search for: xfreq

...[.ts" <- function (x, i, j, drop = TRUE) { y <- NextMethod("[") if (missing(i)) ts(y, start = start(x), freq = frequency(x)) else y } "window.ts" <- function (x, start, end, frequency, deltat) { x <- as.ts(x) xtsp <- tsp(x) xfreq <- xtsp[3] xtime <- time(x) ts.eps <- .Options$ts.eps if (missing(frequency) && missing(deltat)) yfreq <- xfreq else if (missing(deltat)) yfreq <- frequency else if (missing(frequency)) yfreq <- 1/deltat if (xfreq%%yfreq &...

>>>>> On Fri, 02 Jul 1999, Adrian Trapletti <Adrian.Trapletti@wu-wien.ac.at> said: Adrian> There seems to be a problem with plot.ts (R Version 0.64.2) > x<-cbind(1:10,2:11) > x<-as.ts(x) > plot(x) Adrian> Error: subscript (20) out of bounds, should be at most 10 This is definitely a bug --> CC: R-bugs ALL NOTE : This is *not* new

...e ldeaths data: > spectrum(ldeaths, col = "blue") > spec.ar(ldeaths, add = TRUE) the resulting plots do not compare over the same frequency range. This results because spec.ar defines frequency as > freq <- seq.int(0, 0.5, length.out = n.freq) whereas spec.pgram uses > xfreq <- frequency(x) > N <- nrow(x) > Nspec <- floor(N/2) > freq <- seq.int(from = xfreq/N, by = xfreq/N, length.out = Nspec) And so the reason the spectral estimates of lh are similar is that frequency(lh) = 1, whereas frequency(ldeaths) = 12. The documentation seems more extensi...

...nel = NULL, taper = 0.1, pad = 0, fast = TRUE, demean = FALSE, detrend = TRUE, plot = TRUE, na.action = na.fail, ...) { ## Estimate spectral density from (smoothed) periodogram. series <- deparse(substitute(x)) x <- na.action(as.ts(x)) xfreq <- frequency(x) x <- as.matrix(x) N <- N0 <- nrow(x) nser <- ncol(x) if(!is.null(spans)) # allow user to mistake order of args kernel <- { if(is.tskernel(spans)) spans else kernel("modified.daniell", spans %/% 2) }...

...n this convulated way there is a step that I can't solve. I used a "fake" timevar using the table function. df <- data.frame(id=c(rep('IDa',3), rep('IDb', 5), rep('IDc', 2), rep('IDd',5)), let=letters[1:5]) #add Freq to each id xFreqdf <- merge(df, table(df['id']), by.x='id', by.y='Var1') xFreq <- xFreqdf[,'Freq'] #general way of transforming xFreq into: xFreq2 <- c(3:1, 5:1, 2:1, 5:1) #substitute Freq by xFreq2 xFreqdf['Freq'] <- xFreq2 #get final df xReshape <- reshape(...

...06.5, frequency = 0.2) bar = ts(x, start = 2.5, end = 107.5, frequency = 0.2) window(foo, start = 20, end = 30, extend=TRUE) # Time Series:# Start = 20 # End = 25 # Frequency = 0.2 # [1] 5 6 window(bar, start = 20, end = 30, extend=TRUE) # Error in attr(y, "tsp") <- c(ystart, yend, xfreq) : # invalid time series parameters specified The reason is in the rounding procedures for ystart and yend at the end of the stats::window function. For the 'foo' series the ystart and yend values are calculated as: c(20, 25), whereas for the 'bar' series, they become c(20, 30) a...

...to patch this could be by the two commented assignments added by me to the code below to prepend a 0 to tt and then drop the first element of 'keep' to allow correct indexing later. Georgi Boshnakov > stats:::na.contiguous.default function (object, ...) { tm <- time(object) xfreq <- frequency(object) if (is.matrix(object)) good <- apply(!is.na(object), 1L, all) else good <- !is.na(object) if (!sum(good)) stop("all times contain an NA") tt <- cumsum(!good) ## tt <- c(0, tt) ln <- sapply(0:max(tt), function(i) su...

...o, start = 20, end = 30, extend=TRUE) >> >> # Time Series: >> # Start = 20 >> # End = 25 >> # Frequency = 0.2 >> # [1] 5 6 >> >> window(bar, start = 20, end = 30, extend=TRUE) >> >> # Error in attr(y, "tsp") <- c(ystart, yend, xfreq) : >> # invalid time series parameters specified >> >> The reason is in the rounding procedures for ystart and yend at the end > of the stats::window function. For the 'foo' series the ystart and yend > values are calculated as: c(20, 25), whereas for the 'bar&...

....5, frequency = 0.2) > > window(foo, start = 20, end = 30, extend=TRUE) > > # Time Series: > # Start = 20 > # End = 25 > # Frequency = 0.2 > # [1] 5 6 > > window(bar, start = 20, end = 30, extend=TRUE) > > # Error in attr(y, "tsp") <- c(ystart, yend, xfreq) : > # invalid time series parameters specified > > The reason is in the rounding procedures for ystart and yend at the end of the stats::window function. For the 'foo' series the ystart and yend values are calculated as: c(20, 25), whereas for the 'bar' series, they beco...

...eries: >>>> # Start = 20 >>>> # End = 25 >>>> # Frequency = 0.2 >>>> # [1] 5 6 >>>> >>>> window(bar, start = 20, end = 30, extend=TRUE) >>>> >>>> # Error in attr(y, "tsp") <- c(ystart, yend, xfreq) : >>>> # invalid time series parameters specified >>>> >>>> The reason is in the rounding procedures for ystart and yend at the end >>> of the stats::window function. For the 'foo' series the ystart and yend >>> values are calculated...

...; > >> # Time Series: > >> # Start = 20 > >> # End = 25 > >> # Frequency = 0.2 > >> # [1] 5 6 > >> > >> window(bar, start = 20, end = 30, extend=TRUE) > >> > >> # Error in attr(y, "tsp") <- c(ystart, yend, xfreq) : > >> # invalid time series parameters specified > >> > >> The reason is in the rounding procedures for ystart and yend at the end > > of the stats::window function. For the 'foo' series the ystart and yend > > values are calculated as: c(20, 25),...

Hi everyone, first of all, I would like to say that I am a newbie in R, so I apologize in advance if my questions seem to be too easy for you. Well, I'm looking for periodicity in histograms. I have histograms of certain phenomenons and I'm asking whether a periodicity exists in these data. So, I make a periodogram with the function spec.pgram. For instance, if I have a histogram h, I

...e ldeaths data: > spectrum(ldeaths, col = "blue") > spec.ar(ldeaths, add = TRUE) the resulting plots do not compare over the same frequency range. This results because spec.ar defines frequency as > freq <- seq.int(0, 0.5, length.out = n.freq) whereas spec.pgram uses > xfreq <- frequency(x) > N <- nrow(x) > Nspec <- floor(N/2) > freq <- seq.int(from = xfreq/N, by = xfreq/N, length.out = Nspec) And so the reason the spectral estimates of lh are similar is that frequency(lh) = 1, whereas frequency(ldeaths) = 12. The documentation seems more extensi...