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10 bgbb.EstimateParameters # 10%. bgbb.DERT(params, x=3, t.x=4, n.cal=6, d=0.1) # We can also compare DERT for several customers: bgbb.DERT(params, x=1:6, t.x=6, n.cal=6, d=0.1) data(donationsSummary) rf.matrix
gbb.Expectation 11 Details The best-fitting parameters are determined using the bgbb.rf.matrix.LL function. The sum of the log-likelihood for each customer (for a set of parameters) is maximized in order to estimate paramaters. A set of starting parameters must be provided for this method. If no parameters are provided, (1,1,1,1) is used as a default. It may be useful to use starting values for parameters that represent your best guess of the heterogeneity in the transaction and dropout rates of customers. It may be necessary to run the estimation from multiple starting points to ensure that it converges. To compare the log-likelihoods of different parameters, use bgbb.rf.matrix.LL. The lower bound on the parameters to be estimated is always zero, since BG/BB parameters cannot be negative. The upper bound can be set with the max.param.value parameter. Value Vector of estimated paramaters. See Also bgbb.rf.matrix.LL Examples data(donationsSummary) rf.matrix
Page 1 and 2: Type Package Title Implementing Buy
Page 3 and 4: BTYD-package 3 dc.GetLastPurchasePe
Page 5 and 6: BTYD-package 5 Package: BTYD Type:
Page 7 and 8: gbb.ConditionalExpectedTransactions
Page 9: gbb.DERT 9 Arguments params x t.x n
Page 13 and 14: gbb.HeatmapHoldoutExpectedTrans 13
Page 15 and 16: gbb.PAlive 15 Value For bgbb.cbs.LL
Page 17 and 18: gbb.PlotDropoutRateHeterogeneity 17
Page 19 and 20: gbb.PlotFrequencyInHoldout 19 # plo
Page 21 and 22: gbb.PlotFreqVsConditionalExpectedFr
Page 23 and 24: gbb.PlotTrackingCum 23 Value Holdou
Page 25 and 26: gbb.PlotTrackingInc 25 # get the an
Page 27 and 28: gbb.pmf 27 Arguments params BG/BB p
Page 29 and 30: gbb.pmf.General 29 Arguments params
Page 31 and 32: gbb.PosteriorMeanLmProductMoment 31
Page 33 and 34: gnbd.compress.cbs 33 rf.matrix rece
Page 35 and 36: gnbd.ConditionalExpectedTransaction
Page 37 and 38: gnbd.EstimateParameters 37 Details
Page 39 and 40: gnbd.ExpectedCumulativeTransactions
Page 41 and 42: gnbd.PAlive 41 Details x, t.x, T.ca
Page 43 and 44: gnbd.PlotDropoutRateHeterogeneity 4
Page 45 and 46: gnbd.PlotFreqVsConditionalExpectedF
Page 47 and 48: gnbd.PlotRecVsConditionalExpectedFr
Page 49 and 50: gnbd.PlotTrackingInc 49 Examples da
Page 51 and 52: gnbd.pmf 51 Arguments params lim BG
Page 53 and 54: gnbd.pmf.General 53 Arguments param
Page 55 and 56: dc.BuildCBSFromCBTAndDates 55 Detai
Page 57 and 58: dc.BuildCBTFromElog 57 dc.BuildCBTF
Page 59 and 60: dc.CreateSpendCBT 59 Value Reach cu
Page 61 and 62:
dc.ElogToCbsCbt 61 Arguments Value
Page 63 and 64:
dc.GetFirstPurchasePeriodsFromCBT 6
Page 65 and 66:
dc.MakeRFmatrixCal 65 Arguments fre
Page 67 and 68:
dc.MergeCustomers 67 Value Matrix w
Page 69 and 70:
dc.MergeTransactionsOnSameDate 69 #
Page 71 and 72:
dc.PlotLogLikelihoodContour 71 Exam
Page 73 and 74:
dc.PlotLogLikelihoodContours 73 Det
Page 75 and 76:
dc.RemoveTimeBetween 75 Value Event
Page 77 and 78:
discreteSimElog 77 discreteSimElog
Page 79 and 80:
pnbd.ConditionalExpectedTransaction
Page 81 and 82:
pnbd.DERT 81 pnbd.DERT Pareto/NBD D
Page 83 and 84:
pnbd.EstimateParameters 83 Details
Page 85 and 86:
pnbd.ExpectedCumulativeTransactions
Page 87 and 88:
pnbd.LL 87 Details x, t.x, T.cal ma
Page 89 and 90:
pnbd.Plot.DERT 89 Examples params
Page 91 and 92:
pnbd.PlotFrequencyInCalibration 91
Page 93 and 94:
pnbd.PlotFreqVsConditionalExpectedF
Page 95 and 96:
pnbd.PlotTrackingCum 95 Value Matri
Page 97 and 98:
pnbd.PlotTrackingInc 97 Usage pnbd.
Page 99 and 100:
pnbd.pmf 99 pnbd.pmf Pareto/NBD Pro
Page 101 and 102:
spend.EstimateParameters 101 Value
Page 103 and 104:
spend.LL 103 m.x x the customer’s
Page 105 and 106:
spend.marginal.likelihood 105 sum(s
Page 107 and 108:
spend.plot.average.transaction.valu
Page 109:
INDEX 109 dc.FilterCustByBirth, 62
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