similar to: R and Hierarchical Forecasting

Okay, been working with tripack, seems the most mature package for this. Got it to work well with their test data set - data(tritest). When i tried random numbers to explore further, i am getting some results that don't reconcile. example run this: library(tripack) y <- runif(100) x <- runif(100) vm <- voronoi.mosaic(x,y) plot(vm) par(new=T) plot(x,y,col='blue') when

Dear R users, i am wondering if anyone has some hints for this problem (i have not found a clear answer after searching the R-mailing list archive, 'help.search' in R, and R-Wiki, and the like...): let's assume that i have 4 periods compositional time series data: t=1, A=0.1; B=0.5; C=0.4 t=2, A=0.2; B=0.4; C=0.4 t=3, A=0.5; B=0.3; C=0.2 t=4, A=0.4; B=0.3;

Hello, trying to locate all the string commands in the base version of R, can't seem to find an area that describes them. I am in need to do some serious parsing of text data to create my dataset. Is there a summary link to all the character operators? string manipulations that would help in parsing text.

the R developer site has been down for some time, what gives? http://developer.r-project.org/

Hello, we are needing to generate optimal (Fractional) designs for discrete choice applications, where we will be using logistic regression or multinomial logit as the modeling technique. It looks like optFederov, in the AlgDesign package may work, but not sure if this algorithm works when the variable of interest is binary or nominal? Anyone who are experts in this area, anyone interested

Hello, does R estimate belief networks to estimate chains of causality. Anyone point me to the right direction, or the most developed library? looking at DEAL>

Hello, writing some R code to cleanse a data set, if the following set of symbols are identified then perform some actions. trying to write the minimum code to do this. tname = "VIX" checkticker = c("VIX", "TYX", "TNX", "IRX") if (tname == checkticker) { //perform some operations } result i get is > tname == checkticker

Hello, not understanding the output of prcomp, I reduce the number of components and the output continues to show cumulative 100% of the variance explained, which can't be the case dropping from 8 components to 3. How do i get the output in terms of the cumulative % of the total variance, so when i go from total solution of 8 (8 variables in the data set), to a reduced number of

sample report data that i want to forecast quarter quarter_index Revenue 2007 Q1 1 $3,856,799 2007 Q2 2 $4,243,328 2007 Q3 3 $4,930,369 2007 Q4 4 $5,443,579 2008 Q1 5 $5,164,830 2008 Q2 6 $5,104,413 2008 Q3 7

Hi, Although my doubt is pretty,as i m not from stats background i am not sure how to proceed on this. Currently i am doing a forecasting.I used ARIMA to forecast and time series was volatile i used garchFit for residuals. How to use the output of Garch to correct the forecasted values from ARIMA. Here is my code: ###delta is the data fit<-arima(delta,order=c(2,,0,1)) fit.res <-

Hello, we are writing rich internet user interfaces and like to call R for some of the computational needs on the data, as well as some creation of image files. Our objects communicate via the SOAP interface. We have been researching the various packages to expose R as a SOAP service. No current CRAN SOAP packages however. Found 3 to date: RSOAP (http://sourceforge.net/projects/rsoap/)

hello, i just got introduced to R - WOW its beautiful.. I am presently a SAS user and wanted to configure R to work in a multi-user enteprise environment. Client - Server. Where we have a strong LINUX server supporting about 10 statisticians with R. Anyone have any backround or information they can share to help me get jump-started on setting up R in this environment? Does each user have

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Hello, In order to do a future forecast based on my past Time Series data sets (salespricesproduct1, salespricesproduct2, etc..), I used arima() functions with different parameter combinations which give the smallest AIC. I also used auto.arima() which finds the parameters with the smallest AICs. But unfortuanetly I could not get satisfactory forecast() results, even sometimes catastrophic

hi all. i'm busy with some time series data, starting from an earlier period until the current day. i have created a time series forecast taking into account the entire data from the earlier date up until 2007, using the "forecast" package for R. i am comparing this forecasted data to the actual/ observed data (which starts from the earlier date up until the current day). my

Couldn't find this in the archives. I'm fitting a series of historical weather-related data, but would like to use the latest values to forecast. So let's say that I'm using 1970-2000 to fit a model (using fourier terms and arima/auto.arima), but now would like to use the last X values to predict tomorrow's weather. I'm at a loss. All the functions I've come across

Hello everybody! I have a problem when I try to perform a forecast of an ARIMA model produced by an auto.arima function. Here is what I'm doing: c<-auto.arima(fil[[1]],start.p=0,start.q=0,start.P=0,start.Q=0,stepwise=TRUE,stationary=FALSE,trace=TRUE) # fil[[1]] is time series of monthly data ARIMA(0,0,0)(0,1,0)[12] with drift : 1725.272 ARIMA(0,0,0)(0,1,0)[12] with drift

Hello, running a mixed model in the package LME4, lmer() Panel data, have about 322 time periods and 50 states, total data set is approx 15K records and about 20 explanatory variables. Not a very large data set. We run random intercepts as well as random coefficients for about 10 of the variables, the rest come in as fixed effects. We are running into a wall of time to execute these models.

Hello, I have estimated a simultaneous equation model (similar to Klein's model) in R using the system.fit package. I have an identity equation, along with three other equations. Do you know how to explicitly identify the identity equation in R? I am also trying to forecast the dependent variables in the simultaneous equation model, while incorporating the identity equation in the

Dear R Users, I am new to state-space modeling. I am using SSPIR package for Kalman Filter. I have a data set containing one dependent variable and 7 independent variables with 250 data points. I want to use Kalman Filter for forecast the future values of the dependent variable using a multiple regression framework. I have used ssm function to produce the state space (SS)