similar to: Comparing lagged time series

Hello all, I have been using R's time series capabilities to perform analysis for quite some time now and I am having some questions regarding its reliability. In several cases I have had substantial disagreement between R and other packages (such as gretl and the commercial EViews package). I have just encountered another problem and thought I'd post it to the list. In this case,

Dear all, Before I file this as a bug, I wanted to check if I didn't miss something. The help page of lag() says that the function returns a time series object. It actually does return something that looks like a ts object (the attribute tsp is set). But when using a vector, the class "ts" is not added to the result: > avec <- 1:10 > lag(avec) [1] 1 2 3 4 5 6 7 8

Suppose I have multiple time series with names for each one, for example, x <- ts(matrix(rnorm(30,0,1),10,3), names=c("Juan", "Tuey", "Trey"), frequency=4) So now, as I start to explore these series, if I do everything at once, the names stay attached to the series. For example, plot(x) # gives a plot of the series with their names acf(x) # gives the ACFs &

Hello! Recently I got report that my package mar1s doesn't pass checks any more on R 3.0.2. I started to investigate and found the following difference in multivariate time series handling in R 3.0.2 compared to R 2 (I've checked on 2.14.0). Suppose I wish to calculate seasonal component for time series. In case of multivariate time series, I wish to process each column independently.

hi guys, i have some trouble in creating lagged variables to use as external regressors. i'm trying to use lag(x) but it gives me as result the same time series (x), adding this part at the end: attr(,"tsp") [1] 0 2323 1 where do i wrong?are there other functions to be used? thanks sara -- View this message in context:

On Mon, 24 May 2004 12:00:46 +0200 v.demartino2@virgilio.it wrote: > Working - among other things- in the field of (short & long term) electricity > forecast, * * * > we have to comply with the Tramo-seats closed-source procedure (http://www.bde.es/informes/be/docs/dt0014e.pdf) > to deal with seasonality of electricity monthly time-series, in line with > the methodology

Rob J Hyndman gives great explanation here (https://robjhyndman.com/hyndsight/estimation/) for reasons why results from R's arima may differ from other softwares. @iacobus, to cite one, 'Major discrepancies between R and Stata for ARIMA' (https://stackoverflow.com/questions/22443395/major-discrepancies-between-r-and-stata-for-arima), assign the, sometimes, big diferences from R

Hello!!, for linear models fit I use Gretl, but now I'm starting to use R, I would like to know if is there some function to obtain a extended summary like in Gretl. I will write a example in Gretl Modelo 1: MCO, usando las observaciones 1968-1982 (T = 15) Variable dependiente: Invest Coeficient St error t-ratio p-value const 377,631 35,0955 10,7601 <0,00001 *** GNP

There is now a preliminary version of a time series package in the R-devel snapshots, and we would welcome feedback on it. It is based in part on the packages bats (Martyn Plummer) and tseries (Adrian Trapletti) and in part on code I had or have written. (Thanks for the contributions, Martyn and Adrian!) Some of the existing ts code has been changed, for example to plot multiple time series, so

This one showed up while looking at one of Ripley's other reports: > data(freeny) > model.frame(y~1,data=freeny,subset=1:10) y 1962.25 8.79236 1962.5 8.79137 1962.75 8.81486 1963 8.81301 1963.25 8.90751 1963.5 8.93673 1963.75 8.96161 1964 8.96044 1964.25 9.00868 1964.5 9.03049 > model.frame(y~1,data=freeny,subset=1:10)$y Warning: Replacement length not a

Time Series functions in R ========================== I think a good basic S-like functionality for library(ts) in base R would include ts class, tsp, is.ts, as.ts plot methods start end window frequency cycle deltat lag diff aggregate filter spectrum, spec.pgram, spec.taper, cumulative periodogram, spec.ar? ar -- at least univariate by Yule-Walker arima -- sim, filter, mle, diag, forecast

I'm playing with predict.Arima in the 3/19/02 development snapshot of R-devel. The following produces an error message because x is not of class "ts": R> x <- rnorm(20) R> obj <- arima(x, c(2,0,0)) R> predict(obj) Error in round(x, digits) : Non-numeric argument to mathematical function Granted the documentation for arima says x should be a time-series, but

Hola!! A ver si alguien puede ayudarme!! Para ajuste de modelos lineales normalmente uso Gretl. Ahora estoy empezando a hacerlo en R. Me gustaría saber si existe alguna función que haga un summary extendido como el de Gretl. Os pongo un ejemplo del summary de Gretl. Modelo 1: MCO, usando las observaciones 1968-1982 (T = 15) Variable dependiente: Invest              Coeficiente   Desv. Típica

Dear all, I'm confused by the time serie parameters in data(lh) : sueoka:~ lobry$ R --vanilla --quiet > tsp(lh) [1] 1 48 1 because documentation says: QUOTE A regular time series giving the luteinizing hormone in blood samples at 10 mins intervals from a human female, 48 samples. UNQUOTE So that I would expect the time serie to end at 480 minutes or 8 hours. Shouldn't we have

Dear R gurus I would like to take a monthly time series and convert it to a data frame without losing the tsp items, pleae I've tried as.data.frame and data.frame but I get the series without the time element. Any suggestions, please? tia Edna Bell

Dear Helpers, I am looking for methods and tools to compare and then to concatenate two successive time series. They are both in the same frequency and they describe one phenomena. There is no time gap between them. The problem is that the method of measurements has changed between both time series and they are no statistically the same. I would like to merge them to receive one homogeneous

I tried toy problems and there doesn't seem to be a basic problem between lm() and ts objects: X = data.frame(x=c(1,2,7,9), y=c(7,2,3,1)) lm(y ~ x, X) X <- lapply(X, function(x) ts(x, frequency=12, start=c(1994,7))) lm(y ~ x, X) and this works fine - whether you do an lm() before or after making ts objects, it's okay. But I have a situation where things aren't okay.

Hello, I am wondering how I should set up the tsp attribute (available through attr(x, "tsp")) of a dataset x? Let's assume that x has 100 points, and I want to set the frequency to 4. I tried: > attr(x,"tsp")<-c(1,100,4) Error in attr(x, "tsp") <- c(1, 100, 4) : invalid time series parameters specified Is there any other way to set the frequency of

Greetings, I am having trouble getting the function reformulate_ATSP_as_TSP to work for me. I have provided a simple example of some of the code I've been using. In particular, I'm not sure why I'm getting the error "Error in dimnames(tsp) <- list(lab, lab) : length of 'dimnames' [1] not equal to array extent" since I created the object ATSP with a valid

If value is NULL I think tsp assignment should not return a class ts object. Below is a fixed version. Paul Gilbert ____ "tsp<-" <-function(x, value) {if (is.null(value)) {attr(x, "tsp") <- value if(inherits(x,"ts")) class(x) <- NULL return(x) } attr(x, "tsp") <- value class(x) <- "ts" x }