mbgcnbd.EstimateParameters: (M)BG/CNBD-k Parameter Estimation

Description

Estimates parameters for the (M)BG/CNBD-k model via Maximum Likelihood Estimation.

Usage

mbgcnbd.EstimateParameters(cal.cbs, k = NULL, par.start = c(1, 3, 1, 3),
  max.param.value = 10000, trace = 0)
bgcnbd.EstimateParameters(cal.cbs, k = NULL, par.start = c(1, 3, 1, 3),
  max.param.value = 10000, trace = 0)
mbgnbd.EstimateParameters(cal.cbs, par.start = c(1, 3, 1, 3),
  max.param.value = 10000, trace = 0)

Arguments

cal.cbs

Calibration period customer-by-sufficient-statistic (CBS) data.frame. It must contain a row for each customer, and columns x for frequency, t.x for recency , T.cal for the total time observed, as well as the sum over logarithmic intertransaction times litt, in case that k is not provided. A correct format can be easily generated based on the complete event log of a customer cohort with elog2cbs.

Integer-valued degree of regularity for Erlang-k distributed interpurchase times. By default this k is not provdied, and a grid search from 1 to 12 is performed in order to determine the best-fitting k. The grid search is stopped early, if the log-likelihood doesn't increase anymore when increasing k beyond 4.

par.start

Initial (M)BG/CNBD-k parameters. A vector with r, alpha, a and b in that order.

max.param.value

Upper bound on parameters.

trace

If larger than 0, then the parameter values are is printed every trace-step of the maximum likelihood estimation search.

Value

A vector of estimated parameters.

References

(M)BG/CNBD-k: Platzer Michael, and Thomas Reutterer (forthcoming)

MBG/NBD: Batislam, E.P., M. Denizel, A. Filiztekin. 2007. Empirical validation and comparison of models for customer base analysis. International Journal of Research in Marketing 24(3) 201<U+2013>209.

Examples

Run this code

# NOT RUN {
cbs <- cdnow.sample()$cbs
(params <- mbgcnbd.EstimateParameters(cbs))
# }