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Generate Prediction Dataset with Subgroup Treatment Recommendation

Source: R/forestsearch_helpers.R
get_dfpred.Rd

Creates a prediction dataset with a treatment recommendation flag based on the subgroup definition. Supports both label expressions (e.g., "\{er <= 0\}") and bare column names (e.g., "q3.1").

Usage

get_dfpred(df.predict, sg.harm, version = 1)

Arguments

df.predict

Data frame for prediction (test or validation set).

sg.harm

Character vector of subgroup-defining labels. Values may be wrapped in braces and optionally negated, e.g. "\{er <= 0\}" or "!\{size <= 35\}". Plain column names (e.g., "q3.1") are treated as binary indicators that must equal 1.

version

Integer; encoding version (maintained for backward compatibility). Default: 1.

Value

Data frame with treatment recommendation flag (treat.recommend): 0 for harm subgroup, 1 for complement.

Details

Each element of sg.harm is processed as follows:

  1. Outer braces and leading ! are stripped.

  2. If the result matches "var op value" (where op is one of <=, <, >=, >, ==, !=), the comparison is executed directly on df.predict[[var]].

  3. Otherwise the expression is treated as a column name and membership is df.predict[[name]] == 1.

See also

evaluate_comparison for the operator-dispatch logic, forestsearch for the main analysis function.