README

This R package is used to train random competing risks forests, ideally for large data. It's based heavily off of randomForestSRC, although there are some differences which are described in (TODO - link to paper here).

This package is still in a pre-release state and so it not yet available on CRAN. To install it now, in R install the devtools package and run the following command:

R> devtools::install_git("https://git.joeltherrien.ca/joel/largeRCRF.git")

System Requirements

You need:

• R version 3.4.2 or greater
• The rJava package version 0.9-9 or greater
• A Java runtime version 1.8 or greater

Troubleshooting

I get an OutOfMemoryException error but I have plenty of RAM

largeRCRF makes use of the Java virtual machine, which unfortunately restricts itself by default to a quarter of your system memory. You can override the default by including before loading largeRCRF or any other rJava based package the following line:

R> options(java.parameters <- c("-Xmx13G", "-Xms13G"))

with 13G replaced with a little less than your available system memory.

I get an OutOfMemoryException error and I'm short on RAM

Obviously if you're short on RAM there is a limit on how large of a dataset you can train, but there are some techniques you can use to limit how much largeRCRF needs.

• If your training dataset is large you might not want both R and largeRCRF to have their own separate copies (limitations due to Java require largeRCRF have its own copy). When specifying the data parameter into train, instead provide an environment containing one object called data which is the dataset. largeRCRF will delete that variable after importing it into the Java environment.

Example:

R> data.env <- new.env()
R> data.env$data <- trainingData
R> rm(trainingData)
R> model <- train(..., data=data.env, ...)

• Each core that is training trees requires its own memory; you can try limiting largeRCRF to train only one tree at a time by specifiying cores=1.

• By default largeRCRF keeps the entire forest loaded in memory during training, when in practice only the trees being trained on need to be loaded. You can specify savePath to give a directory for largeRCRF to save trees in during training, which will allow to largeRCRF to conserve memory for only those trees being currently trained.