Prepares a dataset for ForestSearch, including options for LASSO-based dimension reduction, GRF cuts, forced cuts, and flexible cut strategies. Returns a list with the processed data, subgroup factor names, cut expressions, and LASSO selection results.
Usage
get_FSdata(
df.analysis,
use_lasso = FALSE,
use_grf = FALSE,
grf_cuts = NULL,
confounders.name,
cont.cutoff = 4,
conf_force = NULL,
conf.cont_medians = NULL,
conf.cont_medians_force = NULL,
replace_med_grf = TRUE,
defaultcut_names = NULL,
cut_type = "default",
exclude_cuts = NULL,
outcome.name = "tte",
event.name = "event",
details = TRUE
)Arguments
- df.analysis
Data frame containing the data.
- use_lasso
Logical. Whether to use LASSO for dimension reduction.
- use_grf
Logical. Whether to use GRF cuts.
- grf_cuts
Character vector of GRF cut expressions.
- confounders.name
Character vector of confounder variable names.
- cont.cutoff
Integer. Cutoff for continuous variable determination.
- conf_force
Character vector of forced cut expressions.
- conf.cont_medians
Character vector of continuous confounders to cut at median.
- conf.cont_medians_force
Character vector of additional continuous confounders to force median cut.
- replace_med_grf
Logical. If TRUE, removes median cuts that overlap with GRF cuts.
- defaultcut_names
Character vector of confounders to force default cuts.
- cut_type
Character. "default" or "median" for cut strategy.
- exclude_cuts
Character vector of cut expressions to exclude.
- outcome.name
Character. Name of outcome variable.
- event.name
Character. Name of event indicator variable.
- details
Logical. If TRUE, prints details during execution.