In anthonydevaux/DynForest: Random Forest with Multivariate Longitudinal Predictors

knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
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Description

DynForest is a R package aiming to predict an outcome using multivariate longitudinal predictors. The method is based on random forest principle where the longitudinal predictors are modeled through the random forest. DynForest currently supports continuous, categorical and survival outcome. The methodology is fully described for a survival outcome in the paper:

Devaux A., Helmer C., Genuer R. and Proust-Lima C. (2023). Random survival forests with multivariate longitudinal endogenous covariates. Statistical Methods in Medical Research.

DynForest user guide is also available in the paper:

Devaux A., Proust-Lima C. and Genuer R. (2023). Random Forests for time-fixed and time-dependent predictors: The DynForest R package. arXiv.

Installation

DynForest package version 1.2.0 could be install from the CRAN with:

install.packages("DynForest")

Development version of DynForest is also available from GitHub with:

# install.packages("devtools")
devtools::install_github("anthonydevaux/DynForest")

Quick example with survival outcome

Manage data

library(DynForest)
data(pbc2)

# Get Gaussian distribution for longitudinal predictors
pbc2$serBilir <- log(pbc2$serBilir)
pbc2$SGOT <- log(pbc2$SGOT)
pbc2$albumin <- log(pbc2$albumin)
pbc2$alkaline <- log(pbc2$alkaline)

Build DynForest objects

# Build longitudinal data
timeData <- pbc2[,c("id","time",
                    "serBilir","SGOT",
                    "albumin","alkaline")]

# Create object with longitudinal association for each predictor
timeVarModel <- list(serBilir = list(fixed = serBilir ~ time,
                                     random = ~ time),
                     SGOT = list(fixed = SGOT ~ time + I(time^2),
                                 random = ~ time + I(time^2)),
                     albumin = list(fixed = albumin ~ time,
                                    random = ~ time),
                     alkaline = list(fixed = alkaline ~ time,
                                     random = ~ time))
# Build fixed data
fixedData <- unique(pbc2[,c("id","age","drug","sex")])

# Build outcome data
Y <- list(type = "surv",
          Y = unique(pbc2[,c("id","years","event")]))

Run `dynforest()` function

# Run DynForest function
res_dyn <- dynforest(timeData = timeData, fixedData = fixedData,
                     timeVar = "time", idVar = "id",
                     timeVarModel = timeVarModel, Y = Y,
                     ntree = 50, nodesize = 5, minsplit = 5,
                     cause = 2, ncores = 15, seed = 1234)