BTYD (version 2.4)

pnbd.PlotFreqVsConditionalExpectedFrequency: Pareto/NBD Plot Frequency vs. Conditional Expected Frequency

Description

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

Usage

pnbd.PlotFreqVsConditionalExpectedFrequency(params, T.star, cal.cbs, x.star, censor, xlab = "Calibration period transactions", ylab = "Holdout period transactions", xticklab = NULL, title = "Conditional Expectation")

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.

T.star

length of then holdout period.

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.

x.star

vector of transactions made by each customer in the holdout period.

censor

integer used to censor the data. See details.

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.

Value

Holdout period transaction frequency comparison matrix (actual vs. expected).

Details

This function requires a censor number, which cannot be higher than the highest frequency in the calibration period CBS. The output matrix will have (censor + 1) bins, starting at frequencies of 0 transactions and ending at a bin representing calibration period frequencies at or greater than the censor number.

Examples

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
# the maximum censor number that can be used
max(cal.cbs[,"x"])

# plot conditional expected holdout period frequencies,
# binned according to calibration period frequencies
pnbd.PlotFreqVsConditionalExpectedFrequency(est.params, T.star=39, cal.cbs, x.star, censor=7)