reward_model

reward_model performs attribution from aggregated signals using a movement/coherence-based reward baseline. It uses the same D_variables and D_measures structure as auto_mmm.

At a high level, auto_mmm estimates predictive contribution with the automatic MMM engine, while reward_model estimates contribution from movement coherence between signals and the target.

Input structure

Data should contain:

• a timestamp column
• one target column mapped to "target" in D_variables
• one or more signal columns mapped to their final channel names

D_variables maps each column in Data to either the target or a channel name.

D_measures maps each non-target input column to its measure type.

Parameters

PARAMETER	TYPE	DEFAULT	DESCRIPTION
Data	data.frame		Aggregated dataset with timestamp, target, and input signal columns.
D_variables	dictionary / list		Mapping from each input column to "target" or to its final channel name.
D_measures	dictionary / list		Mapping from each non-target input column to its measure type.
max_p	int	12	Maximum lag length considered when the model evaluates delayed effects.
seed	int	1234567	Random seed for reproducible results.
verbose	int / bool	1	Controls runtime logging.
server	str / list[str]	app.channelattribution.io	Server endpoint used by the hosted computation service.
password	str	NULL / None	Authentication token for the hosted service.
return_diagnostics	bool	FALSE / False	If enabled, returns attribution together with diagnostic objects for advanced inspection.

Output

If return_diagnostics = FALSE, the function returns an attribution data frame.

If return_diagnostics = TRUE, the function returns a list/dict containing:

OUTPUT	TYPE	DESCRIPTION
attribution	data.frame	Channel attribution by time period.
base_raw	data.frame / object	Raw baseline attribution output before final adjustments.
score_multiplier	data.frame / object	Score multiplier information used during attribution assembly.
applied_score_multiplier	data.frame / object	Effective multiplier applied to the returned attribution.
mode	str	Execution mode metadata.
diagnostics	list / dict	Additional diagnostics intended for advanced inspection.