The R package DynForest implements random forests for predicting a categorical or a (multiple causes) time-to-event outcome based on time-fixed and time-dependent predictors. Through the random forests, the time-dependent predictors can be measured with error at subject-specific times, and they can be endogeneous (i.e., impacted by the outcome process). They are modeled internally using flexible linear mixed models (thanks to lcmm package) with time-associations pre-specified by the user. DynForest computes dynamic predictions that take into account all the information from time-fixed and time-dependent predictors. DynForest also provides information about the most predictive variables using variable importance and minimal depth. Variable importance can also be computed on groups of variables. To display the results, several functions are available such as summary and plot functions. This paper aims to guide the user with a step-by-step example of the different functions for fitting random forests within DynForest.
翻译:R 包 DynForest 执行随机森林, 用于根据时间固定和时间依赖预测器预测绝对值或(多重原因)时间到活动结果。 通过随机森林, 取决于时间的预测器可以在特定主题的时间里用错误来测量, 它们可以是内源的( 受结果过程影响 ) 。 它们使用灵活的线性混合模型( 感谢 lcmm 软件包) 进行内部建模, 由用户预先指定时间联系 。 DynForest 计算动态预测, 其中考虑到时间固定和时间依赖预测器提供的所有信息。 DynForest 还利用不同重要性和最小深度提供关于最可预测变量的信息。 变量群也可以计算变量群的变量重要性 。 要显示结果, 有几个功能可以使用, 如摘要和绘图功能 。 本文旨在用不同功能的逐步示例来指导用户在 DynForest 中适合随机森林。