similar to: n step ahead forecasts

For the following code: Type * type = IntegerType::getInt32Ty(getGlobalContext()); IRBuilder<> builder(BB); std::set<Value *> Vset; Value * Vresult=0; for(std::set<Value*>::iterator Vit=Vset.begin();Vit!=Vset.end();Vit++) { Vresult=builder.CreateOr(Vit, Vresult, "WaitOr"); } Vset is inserted in previous loop by 0 or 1 The error

Hello, I try to write a bash skript and I want to use the variables from my bash skript into R. Ist that possible? My bash skript creates lots of *.data files. I want forward these files directly into R (in x.data.bz2), so that R creates a few data automatically also in PDF. For example: bash created files: hello.data , world.data how R created these files in pdf? please look my plot.R

dear members, I have a heteroscedastic time series which I want to transform to make it homoscedastic by a box cox transformation. I am using Otexts by RJ hyndman and George Athanopolous as my textbook. They discuss transformation and also say the fpp3 and the fable package automatically back transforms the point forecast. they also discuss the process which I find to be

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

Hello dear R Users, I am working on a dataset of 928 Enterprises, of which are observed 12 different characters. I need to randomly sample, without repetition, 70% of the entreprises, to create a testing set, and let the other 30% of the enterprises be a validating set (holdout validation, I think that is). How do I do that? Of course all the characters of each row must remain together. Also, I

Dear Random Forests gurus, I have a question about R^2 provided by randomForest (for regression). I don't succeed in finding this information. In the help file for randomForest under "Value" it says: rsq: (regression only) - "pseudo R-squared'': 1 - mse / Var(y). Could someone please explain in somewhat more detail how exactly R^2 is calculated? Is "mse"

If I understand correctly, rpart() will pick predictor at each node automatically. I am wondering if there is a way to force rpart() including a specific predictor. The reason I am asking is that I'd like to use rpart() to detect interaction terms for some variables. Thanks.

Max and List, Could you advise me if I am using the proper caret syntax to carry out leave-one-out cross validation. In the example below, I use example data from the rda package. I use caret to tune over a grid and select an optimal value. I think I am then using the optimal selection for prediction. So there are two rounds of resampling with the first one taken care of by caret's train

Hi. I'm embarking on my first attempt at creating my own panel function for lattice graphics, and despite all of my online research and pouring through the documentation, I cannot figure out how to solve my particular problem. Hopefully, a generous fellow R user can help. I have some data that is split into two groups: some "actual" data, and some simulated data,

All: How do I obtain one step ahead out-of-sample forecasts from a model using "dyn" or "dynlm" ? Thanks! Best, John [[alternative HTML version deleted]]

hello, I am trying to use predict.lm to make point forecasts based on a model with continuous and categorical independent variables I have no problems fitting the model using lm, but when I try to use predict to make point predictions. it reverts back to the original dataframe and gives me the point predictions for the fitted data rather than for the new data, I imagine that I am missing

Buenos días, Os cuento: Cargo la librería "Forecast" y ejecuto su función Arima(...) sobre una serie temporal: mimodelo <- Arima(miST$miserie, ...); Ahora si ejecuto las siguientes sentencias, voy obteniendo los resultados contenidos en "mimodelo", pero algunos de ellos no sé lo que son: mimodelo[[1]] obtengo los coeficientes del modelo ARIMA mimodelo[[2]] obtengo el

Dear users, I am struggling with this issue. I want to estimate a VAR(1) for three variables, say beta1 beta2 beta3, using monthly observations from January 1984 to September 2009. In-sample period January 1984 to December 2003, out-of-sample January 2004 to September 2009. This is what I have done at the moment

Dear useRs: It's been some time since we last sent out an announcement, so this one will cover more than just the last update. The latest release of randomForestSRC is now available on CRAN at: https://CRAN.R-project.org/package=randomForestSRC The GitHub repository, through which we prefer to receive bug reports, is at: https://github.com/kogalur/randomForestSRC If you do find issues,

Dear useRs: It's been some time since we last sent out an announcement, so this one will cover more than just the last update. The latest release of randomForestSRC is now available on CRAN at: https://CRAN.R-project.org/package=randomForestSRC The GitHub repository, through which we prefer to receive bug reports, is at: https://github.com/kogalur/randomForestSRC If you do find issues,

can you recommend a good manual for R that starts with a data set and gives demonstrations on what can be done using R? I downloadedR Langauage definition and An introduction to R but haven't found them overly useful. I'd really like to be able to follow some tutorials using a dataset or many datasets. The datasets I have available on R are Data sets in package 'datasets':

Got a client that usually uses Outlook I think 2010. This person tends to move their e-mails to certain folers. On Thunderbird, the move shows. Not on Outlook. Any explanation? -- Member - Liberal International This is doctor@@nl2k.ab.ca Ici doctor@@nl2k.ab.ca Yahweh, Queen & country!Never Satan President Republic!Beware AntiChrist rising! https://www.empire.kred/ROOTNK?t=94a1f39b A

I'm trying to classify some observations using lda and I'm getting a strange error. I loaded the MASS package and created a model like so: >train <- mod1[mod1$rand < 1.7,] >classify <- mod1[mod1$rand >= 1.7,] >lda_res <- lda(over_win ~ t1_scrd_a + t1_alwd_a, data=train, CV=TRUE) That works, and all is well until I try to do a prediction for the holdouts:

I'm using the arima function to estimate coefficients and also using predict.Arima to forecast. This works nicely and I can see that the results are the same as using SAS's proc arima. I can also take the coefficent estimates for a simple model like ARIMA(2,1,0) and manually compute the forecast. The results agree to 5 or 6 decimal places. I can do this for models with and without

This post is related to the following post http://lists.llvm.org/pipermail/llvm-dev/2016-April/098823.html I'm still trying to compile a library with clang. But now I'm getting as assert in lib/CodeGen/TargetLoweringBase.cpp:1155: virtual llvm::EVT llvm::TargetLoweringBase::getSetCCResultType(llvm::LLVMContext&, llvm::EVT) const: Assertion `!VT.isVector() && "No default