DefaultTraditionalMediaModule: Model the effect of a traditional media channel.
In google/amss: Agreggate Marketing System Simulator

Description Usage Arguments Value

Simulate the behavior of a traditional media channel, and generate associated observable variables such as media volume and spend.

DefaultTraditionalMediaModule(data.dt, budget.index, budget,
  audience.membership = list(), flighting = rep(1, length(budget.index)),
  unit.cost = 1, effectiveness.function = NULL, hill.ec = 1,
  hill.slope = 1, transition.matrices = list())

`data.dt`	data.table with rows corresponding to population segments and columns corresponding to specific variables
`budget.index`	vector specifying budget period each time point belongs to. For example, rep(1:4, each = 52) would correspond to 4 years of yearly budget periods.
`budget`	vector specifying the target spend for each budget period. For example, given the example `budget.index` from above, `budget = rep(1e6, 4)` would specify a budget of 1 million for each year.
`audience.membership`	list of multipliers used to calculate probability of audience membership. Each element of the list corresponds to a specific dimension of population segmentation. Multipliers corresponding to each dimension are multiplied to derive audience membership probability for each segment. A named list with members 'activity', 'favorability', 'loyalty', and 'availability' is expected. Each member is a numeric vector containing the multipliers to use for each state in the dimension. For example, if member "activity" is c(1, 0.5, 0.7), a multiplier of 0.7 should be used for all segments with activity state "purchase." By default, any missing members will have no effect.
`flighting`	specifies the relative amount to be spent on each time point within a budget period. For example, in a budget period of two weeks, `flighting = c(1,2)` specifies that twice 1/3 of the budget should be spent in the first week, and 2/3 in the second.
`unit.cost`	positive numeric specifying expected unit cost per exposure.
`effectiveness.function`	vectorized function mapping frequency to media effect (relative to transition matrices specifying maximum effect). The range of the function should be bounded between 0 and 1. Given the default value of NULL, the module will used the Hill transformation with parameters hill.ec and hill.slope.
`hill.ec`	parameter controlling the scaling of frequency vs. effect. This is the EC50 of the Hill transformation.
`hill.slope`	parameter controlling the scaling of frequency vs. effect. This is the maximum slope of the Hill transformation.
`transition.matrices`	list of transition matrices for each dimension of population segmentation that may be affected by marketing interventions. A named list with members 'activity', 'favorability', 'loyalty', and 'availability' is expected. By default, any missing members will have no effect.