bgbb.PosteriorMeanDropoutRate: BG/BB Posterior Mean Dropout Rate
In BTYD: Implementing BTYD Models with the Log Sum Exp Patch

Description Usage Arguments Details Value References See Also Examples

Computes the mean value of the marginal posterior value of Theta, the geometric dropout process parameter.

1	bgbb.PosteriorMeanDropoutRate(params, x, t.x, n.cal)

`params`	BG/BB parameters - a vector with alpha, beta, gamma, and delta, in that order. Alpha and beta are unobserved parameters for the beta-Bernoulli transaction process. Gamma and delta are unobserved parameters for the beta-geometric dropout process.
`x`	the number of repeat transactions made by the customer in the calibration period. Can also be vector of frequencies - see details.
`t.x`	recency - the transaction opportunity in which the customer made their last transaction. Can also be a vector of recencies - see details.
`n.cal`	number of transaction opportunities in the calibration period. Can also be a vector of calibration period transaction opportunities - see details.

E(Theta | alpha, beta, gamma, delta, x, t.x, n). This is calculated by setting l = 0 and m = 1 in bgbb.PosteriorMeanLmProductMoment.

x, t.x, and n.cal may be vectors. The standard rules for vector operations apply - if they are not of the same length, shorter vectors will be recycled (start over at the first element) until they are as long as the longest vector. It is advisable to keep vectors to the same length and to use single values for parameters that are to be the same for all calculations. If one of these parameters has a length greater than one, the output will be also be a vector.

The posterior mean dropout rate.

Fader, Peter S., Bruce G.S. Hardie, and Jen Shang. "Customer-Base Analysis in a Discrete-Time Noncontractual Setting." Marketing Science 29(6), pp. 1086-1108. 2010. INFORMS. Web.

bgbb.rf.matrix.PosteriorMeanDropoutRate

 
data(donationsSummary)

rf.matrix <- donationsSummary$rf.matrix
# donationsSummary$rf.matrix already has appropriate column names

# starting-point parameters
startingparams <- c(1, 1, 0.5, 3)
# estimated parameters
est.params <- bgbb.EstimateParameters(rf.matrix, startingparams)

# return the posterior mean dropout rate vector
bgbb.rf.matrix.PosteriorMeanDropoutRate(est.params, rf.matrix)