similar to: Forecast Package in R: auto.arima function

Hey, I have a data frame x which consists of say 10 vectors. I essentially want to find out the best fit exponential smoothing for each of the vectors. The problem while I'm getting results when i say > lapply(x,ets) I am getting an error when I say >> myprint function(x) { for(i in 1:length(x)) { ets(x[i],model="AZZ",opt.crit=c("amse")) } } The error message is

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 -

Hi, I've got a little problem using auto.arima. I run the following command auto.arima(drivers,ic="aic",d=1,D=1,max.order=10,max.p=5,max.q=5,max.P=5,max.Q=5,stepwise=FALSE,allowdrift=FALSE) and I get the following output : Series: drivers ARIMA(0,1,1)(5,1,1)[12] Coefficients: ma1 sar1 sar2 sar3 sar4 sar5 sma1 -0.6421

I've downloaded R for windows (9.0.1) and it is great! I've converted all my lecture notes for my GLM course to run on R (they are available on my web page below). I must admit I particularly like the default contrast options, which are identical to GLIM. Also I like the gl function - very useful! I have a couple of questions/bugs: 1. predict.glm doesn't work, but predict.lm does -

Hey, I have a list of 30 odd time-series (products) in columns of a data-frame. I want to apply time-series forecasting functions across all the columns of the data-frame in order to determine which is the best model to use. How do I go about this? Phani [[alternative HTML version deleted]]

Hola Jorge: Disculpa la tardanza pero me han tenido liado en otros menesteres. Yo no soy ni muchísimo menos un experto ni en series temporales ni en R de hecho retomo el tema después de muchos (demasiados) años aparcado y dedicándome a labores de programación pura y dura. Respecto a la diferencia de resultados con R y Statgraphics, no conozco el proceso de selección del modelo ARIMA que hace

Having trouble with datadist in the Design library. When I set the datadist option, all of the fitting functions give error messages similar to "Error in exists(x, envir, mode, inherits) : invalid first argument". Code below was run in R version 2.5.0 under windows, but get similar results with R version 6.2, except with additional problem that it will not fit model with

Is there any package for Vector Auto-regressive with exogenous variable other than fastVAR? Because it is not able to solve my problem of not taking the base in the model. Please suggest some appropriate solution!!!! -- View this message in context: http://r.789695.n4.nabble.com/Is-there-any-package-for-Vector-Auto-regressive-with-exogenous-variable-other-than-fastVAR-tp4644964.html Sent from

Dear members, I'm fitting linear model using "lm" which has numerous auto-regressive terms as well as other explanatory variables. In order to calculate prediction intervals, i've used a for-loop as the auto-regressive parameters need to be updated each time so that a new forecast and corresponding prediction interval can be calculated. I'm fitting a number of these models

Dear R helpers, I would like to ask why polr occasionally generates results that look very odd. I have been trying to compare the power of proportional odds logistic regression with the Wilcoxon test. I generated random samples, applied both tests and extracted and compared the p-values, thus:- library(MASS) c1=rep(NA,100); c2=c1 for (run in 1:100) { dat=c(rbinom(20,12,0.65),rbinom(20,12,0.35))

Dear all, I need to fit a gee model with an auto-regressive correlation structure and I faced some problems. I attach a simple example: ####################################################### library(gee) library(geepack) # I SIMULATE DATA FROM POISSON DISTRIBUTION, 10 OBS FOR EACH OF 50 GROUPS set.seed(1) y <- rpois(500,50) x <- rnorm(500) id <- rep(1:50,each=10) # EXAMPLES FOR

Dear community, I'd like to build a PLS plot with scores and loadings, sometimes called "biplot". Like in biplot.mvr {pls} but using ggplot2. 1. Scores plot. No problem! ggplot(data=data1,aes(x=plsr1,y=plsr2))+geom_point(aes(colour=solenergy,shape=type))+geom_text(aes(label=res,size=1,hjust=0,vjust=0)) where, > str(data1) 'data.frame': 295 obs. of 5 variables: $

Hi, Could someone please tell me the R codes for fitting VAR(p) (Vector Auto Regressive) models and doing the Johansen?s cointegration tests. TIA Aditya

I have made program code for Vector Auto Regressive in terms of completing my undergraduate program using R. I have an important question related to my project. If I have: data(Canada) var.2c <- VAR(Canada, p = 2, type = "const") var.2c.stabil <- stability(var.2c, type = "OLS-CUSUM") I want to get the value of plot(var.2c.stabil). Can you help

Hello I am facing a curious problem.I have a time series data with which i want to fit auto-regressive model of order p, where p runs from 1:9.I am using a for loop which will fit an AR(p) model for each value of p using the *ar.ols* function. I am using the following code for ( p in 1:9){ a=ar.ols (x=data.ts, order.max=p, demean=T, intercept=T) } Specifying the *order.max* to be p, it gives me a

Hi, I am brand new to R and not familiar with the language, though I have been reading the manuals and making some slow going progress. I am working with some source code from a Global Vector Auto -Regressive program written by Ranier Puhr from the R-forge group. I need help interpreting the processes of the following code. I am going to post in parts since it's pretty long: GVAR

Hello, When considering linear function where dependent variable is a function of lags of dependent variables ( co-variates are kind of auto regressive).Question is .....Can i take entire data set for rpart . Then split data into training and testing and validate the model for testing data.In testing data previously predicted value will be the lag of dependent variable for next prediction.

Hi R-Community, I estimated a VARMA model with bft in dse1 without input: A(L)yt = B(L)et I got the auto-regressive polynomial array A and the moving-average polynomial array B, but how can I access the covariances of the white noise et (disturbance vector), e.g. for simulation? Much thanks in advance, Hagen Schmoeller -- Dipl.-Ing. Hagen K. Schm??ller Leiter Forschungsgruppe Stromerzeugung

Hello, I have two gamm question (I am using gamm in mgcv). 1. In have, say 5 time series. Monthly data, 20 year. The 5 time series are from 5 stations. The data are in vectors, so I have fitted something along the lines of: tmp<-gamm(Y ~ s(Year,by=station1)+s(Year,by=station2)+ s(Year,by=station3)+s(Year,by=station4)+

Dear all: I want to formulate couple of questions about SILK codec: 1. I've seen the documentation to the SILK codec that is part of the OPUS exactly the paper with title "The opus codec voice coding with opus, and the "RFC 6716 specification of opus" . Well, I detected that in both is defined a High-Pass filter as part of SILK; however in the paper you put the following