similar to: Forecasting using ARIMAX

Hola! I got problems using an objects returned from arima (in KalmanSmooth(my.ts, ModArima$model), because my.ts showed up to have storage mode "integer" (is.integer(my.ts was TRUE). Should storage.mode() of a ts be allowed to be integer, should ts() someplace say storage.mode(ts.out) <- "double", or maybe inside arima() storage.mode(x) <- "double"

I have a dataset of 10000 records which I want to use to compare two prediction models. I split the records into test dataset (size = ntest) and training dataset (size = ntrain). Then I run the two models. Now I want to shuffle the data and rerun the models. I want many shuffles. I know that the following command sample ((1:10000), ntrain) can pick ntrain numbers from 1 to 10000. Then I just

Dear All, I am far from being a guru about parallel programming. Most of the time, I rely or randomForest for data mining large datasets. I would like to give a try also to the gradient boosted methods in GBM, but I have a need for parallelization. I normally rely on gbm.fit for speed reasons, and I usually call it this way gbm_model <- gbm.fit(trainRF,prices_train, offset = NULL, misc =

Hi R User, I was trying to find a final model in the following example by using the Boosted regression trees (GBM). The program gives the fitted values but I wanted to calculate the fitted value by hand to understand in depth. Would you give moe some hints on what is the final model for this example? Thanks KG ------- The following script I used #----------------------- library(dismo)

> From: luk > > I got the following results when I run radomForest with below > commands: > > qair <- read.table("train10.dat", header = T) > oz.rf <- randomForest(LESION ~ ., data = qair, ntree = 220, > importance = TRUE) > print(oz.rf) > > Call: > randomForest.formula(x = LESION ~ ., data = qair, ntree = > 220, importance =

I am trying to call a C++ k-means function within R and I am struggling. I know that the below code is used to call a C++ function for gbm but how do I do it for k-means? gbm.obj <- .Call("gbm", Y=as.double(y), Offset=as.double(offset), X=as.double(x), X.order=as.integer(x.order),

Dear R-helpers. Would appreciate if someone can explain the warning messages below, after auto.arima. I couldn't find any clue in the archived help. Also, how do I retrieve the AICs of each tried model in auto.arima? The purposes are (1) to output to a text file, and (2) to find the 2nd best model by finding 2nd lowest AIC instead of eyeballing thru the value at the console