uam

uam combines aggregated attribution with customer journey information. It uses D_variables to map each raw input signal to a final channel and measure, and optional D_measures aggregation weights to control measure-level contribution. If journey paths are provided, the aggregated baseline is adjusted with path-based Markov information; otherwise, UAM returns the aggregated baseline only.

• D_variables maps each raw input variable to a final channel and measure.
• D_measures is optional and can define global aggregation_weight values by measure. If D_measures is not provided, the function uses automatic measure handling.

PARAMETER	TYPE	DEFAULT	DESCRIPTION
df_aggr	data.frame / table	required	Aggregated dataset containing a timestamp column, the target column, and the marketing or business signal columns used for attribution.
D_variables	data.frame / table	required	Mapping table with one row per input signal. It must contain variable, channel, and measure. The target column must not be included.
D_measures	data.frame / table / "auto"	"auto"	Optional measure-level weighting table. The recommended user-facing structure contains columns measure and aggregation_weight. If omitted or set to "auto", measure handling is estimated automatically.
target	string	required	Name of the target column in df_aggr. The target must exist in df_aggr and must not be included in D_variables.
df_paths	data.frame / table	optional	Customer journey data. It can contain serialized paths or event-level journeys, depending on the columns supplied.
baseline_model	string	"linear"	Aggregated attribution engine used as UAM baseline. Allowed values are "linear", "reward", and "copula".
var_path	string	"path"	Column containing serialized journeys when aggregated path data is provided.
var_conv	string	"total_conversions"	Column containing the number of conversions associated with each path.
var_null	string	"total_null"	Column containing null-path counts when available.
channel_conv	string	"((CONV))"	Conversion marker used when df_paths is provided as event-level journey data.
max_p	integer	12	Maximum lag depth considered when evaluating the relationship between input signals and the target.
nsim	integer	1000	Simulation parameter used by model components that require simulation, kept for API consistency.
seed	integer	1234567	Random seed used for reproducible results where random or simulation-based components are involved.
return_diagnostics	bool	False	If True, returns additional diagnostic tables useful to inspect signal weights, scoring, saturation, and attribution shares.
verbose	integer / bool	1	Controls runtime logging. Use 0 or False to reduce printed output.
ncore	integer	1	Number of cores used by supported computation steps.
password	string	optional	ChannelAttributionPro license token.

Example structure for D_variables:

variable	channel	measure
google_clicks	google_ads	clicks
google_impressions	google_ads	impressions
facebook_clicks	facebook_ads	clicks
facebook_impressions	facebook_ads	impressions
direct_searches	direct	direct_searches

Example structure for D_measures:

measure	aggregation_weight
direct_searches	0.45
clicks	0.45
impressions	0.10

aggregation_weight is used when variable-level attributions sharing the same channel are aggregated into final channel-level attribution. The internal signal scaling is estimated automatically from the data.