estimate crossfit lasso-based weights

Usage

crossfit_lasso_weights(
  internal_data,
  external_data,
  select_vars = c("id", "internal", "age_50", "female", "nhw", "hypertension",
    "diabetes", "cancer", "anxiety", "depression", "bmi_obese", "bmi_overweight",
    "bmi_underweight", "bmi_unknown"),
  weight_outcome_var = "WTFA_A",
  samp_var = "samp_WTFA_A",
  external_dataset = "NHIS",
  dataset_name = "MGI",
  id_var = "id",
  internal_var = "internal",
  cancer_factor = FALSE,
  ncores = parallelly::availableCores()/4,
  folds = 5
)

Arguments

internal_data: data.table of internal data
external_data: data.table of external data
select_vars: vector of variables to select
weight_outcome_var: variable name for weight outcome
samp_var: variable name for sampling indicator
external_dataset: name of external dataset
dataset_name: name of internal dataset
id_var: name of id variable
internal_var: name of internal dataset indicator variable
cancer_factor: whether to use a cancer factor
ncores: number of cores to use
folds: number of folds to use

Value

return table of results from model for the exposures