similar to: list assignment

Hello, Using the dse package I have estimated a VAR model using estVARXls(). I can perform forecasts using forecast() with no problems, but when I try to use simulate() with the same model, I get the following error: Error in diag(Cov, p) : 'nrow' or 'ncol' cannot be specified when 'x' is a matrix Can anyone shed some light on the meaning of this error? How can I

(My apologies if this is a repeated posting. I couldn't find any trace of my previous attempt in the archive.) I'm having trouble with forecast() in the dse2 package. It works fine for me on a model without a trend, but gives me NaN output for the forecast values when using a model with a trend. An example: # Set inputs and outputs for the ARMA model fit and test periods

Folks, I have been using the VAR {vars} program to find a fit for the following bi-variate time series (subset): bivariateTS<-structure(c(0.950415958293559, 0.96077848972081, 0.964348957109053, 0.967852884998915, 0.967773510751625, 0.970342843688257, 0.97613937178359, 0.980118627997436, 0.987059493773907, 0.99536830931504, 1.00622672085718, 1.01198013845981, 1.01866618122606,

I am trying to do a forecasting exercise for a series, x. My forecast model consists of the following I first regress log(x) on time and dummy variables for each month. lm(log(x) ~ time + monthly dummies) I then use predict() to obtain a prediction for the next year. I then fit an AR(6)/AR(12) model on the residuals of the regression. I use predict() here also to obtain the prediction for the

My guess would be that if you inspect the output from ma(dat3[1:28], order=3) you will find some NAs in it. And then forecast() doesn't like NAs. But I can't check, because I can't find the ma() and forecast() functions. I assume they come from some package you installed; it would be helpful to say which package. -Don -- Don MacQueen Lawrence Livermore National Laboratory 7000

What is the Rd file alias and usage syntax for an S4 assignment method? I have been trying variations on \alias{TSdoc<-,default-method} \usage{ \S4method{TSdoc}{default}(x) <- value but so far I have not got it right according to various codoc, etc, checks. Paul Gilbert ==================================================================================== La version fran?aise

1. This is an imaginary data on monthly outcomes of 2 years and I want to forecast the outcome for next 12 months of next year. data Data1; input Yr Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec; datalines; 2008 12 13 12 14 13 12 11 15 10 12 12 12 2009 12 13 12 14 13 12 11 15 10 12 12 12 ; run; I converted the above data into the below format to use it in R as it was giving error: asking

Hi all, I use kriging to interpolate the precipitation from stations, but the map of this results show lots of stripes. (please see the attachment)I think there's something wrong with the setting of the dimension of this matrix, however, I have no idea how to know or test to see if this setting is correct or not.I've tried to switch the latitude and longitude, but still got the same

Good day, I'm using a VAR model to forecast sales with some extra variables (google trends data). I have divided my dataset into a trainingset (weekly sales + vars in 2006 and 2007) and a holdout set (2008). It is unclear to me how I should predict the out-of-sample data, because using the predict() function in the vars package seems to estimate my google trends vars as well. However, I want

Hi, I have been using this website ( http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm http://www.stat.pitt.edu/stoffer/tsa2/Rissues.htm ) to help me to fit ARIMA models to my data. At the moment I have two possible methods to use. Method 1 If I use arima(ts.data, order=c(1,2,0), xreg=1:length(ts.data)) then the wrong value for the intercept/mean is given (checked on SPSS and Minitab) and

Hi all, I am playing with some companies' balance sheets and income statements and want to apply what I've just learned from Stats class to see if I can forecast the companies earnings, sales and gross margin in the short term (3rd and 4th Quarter), mid-term (2009) and long term (2011, etc. ) I pulled up some data from companies' financial statements over the past a few years. The

Hi, I'm trying to plot the forecasts I generated using the Plot.Fore function of the BootPR package. But I got an error from zoo: My data: Time Series: Start = 1 End = 18 Frequency = 1 [1] 38731 38628 39117 92809 71984 31226 58613 72360 107956 92066 [11] 95208 99098 95848 120383 110717 105680 98469 101916 Script: y1<-ts(y1);

We want to compute a pseudo R-squared for a model whose parameter estimation was based on maximum likelihood (function likfit, package geoR). I tried to compute the R2 proposed by Maddala (1983) which compare the maximized likelihood for the model without any predictor and the maximized likelihood for the model with all predictors. I got a really low value (0,01%). Did I miss something? Are

Hello, I am very new to R and Time Series. I need some help including R codes about the following issues. I' ll really appreciate any number of answers... # I have a time series data composed of 24 values: myinput = c(n1,n2...,n24); # In order to make a forecasting a, I use the following codes result1 = arima(ts(myinput),order = c(p,d,q),seasonal = list(order=c(P,D,Q))) result2 =

Hi R-users, I have a question relating to the HoltWinters() function. I am trying to forecast a series using the Holt Winters methodology but I am getting some unusual results. I had previously been using R for Windows version 2.7.2 and have just started using R 2.9.1. While using version 2.7.2 I was getting reasonable results however upon changing versions I found I started to see unusual

I have an R script with the following applicable lines: xshort <- window(s, start=st, end=ed) . . . xshort <- ts(xshort, frequency=1, start=1) . . . m1 <- m2 <- m3 <- m4 <- m5 <- m6 <- NULL m1 <- tslm(xshort ~ trend) I get an error: Error in get(dataname) : object 'xshort' not found When I do traceback() I get: 3: get(dataname) 2: tslm(xshort ~

Good morning, I hope someone can help with these questions, or perhaps suggest one of the other R-lists? I have two questions: 1. Why am I getting this warning? 2. Why is the second example "Point Forecast" the same value, I do not see that in previous attempts with similar but different data sets as in example 1? Example1: dat3 <- structure(c(3539122.86, 3081383.87,

Hey, I am using the ets() function in the forecast package to find out the best fit parameters for my time-series. I have about 50 sets of time series data. I'm currently using the function as follows: ets(x,model="AZZ",opt.crit="mse") As to my observation about 5-10 of them have been identified by ets to have a trend and an alpha, beta values have been thrown up -

Hello, I used glsm.mcmc and likfit.glsm to create model. Now I want to predict at different locations, but I can't get glsm.krige to work. I keep getting the error that trend.d and trend.l must have similar specifications. I have tried numerous ways to include the covariates in the glsm.krige model, and I keep getting the same error message. The bolded part is the part that doesn't work.

I have been trying to simulate from a time series with trend but I don't see how to include the trend in the arima.sim() call. The following code illustrates the problem: # Begin demonstration program x <- c(0.168766559, 0.186874000, 0.156710548, 0.151809531, 0.144638812, 0.142106888, 0.140961714, 0.134054659, 0.138722419, 0.134037018, 0.122829846, 0.120188714,