pnbd.LL: Pareto/NBD Log-Likelihood
In BTYD: Implementing BTYD Models with the Log Sum Exp Patch

Description Usage Arguments Value References See Also Examples

Calculates the log-likelihood of the Pareto/NBD model.

1	pnbd.LL(params, x, t.x, T.cal, 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.
`x`	number of repeat transactions in the calibration period T.cal, or a vector of transaction frequencies.
`t.x`	time of most recent repeat transaction, or a vector of recencies.
`T.cal`	length of calibration period, or a vector of calibration period lengths.
`hardie`	if TRUE, use `h2f1` instead of `hypergeo`.

A vector of log-likelihoods as long as the longest input vector (x, t.x, or T.cal).

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/

pnbd.EstimateParameters

# Returns the log likelihood of the parameters for a customer who
# made 3 transactions in a calibration period that ended at t=6,
# with the last transaction occurring at t=4.
pnbd.LL(params, x=3, t.x=4, T.cal=6, hardie = TRUE)

# We can also give vectors as function parameters:
set.seed(7)
x <- sample(1:4, 10, replace = TRUE)
t.x <- sample(1:4, 10, replace = TRUE)
T.cal <- rep(4, 10)
pnbd.LL(params, x, t.x, T.cal, hardie = TRUE)