Fits the specified longitudinal model for time-varying covariates up to the landmark times

Usage

# S4 method for class 'LandmarkAnalysis'
fit_longitudinal(
  x,
  landmarks,
  method,
  formula,
  dynamic_covariates,
  validation_fold = 0,
  cores = getOption("Ncpus", 1L),
  ...
)

Arguments

x: An object of class LandmarkAnalysis.
landmarks: A vector of Landmark times.
method: Either "lcmm" or "lme4" or a function for fitting a longitudinal data model, where the first argument is a formula, and also has a data argument.
formula: A formula to be used in longitudinal sub-model fitting.
dynamic_covariates: Vector of time-varying covariates to be modelled as the outcome of a longitudinal model.
validation_fold: If positive, cross-validation fold where model is fitted. If 0 (default), model fitting is performed using the complete dataset.
cores: Number of cores/threads to be used for parallel computation on Linux and MacOS. Defaults to either options("Ncpus") if set, or 1 (single threaded) otherwise. Only single-threaded computation is currently supported on Windows.
...: Additional arguments passed to the longitudinal model fitting function (e.g. number of classes/clusters for lcmm).

Value

An object of class LandmarkAnalysis.

Details

Parallel processing

As the longitudinal model for each landmark time is independent of the longitudinal models for other landmark times, parallel processing can be used to vastly speed up computation. However, due to issues with parallel processing in R, currently only Unix-like operating systems are supported by landmaRk.