSeries,list-method} % \alias{coerce,timeSeries,matrix-method} % \alias{coerce,timeSeries,ts-method} % \alias{coerce,ts,timeSeries-method} % \alias{colCummaxs,timeSeries-method} % \alias{colCummins,timeSeries-method} % \alias{colCumprods,timeSeries-method} % \alias{colCumreturns,timeSeries-method} % \alias{colCumsums,timeSeries-method} % \alias{colMeans,timeSeries-method} % \alias{colnames,timeSeries-method} % \alias{colnames<-,timeSeries-method} % \alias{colSums,timeSeries-method} % \alias{comment,timeSeries-method} % \alias{comment<-,timeSeries-method} % \alias{coredata,timeSeries-method} % \alias{coredata<-,timeSeries,ANY-method} % \alias{coredata<-,timeSeries,matrix-method} % \alias{cummax,timeSeries-method} % \alias{cummin,timeSeries-method} % \alias{cumprod,timeSeries-method} % \alias{cumsum,timeSeries-method} % \alias{diff,timeSeries-method} % \alias{dim,timeSeries-method} % \alias{dim<-,timeSeries-method} % \alias{dimnames,timeSeries-method} % \alias{dimnames<-,timeSeries,list-method} % \alias{end,timeSeries-method} % \alias{filter,timeSeries-method} % \alias{finCenter,timeSeries-method} % \alias{finCenter<-,timeSeries-method} % \alias{frequency,timeSeries-method} % \alias{getDataPart,timeSeries-method} % \alias{head,timeSeries-method} \alias{initialize,timeSeries-method} % \alias{is.na,timeSeries-method} % \alias{is.unsorted,timeSeries-method} % \alias{isDaily,timeSeries-method} % \alias{isMonthly,timeSeries-method} % \alias{isQuarterly,timeSeries-method} % \alias{isRegular,timeSeries-method} % \alias{lag,timeSeries-method} % \alias{lines,timeSeries-method} % \alias{median,timeSeries-method} % \alias{merge,ANY,timeSeries-method} % \alias{merge,matrix,timeSeries-method} % \alias{merge,numeric,timeSeries-method} % \alias{merge,timeSeries,ANY-method} % \alias{merge,timeSeries,matrix-method} % \alias{merge,timeSeries,missing-method} % \alias{merge,timeSeries,numeric-method} % \alias{merge,timeSeries,timeSeries-method} % \alias{na.contiguous,timeSeries-method} % \alias{na.omit,timeSeries-method} % \alias{names,timeSeries-method} % \alias{names<-,timeSeries-method} % \alias{Ops,array,timeSeries-method} % \alias{Ops,timeSeries,array-method} % \alias{Ops,timeSeries,timeSeries-method} % \alias{Ops,timeSeries,ts-method} % \alias{Ops,timeSeries,vector-method} % \alias{Ops,ts,timeSeries-method} % \alias{Ops,vector,timeSeries-method} % \alias{outlier,timeSeries-method} % \alias{plot,timeSeries-method} % \alias{points,timeSeries-method} % \alias{print,timeSeries-method} % \alias{quantile,timeSeries-method} % \alias{rank,timeSeries-method} % \alias{rbind2,ANY,timeSeries-method} % \alias{rbind2,timeSeries,ANY-method} % \alias{rbind2,timeSeries,missing-method} % \alias{rbind2,timeSeries,timeSeries-method} % \alias{returns,timeSeries-method} % \alias{rev,timeSeries-method} % \alias{rowCumsums,timeSeries-method} % \alias{rownames,timeSeries-method} % \alias{rownames<-,timeSeries,ANY-method} % \alias{rownames<-,timeSeries,timeDate-method} % \alias{sample,timeSeries-method} % \alias{scale,timeSeries-method} % \alias{series,timeSeries-method} % \alias{series<-,timeSeries,ANY-method} % \alias{series<-,timeSeries,matrix-method} % \alias{setDataPart,timeSeries-method} % \alias{show,timeSeries-method} % \alias{sort,timeSeries-method} % \alias{start,timeSeries-method} % \alias{str,timeSeries-method} % \alias{t,timeSeries-method} % \alias{tail,timeSeries-method} % \alias{time,timeSeries-method} % \alias{window,timeSeries-method} \title{Class 'timeSeries' in package timeSeries} \description{ Class \code{"timeSeries"} in package timeSeries. } \section{Objects from the Class}{ The main functions for creating objects from class \code{"timeSeries"} \code{\link{timeSeries}} and \code{\link{as.timeSeries}}. Objects can also be created by calls of the form \code{new("timeSeries", .Data, units, positions, format, FinCenter, recordIDs, title, documentation)} but this is not recommended for routine work. } \section{Slots}{ The structure of the \code{"timeSeries"} objects should, in general, be considered internal. The accessor functions to get and set the components should be used to get and set values of the slots. \describe{ \item{\code{.Data}:}{ Object of class \code{"matrix"} containing the data, one column for each variable. } \item{\code{units}:}{ Object of class \code{"character"}, the unit (or variable, or column) names of the time series object. } \item{\code{positions}:}{ Object of class \code{"numeric"}, the datetime stamps. If the time series doesn't have datetime stamps, then \code{positions} is of length zero. } \item{\code{format}:}{ Object of class \code{"character"}, a datetime format (such as \code{"\%Y-\%m-\%d"}). if there are no time stamps \code{"format"} is equal to \code{"counts"}. } \item{\code{FinCenter}:}{ Object of class \code{"character"}, the financial center. } \item{\code{recordIDs}:}{ Object of class \code{"data.frame"} ~~ } \item{\code{title}:}{ Object of class \code{"character"}, a title for printing. } \item{\code{documentation}:}{ Object of class \code{"character"}, by default it is set to the current date. } } } \section{Extends}{ Class \code{"\linkS4class{structure}"}, from data part. Class \code{"\linkS4class{vector}"}, by class "structure", distance 2, with explicit coerce. } \section{Methods}{ Below is a list of the methods that have \code{"timeSeries"} in their signature. It can be useful for technical purposes, for example in reporting bugs but most methods that need explanation are documented with the corresponding functions and looking at their help pages is recommended. There are short explanations for methods for functions that are not supposed to be called directly. \describe{ \item{[}{\code{signature(x = "timeSeries", i = "ANY", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "character", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "index_timeSeries", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "matrix", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "missing", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "ANY")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "time_timeSeries", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "character")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeDate", j = "missing")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeSeries", j = "index_timeSeries")}: ... } \item{[}{\code{signature(x = "timeSeries", i = "timeSeries", j = "missing")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "character", j = "ANY")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "character", j = "missing")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "timeDate", j = "ANY")}: ... } \item{[<-}{\code{signature(x = "timeSeries", i = "timeDate", j = "missing")}: ... } \item{$}{\code{signature(x = "timeSeries")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "factor")}: ... } \item{$<-}{\code{signature(x = "timeSeries", value = "numeric")}: ... } \item{aggregate}{\code{signature(x = "timeSeries")}: ... } \item{align}{\code{signature(x = "timeSeries")}: ... } \item{apply}{\code{signature(X = "timeSeries")}: ... } \item{as.data.frame}{\code{signature(x = "timeSeries")}: ... } \item{as.list}{\code{signature(x = "timeSeries")}: ... } \item{as.matrix}{\code{signature(x = "timeSeries")}: ... } \item{as.ts}{\code{signature(x = "timeSeries")}: ... } \item{attach}{\code{signature(what = "timeSeries")}: ... } \item{cbind2}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{cbind2}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{coerce}{\code{signature(from = "ANY", to = "timeSeries")} } \item{coerce}{\code{signature(from = "character", to = "timeSeries")} } \item{coerce}{\code{signature(from = "data.frame", to = "timeSeries")} } \item{coerce}{\code{signature(from = "timeSeries", to = "data.frame")} } \item{coerce}{\code{signature(from = "timeSeries", to = "list")}: } \item{coerce}{\code{signature(from = "timeSeries", to = "matrix")} } \item{coerce}{\code{signature(from = "timeSeries", to = "ts")}: } \item{coerce}{\code{signature(from = "ts", to = "timeSeries")}: \code{coerce} should not be called directly. Use \code{as(object, "target_class")} instead. } \item{colCummaxs}{\code{signature(x = "timeSeries")}: ... } \item{colCummins}{\code{signature(x = "timeSeries")}: ... } \item{colCumprods}{\code{signature(x = "timeSeries")}: ... } \item{colCumreturns}{\code{signature(x = "timeSeries")}: ... } \item{colCumsums}{\code{signature(x = "timeSeries")}: ... } \item{colMeans}{\code{signature(x = "timeSeries")}: ... } \item{colnames}{\code{signature(x = "timeSeries")}: ... } \item{colnames<-}{\code{signature(x = "timeSeries")}: ... } \item{colSums}{\code{signature(x = "timeSeries")}: ... } \item{comment}{\code{signature(x = "timeSeries")}: ... } \item{comment<-}{\code{signature(x = "timeSeries")}: ... } \item{coredata}{\code{signature(x = "timeSeries")}: ... } \item{coredata<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{coredata<-}{\code{signature(x = "timeSeries", value = "matrix")}: ... } \item{cummax}{\code{signature(x = "timeSeries")}: ... } \item{cummin}{\code{signature(x = "timeSeries")}: ... } \item{cumprod}{\code{signature(x = "timeSeries")}: ... } \item{cumsum}{\code{signature(x = "timeSeries")}: ... } \item{diff}{\code{signature(x = "timeSeries")}: ... } \item{dim}{\code{signature(x = "timeSeries")}: ... } \item{dim<-}{\code{signature(x = "timeSeries")}: ... } \item{dimnames}{\code{signature(x = "timeSeries")}: ... } \item{dimnames<-}{\code{signature(x = "timeSeries", value = "list")}: ... } \item{end}{\code{signature(x = "timeSeries")}: ... } \item{filter}{\code{signature(x = "timeSeries")}: ... } \item{finCenter}{\code{signature(x = "timeSeries")}: ... } \item{finCenter<-}{\code{signature(x = "timeSeries")}: ... } \item{frequency}{\code{signature(x = "timeSeries")}: ... } \item{getDataPart}{\code{signature(object = "timeSeries")}: ... } \item{head}{\code{signature(x = "timeSeries")}: ... } \item{initialize}{\code{signature(.Object = "timeSeries")}: don't call \code{"initialize"}, call \code{new("timeSeries", ...)} instead. Even better, call \code{timeSeries}. } \item{is.na}{\code{signature(x = "timeSeries")}: ... } \item{is.unsorted}{\code{signature(x = "timeSeries")}: ... } \item{isDaily}{\code{signature(x = "timeSeries")}: ... } \item{isMonthly}{\code{signature(x = "timeSeries")}: ... } \item{isQuarterly}{\code{signature(x = "timeSeries")}: ... } \item{isRegular}{\code{signature(x = "timeSeries")}: ... } \item{lag}{\code{signature(x = "timeSeries")}: ... } \item{lines}{\code{signature(x = "timeSeries")}: ... } \item{median}{\code{signature(x = "timeSeries")}: ... } \item{merge}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "matrix", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "numeric", y = "timeSeries")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "matrix")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "numeric")}: ... } \item{merge}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{na.contiguous}{\code{signature(object = "timeSeries")}: ... } \item{na.omit}{\code{signature(object = "timeSeries")}: ... } \item{names}{\code{signature(x = "timeSeries")}: ... } \item{names<-}{\code{signature(x = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "array", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "array")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "ts")}: ... } \item{Ops}{\code{signature(e1 = "timeSeries", e2 = "vector")}: ... } \item{Ops}{\code{signature(e1 = "ts", e2 = "timeSeries")}: ... } \item{Ops}{\code{signature(e1 = "vector", e2 = "timeSeries")}: ... } \item{outlier}{\code{signature(x = "timeSeries")}: ... } \item{plot}{\code{signature(x = "timeSeries")}: ... } \item{points}{\code{signature(x = "timeSeries")}: ... } \item{print}{\code{signature(x = "timeSeries")}: ... } \item{quantile}{\code{signature(x = "timeSeries")}: ... } \item{rank}{\code{signature(x = "timeSeries")}: ... } \item{rbind2}{\code{signature(x = "ANY", y = "timeSeries")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "ANY")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "missing")}: ... } \item{rbind2}{\code{signature(x = "timeSeries", y = "timeSeries")}: ... } \item{returns}{\code{signature(x = "timeSeries")}: ... } \item{rev}{\code{signature(x = "timeSeries")}: ... } \item{rowCumsums}{\code{signature(x = "timeSeries")}: ... } \item{rownames}{\code{signature(x = "timeSeries")}: ... } \item{rownames<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{rownames<-}{\code{signature(x = "timeSeries", value = "timeDate")}: ... } \item{sample}{\code{signature(x = "timeSeries")}: ... } \item{scale}{\code{signature(x = "timeSeries")}: ... } \item{series}{\code{signature(x = "timeSeries")}: ... } \item{series<-}{\code{signature(x = "timeSeries", value = "ANY")}: ... } \item{series<-}{\code{signature(x = "timeSeries", value = "matrix")}: ... } \item{setDataPart}{\code{signature(object = "timeSeries")}: ... } \item{show}{\code{signature(object = "timeSeries")}: ... } \item{sort}{\code{signature(x = "timeSeries")}: ... } \item{start}{\code{signature(x = "timeSeries")}: ... } \item{str}{\code{signature(object = "timeSeries")}: ... } \item{t}{\code{signature(x = "timeSeries")}: ... } \item{tail}{\code{signature(x = "timeSeries")}: ... } \item{time}{\code{signature(x = "timeSeries")}: ... } \item{window}{\code{signature(x = "timeSeries")}: ... } } } \seealso{ \code{\link{timeSeries}} and \code{\link{as.timeSeries}} for creating and converting to \code{"timeSeries"}, \code{\link{readSeries}} for importing from a text file, \code{\link{dummyDailySeries}} for creation of dummy daily and monthly time series, \code{\link{as.matrix}}, \code{\link{time}}, \code{\link{finCenter}}, \code{\link{getUnits}}, \code{\link{dim}}, \code{\link{start}}, etc., for accessing properties of the time series. } \examples{ ## see the help page for timeSeries() showClass("timeSeries") } \keyword{classes} timeSeries/man/data-examples.Rd0000644000176200001440000000240214436334724016162 0ustar liggesusers\name{TimeSeriesData} \alias{TimeSeriesData} \alias{LPP2005REC} \alias{MSFT} \alias{USDCHF} \title{Time series data sets} \description{ Three data sets used in example files. } \details{ The following datasets are available: \describe{ \item{MSFT}{ Daily Microsoft OHLC (Open-high-low-close) prices and volume from 2000-09-27 to 2001-09-27. } \item{USDCHF}{ USD/CHF intraday foreign exchange rates. } \item{LPP2005REC}{ Swiss pension fund assets returns benchmark from 2005-11-01 to 2007-04-11. } } The datasets are objects from class \code{"timeSeries"}. } \note{ No further information about the \code{LPP2005REC} is available. The meaning of the columns? } \seealso{ \code{\link{readSeries}}, \code{\link{timeSeries}} } \examples{ ## LPP2005 example data set data(LPP2005REC) plot(LPP2005REC, type = "l") class(LPP2005REC) dim(LPP2005REC) head(LPP2005REC) LPP2005REC[1:5, 2:4] range(time(LPP2005REC)) summary(LPP2005REC) ## MSFT example data set data(MSFT) plot(MSFT[, 1:4], type = "l") plot(MSFT[, 5], type = "h") class(MSFT) range(time(MSFT)) head(MSFT) ## Plot USDCHF example data set data(USDCHF) plot(USDCHF) range(time(USDCHF)) head(USDCHF) } \keyword{datasets} timeSeries/man/methods-mathOps.Rd0000644000176200001440000000632514650724114016513 0ustar liggesusers\name{math} \alias{math} \alias{Math,timeSeries-method} \alias{Math2,timeSeries-method} \alias{Summary,timeSeries-method} \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{trunc,timeSeries-method} \alias{log,timeSeries-method} %\alias{\%*\%,timeSeries,vector-method} %\alias{\%*\%,timeSeries,ANY-method} %\alias{\%*\%,ANY,timeSeries-method} \alias{quantile} \alias{quantile.timeSeries} \alias{median} \alias{median.timeSeries} \title{Mathematical operations on 'timeSeries'} \description{ Functions and methods for mathematical operations on \code{"timeSeries"}. } \usage{ \S4method{Ops}{timeSeries,timeSeries}(e1, e2) \S4method{Math}{timeSeries}(x) \S4method{Math2}{timeSeries}(x, digits) \method{quantile}{timeSeries}(x, \dots) \method{median}{timeSeries}(x, na.rm = FALSE, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{digits}{ number of digits to be used in 'round' or 'signif'. } \item{e1, e2}{ at least one of the two objects is from class \code{"timeSeries"} (for the methods described on this page). } \item{na.rm}{ a logical value: should missing values be removed? } \item{\dots}{ arguments to be passed. } } \details{ The methods for the \code{Math} and \code{Math2} groups of mathematical functions return 'timeSeries' objects. Most of them work element-wise on the data part of the time series with the exception of \code{cummin}, \code{cummax}, \code{cumsum}, and \code{cumprod} which work columnwise. The \code{Ops} group includes mathematical operators. For the binary operators methods are defined for pairs of at least one 'timeSeries' object. These work as expected on the data parts of the arguments. If the operation gives a value of the same dimension as the data part of the 'timeSeries' object, it replaces the original data in the object. There are also methods for \code{quantile} and \code{median}. } \value{ the value from a mathematical or logical operation operating on objects of class \code{"timeSeries"} or the value computed by a mathematical function. } \seealso{ \code{\link[=colCum]{colCumXXX}} } \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 ## median, quantile median(TS) quantile(TS) TS[3] <- NA # to demonstrate 'na.rm' median(TS) # NA #quantile(TS) # error median(TS, na.rm = TRUE) quantile(TS, na.rm = TRUE) } \keyword{chron} \keyword{methods} timeSeries/man/fin-drawdowns.Rd0000644000176200001440000000421114436331171016210 0ustar liggesusers\name{drawdowns} \alias{drawdowns} \alias{drawdownsStats} \title{Calculations of drawdowns} \description{ Compute series of drawdowns from financial returns and calculate drawdown statisitcs. } \usage{ drawdowns(x, \dots) drawdownsStats(x, \dots) } \arguments{ \item{x}{ a \code{"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 \code{na.omit}. } } \value{ for \code{drawdowns}, an object of class \code{timeSeries}. for \code{drawdownsStats} an object of class \code{"data.frame"} with the following components: \item{drawdown}{the depth of the drawdown, } \item{from}{the start date, } \item{trough}{the trough period, } \item{to}{the end date, } \item{length}{the length in number of records, } \item{peaktrough}{the peak trough, and } \item{recovery}{the recovery length in number of records.} } \details{ The code in the core of the function \code{drawdownsStats} 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}. } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, %\code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \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/timeSeries-slotFinCenter.Rd0000644000176200001440000000205314436344104020316 0ustar liggesusers\name{finCenter} \alias{finCenter} \alias{finCenter<-} \alias{finCenter,timeSeries-method} \alias{finCenter<-,timeSeries-method} \alias{getFinCenter} \alias{setFinCenter<-} \title{Get and set Financial center of a 'timeSeries'} \description{ Get or assign a financial center to a \code{"timeSeries"} object. } \usage{ \S4method{finCenter}{timeSeries}(x) \S4method{finCenter}{timeSeries}(x) <- value getFinCenter(x) setFinCenter(x) <- value } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{value}{ a character with the the location of the financial center named as \code{"continent/city"}. } } \seealso{ \code{\link[timeDate]{listFinCenter}} and \code{\link[timeDate]{finCenter}} in package \code{"timeDate"} } \examples{ ## An artificial 'timeSeries' Object - tS <- dummyMonthlySeries() tS ## Print Financial Center - finCenter(tS) getFinCenter(tS) ## Assign New Financial Center - finCenter(tS) <- "Zurich" tS setFinCenter(tS) <- "New_York" tS } \keyword{programming} timeSeries/man/base-start.Rd0000644000176200001440000000150314650724115015476 0ustar liggesusers\name{start} \alias{start} \alias{start.timeSeries} \alias{end} \alias{end.timeSeries} %\alias{start,timeSeries-method} %\alias{end,timeSeries-method} \title{Start and end of a 'timeSeries'} \description{ Returns start or end time stamp of a \code{"timeSeries"} object. } \usage{ \method{start}{timeSeries}(x, \dots) \method{end}{timeSeries}(x, \dots) } \arguments{ \item{x}{ an uni- or multivariate \code{"timeSeries"} object. } \item{\dots}{ optional arguments passed to other methods. } } \value{ a \code{"timeSeries"} object } \examples{ \dontshow{set.seed(1234)} ## Create a dummy \code{"timeSeries"} tS <- dummyMonthlySeries()[, 1] tS ## Return start and end time stamp c(start(tS), end(tS)) range(time(tS)) } \keyword{chron} timeSeries/man/stats-lag.Rd0000644000176200001440000000246214650724115015335 0ustar liggesusers\name{lag} \alias{lag} \alias{lag.timeSeries} \title{Lag a 'timeSeries' object} \description{ Compute a lagged version of a \code{"timeSeries"} object. } \usage{ \method{lag}{timeSeries}(x, k = 1, trim = FALSE, units = NULL, \dots) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \item{k}{ an integer number, the number of lags (in units of observations). By default 1. Can also be a vector, in which case the result is a multivariate \code{"timeSeries"} in which column \code{i} contains the series lagged by \code{k[i]}, see the examples. } \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{\dots}{ arguments passed to other methods. } } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link[stats]{lag}} for \verb{stats::lag}, \code{\link{diff}} } \examples{ ## Load Micsrosoft Data Set x <- MSFT[1:20, "Open"] ## Lag the 'timeSeries' Object lag(x, k = -1:1) } \keyword{chron} timeSeries/man/base-subsetting.Rd0000644000176200001440000001060414650724114016531 0ustar liggesusers\name{TimeSeriesSubsettings} \alias{TimeSeriesSubsettings} \alias{$,timeSeries-method} \alias{[,timeSeries,ANY,index_timeSeries-method} \alias{[,timeSeries,character,character-method} \alias{[,timeSeries,character,index_timeSeries-method} \alias{[,timeSeries,character,missing-method} \alias{[,timeSeries,index_timeSeries,character-method} \alias{[,timeSeries,index_timeSeries,index_timeSeries-method} \alias{[,timeSeries,index_timeSeries,missing-method} \alias{[,timeSeries,matrix,missing-method} \alias{[,timeSeries,missing,character-method} \alias{[,timeSeries,missing,index_timeSeries-method} \alias{[,timeSeries,missing,missing-method} \alias{[,timeSeries,time_timeSeries,ANY-method} \alias{[,timeSeries,time_timeSeries,character-method} \alias{[,timeSeries,time_timeSeries,index_timeSeries-method} \alias{[,timeSeries,time_timeSeries,missing-method} \alias{[,timeSeries,timeDate,character-method} \alias{[,timeSeries,timeDate,index_timeSeries-method} \alias{[,timeSeries,timeDate,missing-method} \alias{[,timeSeries,timeSeries,index_timeSeries-method} \alias{[,timeSeries,timeSeries,missing-method} \alias{$<-,timeSeries,ANY-method} \alias{$<-,timeSeries,factor-method} \alias{$<-,timeSeries,numeric-method} \alias{[<-,timeSeries,character,ANY-method} \alias{[<-,timeSeries,character,missing-method} \alias{[<-,timeSeries,timeDate,ANY-method} \alias{[<-,timeSeries,timeDate,missing-method} \alias{head} %\alias{head,timeSeries-method} \alias{head.timeSeries} \alias{tail} %\alias{tail,timeSeries-method} \alias{tail.timeSeries} \alias{outlier} \alias{outlier,timeSeries-method} \alias{outlier,ANY-method} \title{Subsetting time series} \description{ Objects from class \code{"timeSeries"} can be subsetted in different ways. Methods are defined for the subsetting operators \code{"$"}, \code{"["} and their assignment versions, as well as for some related functions from base \R. A function to drop or extract outliers is also described here. } % \method{[}{timeSeries}(x, i, j, drop) % \method{[}{timeSeries}(x, i, j) <- value \usage{ \method{head}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) \method{tail}{timeSeries}(x, n = 6, recordIDs = FALSE, \dots) outlier(x, sd = 5, complement = TRUE, ...) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } % \item{from, to}{ % starting date and end date, \code{to} must be after \code{from}. % } % \item{start, end}{ % starting date and end date, \code{end} must be after \code{start}. % } % \item{i, j}{ % ["["] - \cr % index arguments used for subsettings. % } \item{n}{ an integer specifying the number of lines to be returned. By default \code{n=6}. } \item{recordIDs}{ a logical value. Should the \code{recordIDs} be returned together with the data matrix and time series positions? } \item{sd}{ a numeric value of standard deviations, e.g. 10 means that values larger or smaller than ten times the standard deviation will be removed from the series. } \item{complement}{ a logical flag. If \code{TRUE}, the default, return the series free of outliers. If \code{FALSE}, return the outliers series. } % \item{value}{ % a numeric value to use as a replacement. It will be repeated a % whole number of times if necessary. % } \item{\dots}{ arguments passed to other methods. } } \details{ The \code{"timeSeries"} methods for the subsetting operators \code{"$"}, \code{"["} and their assignment versions, as well as for the functions \code{head} and \code{tail} are meant to do what the user expects. \strong{TODO:} Further details are needed here, despite the above paragraph. \code{outlier} drops the outliers if \code{complement = TRUE} and returns only them if \code{complement = FALSE}. All functions described here return \code{"timeSeries"} objects. See also \code{\link{window}} which extracts the sub-series between two datetimes. } \value{ All functions return an object of class \code{"timeSeries"}. } \seealso{ \code{\link{window}} } \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/base-sort.Rd0000644000176200001440000000641114650724114015332 0ustar liggesusers\name{sort} \alias{sort} \alias{sort.timeSeries} \alias{is.unsorted} \alias{is.unsorted.timeSeries} \alias{is.unsorted,timeSeries-method} \title{Sort a 'timeSeries' by time stamps} \description{ Sort a \code{"timeSeries"} object with respect to its time stamps. } \usage{ \method{sort}{timeSeries}(x, decreasing = FALSE, \dots) \S4method{is.unsorted}{timeSeries}(x, na.rm = FALSE, strictly = FALSE) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{decreasing}{ a logical flag. Should we sort in increasing or decreasing order? By default \code{FALSE}. } \item{na.rm}{ a logical value, should missing values be removed? } \item{strictly}{ logical indicating if the check should be for strictly increasing values. } \item{\dots}{ optional arguments passed to other methods. } } \details{ The method for \code{sort} sorts \code{x} either in increasing or decreasing time stamp order. The method for \code{is.unsorted} returns \code{TRUE} if the time stamps of \code{x} are not sorted in increasing order (including the case when they are sorted in decreasing order) and \code{FALSE} otherwise. \code{is.unsorted} may also return \code{NA} when there are \code{NA}s among the time stamps of \code{x}. All this is in line with the documented functionality of \code{base::is.unsorted}. } \value{ for \code{sort}, a \code{"timeSeries"} object, for the \code{is.unsorted} method, \code{TRUE}, \code{FALSE}, or \code{NA}, as described in section \sQuote{Details}. } \note{ If \code{is.unsorted} returns \code{NA} when there are \code{NA}s in the data but not in the time stamps use \code{library{timeSeries}} or call the function as \code{timeSeries::is.unsorted}. If you need more details, read the rest of this note. \code{base::is.unsorted} 'sees' the method for \code{"timeSeries"} objects when package timeSeries is loaded (whether or not it is attached). However, due to the way \code{base::is.unsorted} is implemented, it may give wrong answers when there are \code{NA}'s among the values of the time series. Developers of packages applying \code{is.unsorted} on timeSeries objects should import if from package timeSeries. The above feature is not a shortcoming of \code{base::is.unsorted} but a consequence of the fact that the timeSeries method is not consistent with its semantics. For example, it works on the time stamps, while \code{is.na} works on the data values. } \seealso{ \code{\link[base]{is.unsorted}} for further details on the \code{NA} case } \examples{ ## a monthly calendar series x <- daily2monthly(LPP2005REC[, 1:2])[3:14, ] \dontshow{set.seed(1234)} ## resample the series with respect to the time stamps, resampled <- sample(x) ## the time stamps are unordered resampled is.unsorted(resampled) # TRUE (i.e., not sorted) ## Now sort the series in decreasing time order backward_in_time <- sort(resampled, , decreasing = TRUE) ## time stamps ordered in decreasing order ## but is.unordered requires increasing order: backward_in_time is.unsorted(backward_in_time) # still TRUE ## Is the reverted series ordered? forward_in_time <- rev(backward_in_time) forward_in_time is.unsorted(forward_in_time) # FALSE (i.e., sorted) } \keyword{chron} timeSeries/man/stats-aggregate.Rd0000644000176200001440000000337014435660315016521 0ustar liggesusers\name{aggregate-methods} \alias{aggregate-methods} \docType{methods} \alias{aggregate} \alias{aggregate.timeSeries} \alias{aggregate,timeSeries-method} \title{Aggregate time series} \description{ Aggregate a \code{"timeSeries"} object over general periods. } \usage{ \S4method{aggregate}{timeSeries}(x, by, FUN, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{by}{ a sequence of \code{"timeDate"} objects denoting the aggregation periods, see section \sQuote{Details}. } \item{FUN}{ the function to be applied. } \item{\dots}{ arguments passed to other methods. } } \details{ \code{aggregate} aggregates \code{x} by applying \code{FUN} on the values of the time series in each of the aggregation periods, specified by argument \code{by}. Argument \code{by} should be of the same class as \code{time(x)}. \code{by} is sorted and duplicated values are removed from it. Each pair of consecutive values in \code{by} then determines a period over which to apply the aggregation function \code{FUN}, see \code{\link[base]{findInterval}}. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{apply}}, \code{\link{align}} } \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) } \keyword{methods} \keyword{chron} timeSeries/man/stats-na.omit.Rd0000644000176200001440000001532614650724115016142 0ustar liggesusers\name{na} \alias{na} \alias{na.omit} \alias{na.omit.timeSeries} \title{Handle missing values in 'timeSeries' objects} \description{ Functions for handling missing values in \code{"timeSeries"} objects. } \usage{ \method{na.omit}{timeSeries}(object, method = c("r", "s", "z", "ir", "iz", "ie"), interp = c("before", "linear", "after"), FUN, \dots) } \arguments{ \item{object}{ an object of class \code{"timeSeries"}. } \item{method}{ the method of handling NAs, see section \sQuote{Details}. } \item{interp}{ Three alternative methods are provided to remove NAs from the data: \code{type="zeros"} replaces the missing values with zeros, \code{type="mean"} replaces the missing values with the column mean, \code{type="median"} replaces the missing values with the column median. } \item{FUN}{ a function or a name of a function, such as \code{"mean"} or \code{median}. \code{FUN} is applied to the non-NA values in each column to determine the replacement value. The call looks like \code{FUN(coli, na.rm = TRUE)}, so \code{FUN} should have argument \code{na.rm}. All arguments except \code{object} are ignored if \code{FUN} is specified. } \item{\dots}{ arguments to be passed to the function \code{as.matrix}. } } \details{ Functions for handling missing values in \code{"timeSeries"} objects and in objects which can be transformed into a vector or a two dimensional matrix. For \code{na.omit} argument \code{method} specifies how to handle \code{NA}s. Can be one of the following strings: \describe{ \item{method = "s"}{\code{na.rm = FALSE}, skip, i.e. do nothing, } \item{method = "r"}{remove NAs,} \item{method = "z"}{substitute NAs by zeros,} \item{method = "ir"}{interpolate NAs and remove NAs at the beginning and end of the series,} \item{method = "iz"}{interpolate NAs and substitute NAs at the beginning and end of the series,} \item{method = "ie"}{interpolate NAs and extrapolate NAs at the beginning and end of theseries.} } % For \code{interpNA} argument \code{method} specifies how to % interpolate the matrix column by column. One of the following % character strings: \code{"linear"}, \code{"before"}, \code{"after"}. % For interpolation the function \code{approx} is used. % % The functions are listed by topic. \cr % % \tabular{ll}{ % \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. } \note{ When dealing with daily data sets, there exists another function \code{alignDailySeries} which can handle missing data in un-aligned calendrical \code{"timeSeries"} objects. % 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}. \strong{Additional remarks by GNB:} \code{removeNA(x)} is equivalent to \code{na.omit(x)} or \code{na.omit(x), methods = "r"}. \code{interpNA} can be replaced by a call to \code{na.omit} with argument method equal to \code{ir}, \code{iz}, or \code{ie}, and argument \code{"interp"} equal to the \code{"method"} argument for \code{interpNA} (note that the defaults are not the same). \code{substituteNA(x, type = "zeros")} is equivalent to \code{na.omit(x, method = "z")}. For other values of \code{type} one can use argument \code{FUN}, as in \code{na.omit(x, FUN = "mean")}. A final remark: the three deprecated functions are non-generic. \code{removeNA(x)} is completely redundant as it simply calls \code{na.omit}. The other two however may be useful for matrix-like objects. Please inform the maintainer of the package if you use them on objects other than from class \code{"timeSeries"} and wish them kept in the future. } \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. } \seealso{ \code{\link{alignDailySeries}} } \examples{ X <- matrix(rnorm(100), ncol = 5) # Create a Matrix X X[3, 5] <- NA # Replace a Single NA Inside X[17, 2:4] <- c(NA, NA, NA) # Replace Three in a Row Inside X[13:15, 4] <- c(NA, NA, NA) # Replace Three in a Column Inside X[11:12, 5] <- c(NA, NA) # Replace Two at the Right Border X[20, 1] <- NA # Replace One in the Lower Left Corner X Xts <- timeSeries(X) # convert X to timeSeries Xts ## remove rows with NAs na.omit(Xts) ## Subsitute NA's with zeros or column means (formerly substituteNA()) na.omit(Xts, method = "z") na.omit(Xts, FUN = "mean") na.omit(Xts, FUN = "median") ## Subsitute NA's with a trimmed mean na.omit(Xts, FUN = function(x, na.rm) mean(x, trim = 0.10, na.rm = na.rm)) ## interpolate NA's linearily (formerly interpNA()) na.omit(X, method = "ir", interp = "linear") na.omit(X, method = "iz", interp = "linear") na.omit(X, method = "ie", interp = "linear") ## take previous values in a column na.omit(X, method = "ir", interp = "before") na.omit(X, method = "iz", interp = "before") na.omit(X, method = "ie", interp = "before") ## examples with X (which is a matrix, not "timeSeries") ## (these examples are not run automatically as these functions are ## deprecated.) if(FALSE){ ## 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/base-scale.Rd0000644000176200001440000000353114436333131015427 0ustar liggesusers\name{scale} \alias{scale} \alias{scale,timeSeries-method} \title{Center and scale 'timeSeries' objects} \description{ Center and scale a \code{"timeSeries"} object. } \usage{ \S4method{scale}{timeSeries}(x, center = TRUE, scale = TRUE) } \arguments{ \item{x}{ an object from class \code{"timeSeries"}. } \item{center, scale}{ a numeric vector or a logical value, see \sQuote{Details}. } } \details{ \code{scale} centers and/or scales the columns of a \code{"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{ a centered and/or scaled \code{"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} \keyword{ts} timeSeries/man/fin-periodical.Rd0000644000176200001440000000422114650724114016315 0ustar liggesusers\name{periodical} \alias{endOfPeriod} \alias{endOfPeriodSeries} \alias{endOfPeriodStats} \alias{endOfPeriodBenchmarks} \title{End-of-Period series, stats, and benchmarks} \description{ Computes periodical statistics back to a given period. } \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 \code{"timeSeries"} object. One of the columns holds the benchmark series specified by argument \code{benchmark}, } \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 benchmark series in \code{x}. By default this is the last column of \code{x}. } } \details{ \code{endOfPeriodSeries} extract the data for the last few years, as specified by argument \code{nYearsBack}. \code{endOfPeriodStats} computes basic exploratory statistics for the last few years in the data. \code{endOfPeriodBenchmarks} returns benchmarks back to a given period. \code{x} must be end of month data. Such series can be created using functions like \code{align}, \code{alignDailySeries}, \code{daily2monthly}. } \value{ for \code{endOfPeriodSeries}, a \code{"timeSeries"}, for \code{endOfPeriodStats}, a data frame, for \code{endOfPeriodBenchmarks} - currently \code{NULL} (invisibly), the function is unfinished. } \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/fin-durations.Rd0000644000176200001440000000251414436606710016220 0ustar liggesusers\name{durations} \alias{durations} \alias{durationSeries} % removed \title{Durations from a 'timeSeries'} \description{ Computes durations from an object of class \code{"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{ 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/timeSeries-readSeries.Rd0000644000176200001440000000540614436334637017644 0ustar liggesusers\name{readSeries} \alias{readSeries} \title{Read a 'timeSeries' from a text file} \description{ Reads a file in table format and creates a \code{"timeSeries"} object from it. The first column of the table must hold the timestamps. } \usage{ readSeries(file, header = TRUE, sep = ";", zone = "", FinCenter = "", format, \dots) } \arguments{ \item{file}{ the filename of a spreadsheet dataset from which to import the data records. } \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 fields than the number of columns. } \item{sep}{ the field seperator used in the spreadsheet file to separate columns, by default \code{";"}. If \code{sep = ";"} 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{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{format}{ a character string with the format in POSIX notation specifying the timestamp format. The format has not to be specified if the first column in the file has the timestamp format specifier, e.g. "\%Y-\%m-\%d" for the short ISO 8601 format. } \item{\dots}{ Additional arguments passed to \code{read.table()} which is used to read the file. } } \details{ The file is imported with \code{\link{read.table}}. Note the different default for argument \code{"sep"}. The first column of the table must hold the timestamps. Format of the timestamps can be either specified in the header of the first column or by the \code{format} argument. } \value{ an object of class \code{"timeSeries"} } \seealso{ \code{\link{as.timeSeries}}, \code{\link{timeSeries}}, \code{\link{dummyMonthlySeries}}, \code{\link{dummyDailySeries}} } \examples{ ## full path to an example file fn <- system.file("extdata/msft.csv", package = "timeSeries") ## first few lines of the file readLines(fn, n = 5) ## import the file msft <- readSeries(fn) head(msft) ## is msft the same as the data object MSFT? all.equal(msft, MSFT) ## ... almost, except for slot 'documentation' c(msft@documentation, MSFT@documentation) ## actually, all.equal() says 'attribute', not slot. this is ok too: c(attr(MSFT, "documentation"), attr(msft, "documentation")) ## make 'documentation' equal, here "", and compare again: msft@documentation <- "" all.equal(msft, MSFT) # TRUE } timeSeries/man/base-rev.Rd0000644000176200001440000000105514650724114015136 0ustar liggesusers\name{rev} \alias{rev.timeSeries} \title{Reverse a 'timeSeries'} \description{ Reverses an uni- or multivariate \code{"timeSeries"} object. } \usage{ \method{rev}{timeSeries}(x) } \arguments{ \item{x}{ an uni- or multivariate \code{"timeSeries"} object. } } \value{ a \code{"timeSeries"} object } \examples{ \dontshow{set.seed(1234)} ## Create Dummy "timeSeries" tS <- dummyMonthlySeries() ## reverse series rev(tS) } \keyword{chron} timeSeries/man/methods-as.Rd0000644000176200001440000000677214650724114015511 0ustar liggesusers\name{as} \alias{as} \alias{as.timeSeries} \alias{as.timeSeries.default} \alias{as.timeSeries.data.frame} \alias{as.timeSeries.character} \alias{as.timeSeries.ts} \alias{as.timeSeries.zoo} \alias{as.matrix,timeSeries-method} \alias{as.data.frame,timeSeries-method} \alias{as.list,timeSeries-method} %\alias{as.ts,timeSeries-method} % S3 versions of the above \alias{as.matrix.timeSeries} \alias{as.data.frame.timeSeries} \alias{as.list.timeSeries} \alias{as.ts} \alias{as.ts.timeSeries} % for as(x, "class") where x is the 1st element of the signature \alias{coerce,ANY,timeSeries-method} \alias{coerce,data.frame,timeSeries-method} \alias{coerce,character,timeSeries-method} \alias{coerce,ts,timeSeries-method} % for as(x, "class") where x is timeSeries \alias{coerce,timeSeries,matrix-method} \alias{coerce,timeSeries,data.frame-method} \alias{coerce,timeSeries,list-method} \alias{coerce,timeSeries,ts-method} \alias{coerce,timeSeries,tse-method} \title{Convert objects to/from class 'timeSeries'} \description{ Functions and methods dealing with the coercion between \code{"timeSeries"} and other classes. } \usage{ ## convert to 'timeSeries' as.timeSeries(x, \dots) ## convert from 'timeSeries' to other classes \method{as.ts}{timeSeries}(x, \dots) \S4method{as.matrix}{timeSeries}(x, \dots) \S4method{as.data.frame}{timeSeries}(x, row.names = NULL, optional = FALSE, \dots) \S4method{as.list}{timeSeries}(x, \dots) } \arguments{ \item{x}{ the object to be converted, see Section \sQuote{Details} for the special case when \code{class(x)} is \code{"character"}. } \item{row.names}{ \code{NULL} or a character vector giving the row names for the data frame. Missing values are not allowed. } \item{optional}{ a logical value. If \code{TRUE}, setting row names and converting column names (to syntactic names) is optional. } \item{\dots}{ arguments passed to other methods. } } \details{ Functions to create \code{"timeSeries"} objects from other objects and to convert \code{"timeSeries"} objects to other classes. \code{as.timeSeries} is a generic function to convert an object to \code{"timeSeries"}. There are specialised methods for the following classes: \code{"ts"}, \code{"data.frame"}, \code{"character"}, and \code{"zoo"}. The default method is equivalent to calling \code{"timeSeries()"}, so \code{x} can be of any type that \code{"timeSeries()"} accepts. The \code{character} method of \code{as.timeSeries} is special, in that its contents are parsed and evaluated, then \code{as.timeSeries} is called on the returned value (passing also the \code{"..."} arguments. Care is needed to avoid infinite recursion here since currently the code doesn't guard against it. } \value{ for \code{as.timeSeries}, an object of class \code{"timeSeries"}. \cr for \code{as.numeric}, \code{as.data.frame}, \code{as.matrix}, \code{as.ts}, \code{as.list} - a numeric vector, a data frame, a matrix, an object of class \code{ts}, or a \code{"list"}, respectively. } \seealso{ \code{\link{timeSeries}}, class \code{\linkS4class{timeSeries}} } \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} \keyword{ts} timeSeries/man/timeSeries-slotUnits.Rd0000644000176200001440000000137114436343424017551 0ustar liggesusers\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 \code{"timeSeries"} object. The column names are also called units or unit names. } \usage{ getUnits(x) setUnits(x) <- value } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{value}{ a character vector of unit names. } } \seealso{ \code{\link{timeSeries}} } \examples{ ## A Dummy 'timeSeries' Object tS <- dummyMonthlySeries() tS ## Get the Units - getUnits(tS) ## Assign New Units to the Series - setUnits(tS) <- c("A", "B") head(tS) } \keyword{programming} timeSeries/man/base-dim.Rd0000644000176200001440000000611714436341416015121 0ustar liggesusers\name{dimnames} \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} \title{Dimension and their names for 'timeSeries' objects} \description{ Get and assign names, row names, column names, and dim names of \code{"timeSeries"} objects. } % \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}. % } % %} \details{ \code{"timeSeries"} methods are available for base R functions working on dimension names, including \code{dim}, \code{dimnames}, \code{colnames}, \code{rownames}, \code{names} and their assignment variants. \code{dim} is the dimension of the underlying data matrix. \code{rownames} gives the datetime stamps as a character vector. \code{rownames<-} sets them. \code{colnames} gives the values of \code{x@units}. These are conceptually the column names of the data matrix. \code{colnames<-} sets slot \code{units} of \code{x}. \code{dimnames} gives \code{list(rownames(x), colnames(x)}. \code{dimnames<-} calls \code{rownames} and \code{colnames} on \code{value[[1]]} and \code{value[[2]]}, respectively. } \note{ (GNB; todo) The \code{"dim<-"}, currently converts \code{x} to a vector if \code{value} is \code{NULL}, otherwise it ignores \code{value}, does nothing and returns \code{x} unchanged. This behaviour should not be relied upon and may be changed in the future, e.g. by issuing warning when \code{value} is not \code{NULL}. Or throwing error altogether if assignment with \code{"dim<-"} is attempted. } \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/utils-structure.Rd0000644000176200001440000000140214650724115016625 0ustar liggesusers\name{str-methods} \alias{str-methods} \alias{str} \alias{str.timeSeries} \title{Display the structure of 'timeSeries' objects} \description{ Compactly display the structure of a \code{"timeSeries"} object. } \usage{ \method{str}{timeSeries}(object, \dots) } \arguments{ \item{object}{ an object of class \code{timeSeries}. } \item{\dots}{ arguments passed to other methods. } } \value{ \code{NULL}, invisibly. The function is called for its side effect of printing a compact representation of the structure of the \code{"timeSeries"} object. } \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-diff.Rd0000644000176200001440000000227014650724114015252 0ustar liggesusers\name{diff} \alias{diff} \alias{diff.timeSeries} \title{Difference a 'timeSeries' object} \description{ Difference a \code{"timeSeries"} object. } \usage{ \method{diff}{timeSeries}(x, lag = 1, diff = 1, trim = FALSE, pad = NA, \dots) } \arguments{ \item{x}{ an object of class \code{"timeSeries"}. } \item{lag}{ an integer indicating which lag to use. } \item{diff}{ an integer indicating the order of the difference. } \item{trim}{ a logical flag. Should \code{NA}s at the beginning of the series be removed? } \item{pad}{ a numeric value with which \code{NA}s should be replaced at the beginning of the series. } \item{\dots}{ currently not used. } } %\details{ %} \value{ the differenced \code{"timeSeries"} object } \seealso{ \code{\link[base]{diff}} for \verb{base::diff}, \code{\link{lag}} } \examples{ ## load Microsoft dataset 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} \keyword{ts} timeSeries/man/utils-description.Rd0000644000176200001440000000047514433602271017116 0ustar liggesusers\name{description} \alias{description} \title{Creates date and user information} \description{ Creates and returns a string containing the user, the current datetime and the user name. } \usage{ description() } \examples{ ## Show Default Description String - description() } \keyword{programming} timeSeries/man/statistics-rollMean.Rd0000644000176200001440000000424014436334355017400 0ustar liggesusers\name{rollMean} \alias{rollMean} \alias{rollStats} \alias{rollMin} \alias{rollMax} \alias{rollMedian} \title{Rolling statistics} \description{ Computes rolling mean, min, max and median for a \code{"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 \code{"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{ an object of class \code{"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.Rd0000644000176200001440000001266514436343326015560 0ustar liggesusers\name{TimeSeriesClass} \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{index_timeSeries-class} \alias{time_timeSeries-class} \alias{summary.timeseries} \title{Create objects from class 'timeSeries'} \description{ \code{timeSeries} creates a \code{"timeSeries"} object from scratch. } \usage{ timeSeries(data, charvec, units = NULL, format = NULL, zone = "", FinCenter = "", recordIDs = data.frame(), title = NULL, documentation = NULL, \dots) } \arguments{ \item{data}{ a \code{matrix} object or any objects which can be coerced to a matrix. } \item{charvec}{ a character vector of dates and times or any objects which can be coerced to a \code{"timeDate"} object. } \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, a character string with the format in POSIX notation. } \item{zone}{ the time zone or financial center where the data were recorded. } \item{FinCenter}{ a character with the the location of the financial center named as "continent/city". } \item{recordIDs}{ for \code{timeSeries}, a data frame which can be used for record identification. } \item{title}{ an optional title string, if not specified the input's data name is deparsed. } \item{documentation}{ optional documentation string, or a vector of character strings. } \item{\dots}{ arguments passed to other methods. } } \value{ an S4 object of class \code{"timeSeries"} } \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. \code{\link{as.timeSeries}} also creates \code{"timeSeries"} objects. \code{as.timeSeries(x)} is mostly equivalent to \code{timeSeries(x)} but the two functions have different methods. Beside that, the main difference between the two functions is that \code{as.timeSeries} doesn't accept additional arguments. The one argument call is naturally interpreted as \sQuote{convert to}, so \code{\link{as.timeSeries}} is more expressive and is recommended in that case. \code{"timeSeries"} methods are provided for many base R functions, including arithmetic operations, mathematical functions, \code{print}, \code{summary}, and time series functions. Not all are explicitly documented, since they can just be used. } % \note{ % % These functions were written for Rmetrics users using R and Rmetrics % under Microsoft's Windows operating system where time zones, % daylight saving times and holiday calendars are insuffeciently % supported. % % } \seealso{ \code{\link{as.timeSeries}}, class \code{\linkS4class{timeSeries}}, } \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-turnpoints.Rd0000644000176200001440000000644614435105524016441 0ustar liggesusers\name{turns} \alias{turns} \alias{turnsStats} \title{Turning points of a time series} \description{ Extracts and analyzes turning points of an univariate \code{"timeSeries"} object. } \usage{ turns(x, \dots) turnsStats(x, doplot = TRUE) } \arguments{ \item{x}{ an univariate \code{"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 positions of extrema (turning points, either peaks or pits) in a regular time series. The function \code{turnsStats} calculates the quantity of information associated with 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{ for \code{turns}, an object of class \code{timeSeries}.\cr for \code{turnsStats}, an object of class \code{turnpoints} with the following entries: \item{data}{The dataset to which the calculation is done.} \item{n}{The number of observations.} \item{points}{The value of the points in the series, after elimination of ex-aequos.} \item{pos}{The position of the points on the time scale in the series (including ex-aequos).} \item{exaequos}{Location of exaequos (1), or not (0).} \item{nturns}{Total number of turning points in the whole time series.} \item{firstispeak}{Is the first turning point a peak (TRUE), or not (FALSE).} \item{peaks}{Logical vector. Location of the peaks in the time series without ex-aequos.} \item{pits}{Logical vector. Location of the pits in the time series without ex-aequos.} \item{tppos}{Position of the turning points in the initial series (with ex-aequos).} \item{proba}{Probability to find a turning point at this location.} \item{info}{Quantity of information associated with this point.} } \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/statistics-smoothLowess.Rd0000644000176200001440000000463314436334072020337 0ustar liggesusers\name{smooth} \alias{smoothLowess} \alias{smoothSpline} \alias{smoothSupsmu} \title{Smooths time series objects} \description{ Smooths a \code{"timeSeries"} object. } \usage{ smoothLowess(x, f = 0.5, \dots) smoothSpline(x, spar = NULL, \dots) smoothSupsmu(x, bass = 5, \dots) } \arguments{ \item{x}{ an univariate \code{"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{ a bivariate \code{"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-t.Rd0000644000176200001440000000071614436342260014610 0ustar liggesusers\name{t} \alias{t,timeSeries-method} \title{Transpose 'timeSeries' objects} \description{ Returns the transpose of a \code{"timeSeries"} object. } \usage{ \S4method{t}{timeSeries}(x) } \arguments{ \item{x}{ a 'timeSeries' object. } } \value{ a matrix } \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/fin-spreads.Rd0000644000176200001440000000312514436612715015652 0ustar liggesusers\name{spreads} \alias{spreads} \alias{midquotes} \alias{spreadSeries} % removed \alias{midquoteSeries} % removed \title{Spreads and mid quotes} \description{ Compute spreads and midquotes from price streams. } \usage{ spreads(x, which = c("Bid", "Ask"), tickSize = NULL) midquotes(x, which = c("Bid", "Ask")) } \arguments{ \item{x}{ an object of class \code{timeSeries}. } \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 the bid and ask prices with column names \code{c("Bid", "Ask")}. } \item{tickSize}{ the default is \code{NULL} to simply compute price changes in original price levels. If \code{ticksize} is supplied, the price changes will be divided by the value of \code{inTicksOfSize} to compute price changes in ticks. } } \value{ all functions return an object of class \code{timeSeries} } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, \code{\link{splits}}, %\code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \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/base-rank.Rd0000644000176200001440000000354714436332513015305 0ustar liggesusers\name{rank} \alias{rank} \alias{rank,timeSeries-method} \title{Sample ranks of a time series} \description{ Compute the sample ranks of the values of a 'timeSeries' object. } \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 \code{NA}s. If \code{TRUE}, missing values in the data are put last; if \code{FALSE}, they are put first; if \code{NA}, they are removed; if \code{"keep"} they are kept with rank \code{NA}. } \item{ties.method}{ a character string specifying how ties are treated; can be abbreviated. } } \details{ If all components are different (and no \code{NA}s), the ranks are well defined, with values in \code{seq_len(x)}. With some values equal (called \sQuote{ties}), argument \code{ties.method} determines the result at the corresponding indices. The \code{"first"} method results 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"} replace them with their maximum and minimum respectively, the latter being the typical sports ranking. \code{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}. } \value{ 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-align.Rd0000644000176200001440000001076414435660560015312 0ustar liggesusers\name{align-methods} \docType{methods} \alias{align} \alias{align-methods} \alias{align,timeSeries-method} \alias{alignDailySeries} \alias{daily2weekly} \alias{daily2monthly} \title{Align a 'timeSeries' object to equidistant time stamps} \description{ Aligns a \code{"timeSeries"} object to equidistant time stamps. There are also functions for the common cases of changing daily to weekly and daily to monthly. } \usage{ \S4method{align}{timeSeries}(x, by = "1d", offset = "0s", method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, ...) alignDailySeries(x, method = c("before", "after", "interp", "fillNA", "fmm", "periodic", "natural", "monoH.FC"), include.weekends = FALSE, units = NULL, zone = "", FinCenter = "", ...) daily2monthly(x, init = FALSE) daily2weekly(x, startOn = "Tue", init = 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}{ 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{include.weekends}{ a logical value. Should weekend dates be included or removed from the series? } \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{FinCenter}{ a character with the the location of the financial center named as \code{"continent/city"}. } \item{startOn}{ a character string, 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 \code{init} is set to \code{FALSE}. } \item{\ldots}{ further arguments to be passed to the interpolating function. } } \details{ TODO: complete. \code{alignDailySeries} aligns a daily 'timeSeries' to new positions, Effectively, it is a frontend to the \code{"timeSeries"} method for \code{align} with \code{by = "1d"}, and \code{offset = "0s"}. In addition, there are two tailored functions for common cases: \code{daily2monthly} and \code{daily2weekly} which aggregate \code{"timeSeries"} objects from daily to monthly or weekly levels, respectively. In the case of the function \code{daily2weekly} one can explicitly set the starting day of the week, the default value is Tuesday, \code{startOn = "Tue"}. } \seealso{ \code{\link{aggregate}}, \code{\link{apply}} } \value{ a \code{"timeSeries"} object, for \code{alignDailySeries}, a weekly aligned daily \code{"timeSeries"} object from a daily time series with missing holidays. } \examples{ ## Use Microsofts' OHLCV Price Series - head(MSFT) end(MSFT) ## 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) ## alignDailySeries ## 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) ## Load Microsoft Data Set - x <- MSFT ## Aggregate daily records to end of month records - X <- daily2monthly(x) X isMonthly(X) ## Aggregate daily records to end of week records - X <- daily2weekly(x, startOn="Fri") X dayOfWeek(time(X)) } \keyword{methods} \keyword{chron} timeSeries/man/fin-splits.Rd0000644000176200001440000000272614436330424015527 0ustar liggesusers\name{splits} \alias{splits} \title{splits} \description{ Searches for outlier splits in a \code{"timeSeries"} object. } \usage{ splits(x, sd = 3, complement = TRUE, ...) } \arguments{ \item{x}{ a \code{"timeSeries"} object. } \item{sd}{ \code{numeric(1)}; deviations of how many standard deviations to consider too big? Can be fractional. 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. } } \details{ This function finds splits in financial price or index series. If a price or index is splitted we observe a big jump of several standard deviations in the returns, which is identified usually as an outlier. } \value{ a \code{"timeSeries"} object } \seealso{ \code{\link{returns}}, \code{\link{cumulated}}, \code{\link{drawdowns}}, %\code{\link{splits}}, \code{\link{spreads}}, \code{\link{midquotes}}, \code{\link{index2wealth}} } \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-slotDocumentation.Rd0000644000176200001440000000414514436343567021272 0ustar liggesusers\name{attributes} \alias{attributes} \alias{getAttributes} \alias{setAttributes<-} \alias{documentation} \title{Get and set optional attributes of a 'timeSeries'} \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{ \dontshow{set.seed(1234)} ## Create an artificial 'timeSeries' Object - tS <- dummyMonthlySeries() 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/methods-plot.Rd0000644000176200001440000002005114650724114016046 0ustar liggesusers\name{plot-methods} \alias{plot} \alias{lines} \alias{points} \alias{plot,timeSeries-method} \alias{lines,timeSeries-method} \alias{points,timeSeries-method} \alias{pretty.timeSeries} \title{Plot 'timeSeries' objects} \description{ \code{"timeSeries"} methods for \code{\link[base]{plot}}, \code{\link[graphics]{lines}} and \code{\link[graphics]{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{ Our original method \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 \code{"ts"}. With verson R 3.1 we have inroduced two new additional plotting themes called \code{"pretty"} and \code{"chick"}. They are becoming active when we set \code{at = "pretty"} or \code{at = "chic"}. Plot style or theme \code{"pretty"} is an extension of our original plotting method. Plot style or theme \code{"chic"} is an implementation along the contributed packages \code{xts} and \code{PerformanceAnalytics} from the Chicago finance group members (\code{"chic"} is an abbreviation of Chicago. For both themes, \code{"pretty"} and \code{"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 they 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, or 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{ \code{NULL} (invisibly), the functions are called for the side effect of producing plots } \seealso{ \code{vignette("timeSeriesPlot", package="timeSeries")}, which provides extensive plot examples. } \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/methods-stats.Rd0000644000176200001440000000431014435105645016231 0ustar liggesusers\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{Base R functions applied to 'timeSeries' objects} \description{ Many base R statistical functions work on (the data part of) \code{timeSeries} objects without the need for special methods, e.g., \code{var}, \code{sd}, \code{cov}, \code{cor}, probability densities, and others. This page gives some examples with such functions. } %% 2023-05-29: these S4 methods don't exist! (and the functions are not generic!) %\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{x}{ % an univariate object of class \code{timeSeries}. % } % \item{y}{ % \code{NULL} (default) or a \code{timeSeries} object with compatible % dimensions to \code{x}. The default is equivalent to \code{y = x} % (but more efficient). % } % \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{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. % } %} % %\value{ % covariance or correlation matrix %} \seealso{ \code{\link{colStats}}, \code{\link{colVars}}, and other \code{colXXX} functions } \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) cor(X) } \keyword{methods} \keyword{chron} timeSeries/man/timeSeries-isRegular.Rd0000644000176200001440000000466414650724114017507 0ustar liggesusers\name{isRegular} \alias{isRegular} \alias{isRegular,timeSeries-method} \alias{isRegular.timeSeries} \alias{isDaily} \alias{isDaily,timeSeries-method} \alias{isDaily.timeSeries} \alias{isMonthly} \alias{isMonthly,timeSeries-method} \alias{isMonthly.timeSeries} \alias{isQuarterly} \alias{isQuarterly,timeSeries-method} \alias{isQuarterly.timeSeries} \alias{frequency} \alias{frequency,timeSeries-method} \alias{frequency.timeSeries} \title{Checks if a time series is regular} \description{ Checks if a time series is regular. } \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. } } \details{ What is a regular time series? If a time series is daily, monthly, or weekly, 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 its frequency can be determined by calling \code{frequency}. Here are the definitions of daily, monthly, and quarterly time series: \describe{ \item{daily}{if the series has no more than one date/time stamp per day.} \item{monthly}{if the series has no more than one date/time stamp per month.} \item{quarterly}{if the series has no more than one date/time stamp per quarter.} } A regular series is either a monthly or a quarterly series. Note that with the above definitions a monthly series is also a daily series, a quarterly series is also a monthly series. On the other hand, a daily series is not regular! NOT yet implemented is the case of weekly series. } \value{ The \code{is*} functions return \code{TRUE} or \code{FALSE} depending on whether the series fulfills the condition or not.\cr \code{frequency} returns in general 1, for quarterly series 4, and for monthly series 12. } \seealso{ \code{\link[timeDate]{isRegular}} \code{\link[stats]{frequency}} } \examples{ data(MSFT) isRegular(MSFT) # FALSE frequency(MSFT) # 1 ## a monthly ts ap <- as.timeSeries(AirPassengers) isRegular(ap) # TRUE frequency(ap) # 12 ## a quarterly ts pres <- as.timeSeries(presidents) isRegular(pres) # TRUE frequency(pres) # 4 } \keyword{chron} timeSeries/man/timeSeries-slotSeries.Rd0000644000176200001440000000430614434376477017716 0ustar liggesusers\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{coredata.timeSeries} \alias{coredata<-.timeSeries} \title{Get and set the data component of a 'timeSeries'} \description{ Get and set the data component of a 'timeSeries'. } \usage{ series(x) series(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. } } \details{ \code{series} returns the \code{@.Data} slot of a \code{timeSeries} object in \code{matrix} form. The assignment version of \code{series} replaces the values of the time series with \code{value}. The row and column names of \code{value} are used if not \code{NULL}, otherwise they are left as in \code{x}. The most natural use is when \code{value} has the same dimensions as \code{as.matrix(x)}, but if that is not the case the result is almost as if \code{value} was converted to \code{"timeSeries"} directly. Methods for \code{zoo::coredata} and its assignment counterpart are defined, as well. Users who wish to use them should ensure that \code{zoo::coredata} is visible (e.g., by calling \code{library('zoo')} or \code{library('xts')}) or employ the \code{zoo::} prefix in the calls. These methods are equivalent to \code{series} and \code{`series<-`}, respectively. } \seealso{ \code{\link{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) <- matrix(rnorm(12), ncol = 2) ts } \keyword{programming} timeSeries/man/base-merge.Rd0000644000176200001440000000452714436333306015451 0ustar liggesusers\name{merge} \docType{methods} \alias{merge} \alias{merge-methods} \alias{merge,ANY,ANY-method} \alias{merge,ANY,timeSeries-method} \alias{merge,matrix,timeSeries-method} \alias{merge,numeric,timeSeries-method} \alias{merge,timeSeries,ANY-method} \alias{merge,timeSeries,matrix-method} \alias{merge,timeSeries,missing-method} \alias{merge,timeSeries,numeric-method} \alias{merge,timeSeries,timeSeries-method} \title{Merge 'timeSeries' objects} \description{ Merges several object types with \code{"timeSeries"} objects. The number of rows must match. } \usage{ merge(x, y, \dots) } \arguments{ \item{x,y}{ objects to merge, at least one of class \code{"timeSeries"}. } \item{...}{further objects to merge.} } %\details{ %} \value{ a \code{"timeSeries"} object } \section{Methods}{ \describe{ \item{\code{signature(x = "timeSeries", y = "missing")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "ANY")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "matrix")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "numeric")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "timeSeries", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "ANY", y = "ANY")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "ANY", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "matrix", y = "timeSeries")}}{ %% ~~describe this method here~~ } \item{\code{signature(x = "numeric", y = "timeSeries")}}{ %% ~~describe this method here~~ } } } \seealso{ \code{\link{cbind}} } \examples{ ## Load Series - x <- MSFT[1:12, ] ## Merge 'timeSeries' with missing Object - merge(x) \dontshow{set.seed(1234)} ## 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{methods} \keyword{chron} \keyword{ts} timeSeries/man/internals.Rd0000644000176200001440000000120414436472544015436 0ustar liggesusers\name{internals} %\alias{.align.timeSeries} % \alias{.appendList} % \alias{.axTicksByTime2} \alias{.colorwheelPalette} % \alias{.endpoints2} % \alias{.ohlcDailyPlot} % \alias{.periodicity2} % \alias{.plotOHLC} % \alias{.xtsPlot} \title{Exported internal functions} \description{ Internal functions which are still exported only because some packages are using them. The intent is to stop exporting or removing them. } \usage{ .colorwheelPalette(n) } \arguments{ \item{n}{ } } \details{ If you think that any of these functions are useful and don't have exported analogs, please contact the maintainer. } \keyword{internal} timeSeries/man/methods-show.Rd0000644000176200001440000000422514650724114016055 0ustar liggesusers\name{print-methods} \alias{show,timeSeries-method} \alias{print.timeSeries} \title{Print 'timeSeries' objects} \description{ Print \code{"timeSeries"} objects. } \usage{ \S4method{show}{timeSeries}(object) \method{print}{timeSeries}(x, FinCenter = NULL, format = NULL, style = c("tS", "h", "ts"), by = c("month", "quarter"), ...) } \arguments{ \item{object,x}{ an object of class \code{"timeSeries"}. } \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, a character string with the format in POSIX notation. } \item{style}{ a character string, one of \code{"tS"}, \code{"h"}, or \code{"ts"}. } \item{by}{ a character string, one of \code{"month"}, \code{"quarter"}. } \item{\dots}{ arguments passed to the print method for the data part, which is a \code{"matrix"} or, in the case of \code{style = "ts"}, to the print method for class \code{"ts"}. } } % \item{recordIDs}{ % for the \code{print} method, a logical value - should the % \code{recordIDs} be printed together with the data matrix and time % series positions? \details{ \code{show} does not have additional arguments. The \code{print} method allows to modify the way the object is shown by explicitly calling \code{print}. The default for \code{style} is \code{tS}. For univariate time series \code{style = "h"} causes the object to be printed as a vector with the time stamps as labels. Finally, \code{style = "ts"} prints like objects from base R class \code{"ts"}, which is suitable for quarterly and monthly time series. } \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 LPP # equivalently, show(LPP) ## a short subseries to demo 'print' hC <- head(MSFT[ , "Close"]) class(hC) print(hC) print(hC, style = "h") } \keyword{chron} timeSeries/man/timeSeries-slotTime.Rd0000644000176200001440000000347314434446143017352 0ustar liggesusers\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{time<-.default} \alias{getTime} \alias{setTime<-} \description{ Functions and methods extracting and modifying positions of 'timeSeries' objects. } \usage{ \S4method{time}{timeSeries}(x, \dots) \method{time}{timeSeries}(x) <- value getTime(x) setTime(x) <- value } \arguments{ \item{value}{ a valid value for the time component of \code{x}. } \item{x}{ an object of class \code{timeSeries}. } \item{\dots}{ optional arguments passed to other methods. } } \details{ \code{time} and \code{time<-} are generic functions with methods for class \code{"timeSeries"}. They get and set the time component of the object. \code{getTime} and \code{setTime} are non-generic alternatives are non-generic wrappers of \code{time} and \code{time<-}, respectively. There is another generic function \code{time<-} defined in package \pkg{zoo}. When that package is loaded its \code{time<-} gets the \code{"timeSeries"} method. Also, if \code{"time<-"} is called with an object from class other than \code{"timeSeries"}, the call is dispatched to \code{"zoo:time<-"} to apply a suitable method. } \value{ for \code{time} and \code{getTime}, a \code{"timeDate"} object, for \code{time<-} and and \code{setTime}, the modified \code{"timeSeries"} 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/methods-comment.Rd0000644000176200001440000000146514436344170016544 0ustar liggesusers\name{comment} \alias{comment} \alias{comment<-} \alias{comment,timeSeries-method} \alias{comment<-,timeSeries-method} \title{Get and set comments for 'timeSeries' objects} \description{ Get 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 vector, the comment. } } \details{ Objects from class \code{"timeSeries"} have a slot for documentation. These functions get and change its contents. } \examples{ ## Get description from a 'timeSeries' - comment(LPP2005REC) ## Add User to comment - comment(LPP2005REC) <- paste(comment(LPP2005REC), "by User Rmetrics") comment(LPP2005REC) } \keyword{chron} timeSeries/man/timeSeries-getDataPart.Rd0000644000176200001440000000060514436333424017743 0ustar liggesusers\name{DataPart,timeSeries-method} \alias{getDataPart,timeSeries-method} \alias{setDataPart,timeSeries-method} \title{DataPart,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/DESCRIPTION0000644000176200001440000000305114673766373014116 0ustar liggesusersPackage: timeSeries Title: Financial Time Series Objects (Rmetrics) Version: 4041.111 Authors@R: c(person("Diethelm", "Wuertz", role="aut", comment = "original code") , person("Tobias", "Setz", role = c("aut"), email = "tobias.setz@live.com") , person("Yohan", "Chalabi", role = "aut") , person("Martin","Maechler", role="ctb", email="maechler@stat.math.ethz.ch", comment = c(ORCID = "0000-0002-8685-9910")) , person(given = c("Georgi", "N."), family = "Boshnakov", role = c("cre", "aut"), email = "georgi.boshnakov@manchester.ac.uk") ) Description: 'S4' classes and various tools for financial time series: Basic functions such as scaling and sorting, subsetting, mathematical operations and statistical functions. Depends: R (>= 2.10), timeDate (>= 4041.110), methods Imports: graphics, grDevices, stats, utils Suggests: RUnit, robustbase, xts, zoo, PerformanceAnalytics, fTrading LazyData: yes License: GPL (>= 2) URL: https://geobosh.github.io/timeSeriesDoc/ (doc), https://r-forge.r-project.org/scm/viewvc.php/pkg/timeSeries/?root=rmetrics (devel), https://www.rmetrics.org BugReports: https://r-forge.r-project.org/projects/rmetrics NeedsCompilation: no Packaged: 2024-09-21 13:05:35 UTC; georgi Author: Diethelm Wuertz [aut] (original code), Tobias Setz [aut], Yohan Chalabi [aut], Martin Maechler [ctb] (), Georgi N. Boshnakov [cre, aut] Maintainer: Georgi N. Boshnakov Repository: CRAN Date/Publication: 2024-09-22 10:10:03 UTC