pnbd.PlotRecVsConditionalExpectedFrequency: Pareto/NBD Plot Actual vs. Conditional Expected Frequency by...
In BTYD: Implementing BTYD Models with the Log Sum Exp Patch

Description Usage Arguments Details Value Examples

Plots the actual and conditional expected number of transactions made by customers in the holdout period, binned according to calibration period recencies. Also returns a matrix with this comparison and the number of customers in each bin.

pnbd.PlotRecVsConditionalExpectedFrequency(
  params,
  cal.cbs,
  T.star,
  x.star,
  hardie = TRUE,
  xlab = "Calibration period recency",
  ylab = "Holdout period transactions",
  xticklab = NULL,
  title = "Actual vs. Conditional Expected Transactions by Recency"
)

`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.
`T.star`	length of the holdout period. It must be a scalar for this plot's purposes: you have one holdout period of a given length.
`x.star`	vector of transactions made by each customer in the holdout period.
`hardie`	if TRUE, have `pnbd.ConditionalExpectedTransactions` use `h2f1` instead of `hypergeo`.
`xlab`	descriptive label for the x axis.
`ylab`	descriptive label for the y axis.
`xticklab`	vector containing a label for each tick mark on the x axis.
`title`	title placed on the top-center of the plot.

This function does bin customers exactly according to recency; it bins customers according to integer units of the time period of cal.cbs. Therefore, if you are using weeks in your data, customers will be binned as follows: customers with recencies between the start of the calibration period (inclusive) and the end of week one (exclusive); customers with recencies between the end of week one (inclusive) and the end of week two (exlusive); etc.

The matrix and plot will contain the actual number of transactions made by each bin in the holdout period, as well as the expected number of transactions made by that bin in the holdout period, conditional on that bin's behavior during the calibration period.

Matrix comparing actual and conditional expected transactions in the holdout period.

data(cdnowSummary)

cal.cbs <- cdnowSummary$cbs
# cal.cbs already has column names required by method

# number of transactions by each customer in the 39 weeks following
# the calibration period
x.star <- cal.cbs[,"x.star"]

# parameters estimated using pnbd.EstimateParameters
est.params <- cdnowSummary$est.params

# plot conditional expected holdout period transactions, binned according to
# calibration period recencies
pnbd.PlotRecVsConditionalExpectedFrequency(params = est.params, 
                                           cal.cbs = cal.cbs, 
                                           T.star = 39, 
                                           x.star = x.star, 
                                           hardie = TRUE)