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## Authors: Lukasz Dziurzynski, Daniel McCarthy, Edward Wadsworth
data(cdnowSummary)
## Get the calibration period customer-by-sufficient-statistic matrix from the cdnow data:
cbs <- cdnowSummary$cbs
## Estimate parameters for the Pareto/NBD model from the CBS:
par.start <- c(0.5, 1, 0.5, 1)
params <- pnbd.EstimateParameters(cbs,
par.start,
hardie = TRUE)
params
## Check log-likelihood of the params:
pnbd.cbs.LL(params,
cbs,
hardie = TRUE)
## Plot the comparison of actual and expected calibration period frequencies:
pnbd.PlotFrequencyInCalibration(params = params,
cal.cbs = cbs,
censor = 7,
plotZero = TRUE,
hardie = TRUE)
T.star <- 39 # Length of holdout period
x.star <- cbs[,"x.star"] # Transactions made by each customer in the holdout period
## Plot the comparison of actual and conditional expected holdout period frequencies,
## binned according to calibration period frequencies:
pnbd.PlotFreqVsConditionalExpectedFrequency(params = params,
T.star = T.star,
cal.cbs = cbs,
x.star = x.star,
censor = 7,
hardie = TRUE)
## Plot the comparison of actual and conditional expected holdout period frequencies,
## binned according to calibration period recencies:
pnbd.PlotRecVsConditionalExpectedFrequency(params,
cbs,
T.star,
x.star,
hardie = TRUE)
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