mmcif_start_values function

Finds Staring Values

Fast heuristic for finding starting values for the mixed cumulative incidence functions model.

mmcif_start_values(object, n_threads = 1L, vcov_start = NULL)

Arguments

• object: an object from mmcif_data.
• n_threads: the number of threads to use.
• vcov_start: starting value for the covariance matrix of the random effects. NULL yields the identity matrix.

Returns

A list with

• an element called "full" with the starting value where the last components are the covariance matrix.
• an element called "upper" the staring values where the covariance matrix is stored as a log Cholesky decomposition. This is used e.g. for optimization with mmcif_fit.

Examples

if(require(mets)){
  # prepare the data
  data(prt)

  # truncate the time
  max_time <- 90
  prt <- within(prt, {
    status[time >= max_time] <- 0
    time <- pmin(time, max_time)
  })

  # select the DZ twins and re-code the status
  prt_use <- subset(prt, zyg == "DZ") |>
    transform(status = ifelse(status == 0, 3L, status))

  # randomly sub-sample
  set.seed(1)
  prt_use <- subset(
    prt_use, id %in% sample(unique(id), length(unique(id)) %/% 10L))

  n_threads <- 2L
  mmcif_obj <- mmcif_data(
    ~ country - 1, prt_use, status, time, id, max_time,
    2L, strata = country)

  # get the staring values
  start_vals <- mmcif_start_values(mmcif_obj, n_threads = n_threads)

  # the starting values
  print(start_vals)
}