Description Usage Arguments Value References
Calculates the log-likelihood of the (M)BG/CNBD-k model.
1 2 3 4 5 6 7 | mbgcnbd.cbs.LL(params, cal.cbs)
mbgcnbd.LL(params, x, t.x, T.cal, litt)
bgcnbd.cbs.LL(params, cal.cbs)
bgcnbd.LL(params, x, t.x, T.cal, litt)
|
params |
A vector with model parameters |
cal.cbs |
Calibration period customer-by-sufficient-statistic (CBS)
data.frame. It must contain a row for each customer, and columns |
x |
frequency, i.e. number of re-purchases |
t.x |
recency, i.e. time elapsed from first purchase to last purchase |
T.cal |
total time of observation period |
litt |
sum of logarithmic interpurchase times |
For bgcnbd.cbs.LL
, the total log-likelihood of the provided
data. For bgcnbd.LL
, a vector of log-likelihoods as long as the
longest input vector (x
, t.x
, or T.cal
).
(M)BG/CNBD-k: Reutterer, T., Platzer, M., & Schroeder, N. (2020). Leveraging purchase regularity for predicting customer behavior the easy way. International Journal of Research in Marketing. doi: 10.1016/j.ijresmar.2020.09.002
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