pnbd.LL {BTYD} R Documentation

## Pareto/NBD Log-Likelihood

### Description

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

### Usage

```pnbd.LL(params, x, t.x, T.cal, hardie = TRUE)
```

### Arguments

 `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`.

### Value

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

### References

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`

### Examples

```# 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)
```

[Package BTYD version 2.4.3 Index]