similar to: Having trouble with tsdiag function on a time series

I am using the ts package to fit ARIMA models, and the tsdiag() function to plot diagnostics. In doing so I'm generating an awful lot of diagnostic plots of different models and different data sets all within the same R session. So my question is, is there an option in tsdiag() similar to <main="Title"> that I can use? This would be quite helpful when I print out the plots,

I've just built R 2.9.2 from source on Slackware Linux 13.0 - 32-bit (will try 64-bit also next) - and seen: > Collecting examples for package 'stats' > Running examples in package 'stats' > Error: testing 'stats' failed > Execution halted > make[3]: *** [test-Examples-Base] Error 1 Looking at R-2.9.2/tests/Examples/stats-Ex.Rout.fail I see: ... >

Hi, I've noted that not all `predict' methods are hidden in the namespace: > methods("predict") [1] predict.ar* predict.Arima* [3] predict.arima0* predict.glm [5] predict.HoltWinters* predict.lm [7] predict.loess* predict.mlm [9] predict.nls* predict.poly [11] predict.ppr* predict.prcomp* [13]

Hi R-Helpers, I'm dealing with the STL procedure and trying to apply the tsdiag and StructTS onto the ts object to analyse the different parameters which need to be set. How can I use the tsStructure & tsdiag to create a seasonal, trend and cycle subseries plot so that I can select & analyse the correct np,ns, nt and nl? The problem is that too much signal goes into the seasonal

HI list, I want to know whether tsdiag uses k-(p+q) as the lag in ljung box test. How is it possible to save those values nuncio -- Nuncio.M Research Scientist National Center for Antarctic and Ocean research Head land Sada Vasco da Gamma Goa-403804 [[alternative HTML version deleted]]

Hi all, I am modelling a time series with missing data. *Q1)* However, I am not sure if I should use the next *graphics* to understand my data: *a)* ACF & PACF (original series) *b)* ACF & PACF (residuals) * * *Q2)* I am using *tsdiag*, so I obtain a graphic with 3 plots: stand. residuals vs time; acf for residuals; Ljung-Box for residuals (it is wrong for residuals). I know that using

Hi, I am newbie user of ggplot and would like some assistance in implementing time series plots. I'd like to know how the tsdiag plot can be made in ggplot? Thanks Harsh Singhal Decisions Systems, Mu Sigma Inc.

The "tsdiag" function in the TSA package overrides the "tsdiag" function in the "stats" package. There are a few annoying bugs in the TSA's version of the function so I would like to use the "stats" function but still have access to other TSA functions. I have tried using stats::tsdiag( ) but as long as the TSA package is attached the function from

Dear Everyone, The document of `tsdiag? says > These tests are sometimes applied to the residuals from an ARMA(p, q) fit, in which case the references suggest a better approximation to the null-hypothesis distribution is obtained by setting fitdf = p+q, provided of course that lag > fitdf. This implies that we should pass the `fitdf' parameter when applying `Box.test' to

Hi R experts, I am trying to remove autocorrelation from Simple Moving Average time series. I know that this can be done by using seasonal ARIMA like, library(TTR) data <- rnorm(252) n=21 sma_data=SMA(data,n) sma_data=sma_data[-1:-n] acf(sma_data,length(sma_data))

Dear All I am a biginner of R. I have difficulty with reading the code of a method. I am using the vars package to estimate a VAR model and I want to view the code of "predict" method for objects with class attribute "varest". I thougt I could just type the name "predict" without anything to display the code of the method as I often do with generic function.

Oh yes. The "load package" under the "packages menu" in the Windows version does that. To check I typed "library(assist)" after starting R. Same behavior, ssr is found, but others like predict.ssr, and plot.ssr, give a "not found" message. Thanks for the suggestion. Mike

Hello. I just downloaded R onto a new computer, and after entering library(MASS), I still get the message "Error: could not find function "predict.lda"" when I try to use the predict.lda function (even just "predict.lda()") Can anyone help me out? Thank you! Diana Connett

Hi Every Body, I don't know why some regression functions have no related prediction function. For example lowess, loess, lokerns, lpridge, lpepa, and ksmooth. What could help? Is there any global or wrapper function so that can help? Regards, Amir Safari --------------------------------- [[alternative HTML version deleted]]

Hello, I just ran the predict.StructTS function using the AirPassengers data and got a ridiculous result. Here's what I ended up with: http://24.210.155.111/PredictWhat!.pdf Who wrote this? Am I seriously supposed to think this function would accurately predict a time series? -AnalogKid

Hi everybody, I'am very interested with the basic structural model of time series. So I used the command "StructTS" but I failed to obtain a desirable output, in fact when I write in R Console the following lines: > x=(1,2,3,4,5,2,25,14,12,13,11,6,9,24,12,13,14,12,12,14,11,12,14,15,20,21,22,23,21,25,28) >(fit <- StructTS(x,type = "BSM")) I obtained the following

Good Morning! May you help me? I need to understand the function predict. I need to understand the algorithm implemented, the calculations associated. Where can I find this information? Thank You!

I have a data file with a given time series of price data and I would like to split the time series into a test set and training set. I would then like to build an ARIMA model on the training set and apply this model on test set. Below is some code: [CODE] data= read.table("A.txt",sep=",") attach(data) training = data[1:120, 6] test = data[121:245, 6] ts1 = ts(training) ts2 =

Hi all, I've experienced this problem using StructTS(x) when the *first* element of x is a NA (R:R1.7.0, os: w2ksp4). Please look at the following code: a=rep(1:7,10) library(ts) #this works StructTS(a) #this works x=a x[2]=NA StructTS(x) #this doesn't work x=a x[1]=NA StructTS(x) The last command returns this error "Error in optim(init[mask], getLike,

>From: "Ivaha C (AT)" <civaha at glam.ac.uk> >Date: 2008/04/10 Thu AM 08:51:14 CDT >To: R Help <r-help at r-project.org> >Subject: [R] Structural Modelling in R-project if you have a univariate time series and you want to break it into its various components, then you get the scalars based on a decomposition. if you have a kalman filter/ basic strucutural