similar to: Box.test degrees of freedom

Dear colleagues, Can anyone of you tell me how to write a "nabla" symbol in an R graph? Thanks in advance, Nuno ______________________________________________ Centro de Oceanografia - IO-FCUL, Portugal Center for Quantitative Fisheries Ecology - ODU, USA [[alternative HTML version deleted]]

Hi, When I run this code a part of my x-axis and y-axis changes color. Can somebody tell me what is wrong? Also, is there a way to control the color of the average lines? monthplot(AirPassengers+500, ylim=c(min(AirPassengers), max(AirPassengers+500)), ylab="") par(new=T) monthplot(AirPassengers, col="blue", ylim=c(min(AirPassengers), max(AirPassengers+500)),

Can someone explain me this spec.pgram effect? Code: period.6<-c(0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10 ,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10,0,0,0,0,0,10) period.5<-c(0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10 ,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0,0,0,0,0,0,10,0,0,0,0,10,0) par(mfrow=c(2,1))

Dear colleagues, ?arima? returns directly the 1-step ahead errors but I am interested in obtaining other h-step ahead errors for several ARIMA models I have fitted. Is there any way I can obtain this with R? Any help would be appreciated. Sincerely, Nuno Prista _________________________ CO - FCUL, Lisboa, Portugal CQFE - ODU, Norfolk, USA

Dear R colleagues, This seems pretty straight forward but I have been banging my head on this for some time and can't seem to find a solution suppose I have something like a1<-1; a2<-2; a3<-3; a4<-4; b1<-3; b2<-4 I would like to quickly assign to objects with a certain pattern, e.g., those in ls(pattern="a") a specific value, e.g., "99", without

After an RSiteSeach("Box.test") I found some discussion regarding the degrees of freedom in the computation of the Ljung-Box test using Box.test(), but did not find any posting about the proper degrees of freedom. Box.test() uses "lag=number" as the degrees of freedom. However, I believe the correct degrees of freedom should be "number-p-q" where p and q are

Dear list members, I have 982 quotations of a given stock index and I want to run a Ljung-Box test on these data to test for autocorrelation. Later on I will estimate 8 coefficients. I do not know how many degrees of freedom should I assume in the formula for Ljung-Box test. Could anyone tell me please? Below the formula: Box.test(x, lag = ????, type = c("Ljung-Box"), fitdf = 0)

Full_Name: Peter Solymos Version: 2.8.1 OS: Windows Submission from: (NULL) (129.128.141.92) The help page of the Box.test function (stats) states that the Ljung-Box test was published in: Ljung, G. M. and Box, G. E. P. (1978), On a measure of lack of fit in time series models. Biometrika 65, 553--564. The page numbers are incorrect. The correct citation should be as follows: Ljung, G. M.

Dear colleagues, I am relatively new to R and time series and so I am experiencing difficulties in interpreting the output of "arima" in MA models (but not in AR models). I cannot make sense of the 1st innovations returned by "arima". In an AR(1) model I expect data[t]=phi1*data[t-1]+a[t] and in a MA(1) model data[t]=a[t]+theta1*a[t-1]. My interpretation from R-help is

Hello, I am using the Ljung Box test in R to compute if the resiudals of my fitted model is random or not. I am not sure though what the results mean, I have looked at various sources on the internet and have come up with contrasting explanations (mainly because these info deal with different program languages, like SAS, SPSS, etc). I know that my residuals should appropriate white noise( is

Hi all I'm using the Box-Ljung test (from within R) to test if a time-series in independently distributed. 2 questions: 1) p-value returned by Box-Ljung: IF I want to test if the time-series is independant at say 0.05 sig-level (it means that prob of erroneously accepting that the time-series is independent is 0.05 right?) --> then do I consider time-series as "independant"

I fit a simple linear model y = bX to a data set today, and that produced 24 residuals (I have 24 data points, one for each year from 1984-2007). I would like to test the time-independence of the residuals of my model, and I was recommended by my supervisor to use the Ljung-Box test. The Box.test function in R takes 4 arguments:  x a numeric vector or univariate time series. lag the statistic

Hi, I am far from experienced in both R and time series hence the question. The code for spec.pgram() seems to involve a circularity of the kernel (see below) yielding new power estimates to all frequencies computed by FFT. " if (!is.null(kernel)) { for (i in 1:ncol(x)) for (j in 1:ncol(x)) pgram[, i, j] <- kernapply(pgram[, i, j], kernel, circular = TRUE)

Hi, James S. Clark - Statistical computation for environmental sciences in R - mentions a function centralm (pg25) that I believe should be present in the base package but I can't find it. It is supposed to calculate means, variances, skewness, kurtosis of arrays. Does it exist in some other package? Thanks, Nuno Prista [[alternative HTML version deleted]]

Hi All R Experts, I met with below mentioned statistics in paper "Stock Index Volatility Forecasting with High Frequency Data" by Eugenie Hol, Siem Jan Koopman http://ideas.repec.org/p/dgr/uvatin/20020068.html I would like to ask that what is "Box-Ljung portmantacau statistic based on N squared autocorrelation" ? Is it same as "Box-Ljung Statistics" of stats

Dear All, I want to create a loop within a function r. The example follows: Box.test (lfut, lag = 1, type="Ljung") if i want to compute the Box.test for lag 1 to 10, I have to write manually change each time for different lag. So i wan to write a loop for the lag 1 to 10 and return the statistics for each lag. Is there any method to do this ? With regards, Upananda -- You may

I'm analizing the Argentina stock market (merv) I download the data from yahoo library(tseries) Argentina <- get.hist.quote(instrument="^MERV","1996-10-08","2003-11-03", quote="Close") merv <- na.remove(log(Argentina)) I made the Augmented Dickey-Fuller test to analyse if merv have unit root: adf.test(merv,k=13) Dickey-Fuller = -1.4645,

Hi all, Can anyone clear my doubts about what conclusions to take with the following what puts of some time series tests: > adf.test(melbmax) Augmented Dickey-Fuller Test data: melbmax Dickey-Fuller = -5.4075, Lag order = 15, p-value = 0.01 alternative hypothesis: stationary Warning message: p-value smaller than printed p-value in: adf.test(melbmax)

Hi: I am trying to do a spurious regression in R but I can not find the function. Anybody used it before? The problem I have is try to do a regression with several time series. An alternative is to use the GLS function to fit the linear regression with the correlation structure AR(3) for the response (or residual). I hope the residuals after the GLS regression will be independent judged by

two ARIMA models, both have several bars signicant in ACF and PACF plots of their residuals, but when run Ljung Box tests, both don't show any significant correlations... however, one model has p-value that is larger than the other model, based on the p-values, can I say the model with larger p-values should be better than the model with smaller p-values? [[alternative HTML version