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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]]

...ries I was expecting to see a signal occurring at frequency 1/5 but it does not. Can anybody explain me why? Thanks in advance, Nuno ______________________________________________ Centro de Oceanografia - IO-FCUL, Portugal Center for Quantitative Fisheries Ecology - ODU, USA email: nmprista*@fc.ul.pt ; nprista*@odu.edu [[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)),

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

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, Hope this does not sound too ignorant . I am trying to detrend and transform variables to achieve normality and stationarity (for time series use, namely spectral analysis). I am using the boxcox transformations. As my dataset contains zeros, I found I need to add a constant to it in order to run "boxcox". I have ran tests adding several types of constants, from .0001

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 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

Dear all, I can't seem to run what seems (to me!) like a common SQL statement in RODBC: data <- sqlQuery(a, 'SELECT TO_CHAR (DB1.DATE, 'yyyy') "year" FROM DB1') In contrast, data <- sqlQuery(a, 'SELECT TO_CHAR(DB1.DT_INICIO) "year" FROM DB1') works but it does not come up as I want it to (i.e., in "year" format). Can

Dear colleagues, I am new to R and statistics so please keep that in mind. I have doubts on the df calculation of Ljung-Box test (Box.test). The function seems to use always the df=lag=m and not df=m-p-q like suggested in Ljung and Box (1978) paper (that is referenced). Do you agree with this? If so, is there an R package function that computes Ljung-Box test with the degrees of