pnbd.cbs.LL {BTYD} | R Documentation |
Calculates the log-likelihood of the Pareto/NBD model.
pnbd.cbs.LL(params, cal.cbs, hardie = TRUE)
params |
Pareto/NBD parameters - a vector with r, alpha, s, and beta, in that order. r and alpha are unobserved parameters for the NBD transaction process. s and beta are unobserved parameters for the Pareto (exponential gamma) dropout process. |
cal.cbs |
calibration period CBS (customer by sufficient statistic). It
must contain columns for frequency ("x"), recency ("t.x"), and total time
observed ("T.cal"). Note that recency must be the time between the start of
the calibration period and the customer's last transaction, not the time
between the customer's last transaction and the end of the calibration
period. If your data is compressed (see |
hardie |
The log-likelihood of the provided data.
Fader, Peter S., and Bruce G.S. Hardie. "A Note on Deriving the Pareto/NBD Model and Related Expressions." November. 2005. Web. http://www.brucehardie.com/notes/008/
data(cdnowSummary) cal.cbs <- cdnowSummary$cbs # cal.cbs already has column names required by method # random assignment of parameters params <- c(0.5, 8, 0.7, 10) # returns the log-likelihood of the given parameters pnbd.cbs.LL (params, cal.cbs, TRUE) # compare the speed and results to the following: cal.cbs.compressed <- dc.compress.cbs(cal.cbs) pnbd.cbs.LL (params, cal.cbs.compressed, TRUE)