Initialize starting values for each initial partition of the EM algorithm call

Usage

initialize_starting_values(
  n_inits,
  k,
  starting_values_type,
  starting_values_df = NULL,
  starting_values_window,
  input_df,
  outc_model_formula,
  weights_input,
  outc_distribution
)

Arguments

n_inits: Number of initial partitions
k: Number of subgroups
starting_values_type: One of "single_survreg", "uniform_pct", or "non_random_start". "single_survreg" fits a single AFT model to all of the data and then generates random starting values based on the `starting_values_window` parameter for each initial partition. If not supplying starting values, they are randomly generated for each initial partition. Be sure to set a seed at the top of your script to ensure reproducibility.
starting_values_df: Optional input dataset with starting values for algorithm
starting_values_window: The percent margin around the starting values. For example, starting_values_type = 'single_survreg' and starting_values_window = 0.5 means that starting values are randomly generated uniformly +/- 50 fit. For starting_values_type = 'uniform' and starting_values_df is supplied, the starting values are generated uniformly
input_df: Input data frame containing 1 row/observation, along with each observation's event status (0=censored, 1=event) and event time variables
outc_model_formula: Formula object for subgroup-specific outcome models
weights_input: Variable for inverse probability of treatment weights, if applicable. For IPCW-FMM, supply the IPCW. For IPCW-FMM that also uses IPTW, the product of the ITPW and IPCW may be supplied.
outc_distribution: Outcome distribution for subgroup-specific outcome models. Currently allowed values are "Weibull" and "Log-Normal" (not case-sensitive)

Value

List of starting values, with 1 list element per set of starting values