Generate Prediction Dataset with Subgroup Treatment Recommendation

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.

Examples

if (FALSE) { # \dontrun{
# With brace-wrapped label expressions
sg <- c("{er <= 0}", "{size <= 35}")
df_out <- get_dfpred(df.predict = test_data, sg.harm = sg)

# With negation
sg_neg <- c("{er <= 0}", "!{size <= 35}")
df_neg <- get_dfpred(df.predict = test_data, sg.harm = sg_neg)

# With bare column names (binary indicators)
sg_col <- c("q1.1", "q3.1")
df_col <- get_dfpred(df.predict = encoded_data, sg.harm = sg_col)
} # }