NBD...
In BTYDplus: Probabilistic Models for Assessing and Predicting your Customer Base

Description Usage Arguments Details Value Examples

Uses model parameter draws to return the expected number of repeat transactions that a randomly chosen customer (for whom we have no prior information) is expected to make in a given time period.

mcmc.ExpectedCumulativeTransactions(
  draws,
  T.cal,
  T.tot,
  n.periods.final,
  sample_size = 10000,
  covariates = NULL
)

`draws`	MCMC draws as returned by `*.mcmc.DrawParameters`
`T.cal`	A vector to represent customers' calibration period lengths (in other words, the `T.cal` column from a customer-by-sufficient-statistic matrix). Considering rounding in order to speed up calculations.
`T.tot`	End of holdout period. Must be a single value, not a vector.
`n.periods.final`	Number of time periods in the calibration and holdout periods.
`sample_size`	Sample size for estimating the probability distribution.
`covariates`	(optional) Matrix of covariates, for Pareto/NBD (Abe) model, passed to `abe.GenerateData` for simulating data.

The expected transactions need to be sampled. Due to this sampling, the return result varies from one call to another. Larger values of sample_size will generate more stable results.

Numeric vector of expected cumulative total repeat transactions by all customers.

data("groceryElog")
cbs <- elog2cbs(groceryElog)
param.draws <- pnbd.mcmc.DrawParameters(cbs,
  mcmc = 100, burnin = 50, thin = 10, chains = 1) # short MCMC to run demo fast
# Returns a vector containing expected cumulative repeat transactions for 104
# weeks, with every eigth week being reported.
mcmc.ExpectedCumulativeTransactions(param.draws,
  T.cal = cbs$T.cal, T.tot = 104, n.periods.final = 104/8, sample_size = 1000)