plot.clv.fitted.transactions: Plot expected and actual repeat transactions

Description

Plot the actual repeat transactions and overlay it with the repeat transaction as predicted by the fitted model. Currently, following previous literature, the in-sample unconditional expectation is plotted in the holdout period. In the future, we might add the option to also plot the summed CET for the holdout period as an alternative evaluation metric.

Usage

# S3 method for clv.fitted.transactions
plot(
  x,
  prediction.end = NULL,
  newdata = NULL,
  cumulative = FALSE,
  transactions = TRUE,
  label = NULL,
  plot = TRUE,
  verbose = TRUE,
  ...
)
# S4 method for clv.fitted.transactions
plot(
  x,
  prediction.end = NULL,
  newdata = NULL,
  cumulative = FALSE,
  transactions = TRUE,
  label = NULL,
  plot = TRUE,
  verbose = TRUE,
  ...
)

Arguments

The fitted clv model to plot

prediction.end

Until what point in time to predict. This can be the number of periods (numeric) or a form of date/time object. See details.

newdata

An object of class clv.data for which the plotting should be made with the fitted model. If none or NULL is given, the plot is made for the data on which the model was fit.

cumulative

Whether the cumulative expected (and actual) transactions should be plotted.

transactions

Whether the actual observed repeat transactions should be plotted.

label

Character string to label the model in the legend

plot

Whether a plot should be created or only the assembled data is returned.

verbose

Show details about the running of the function.

...

Ignored

Value

An object of class ggplot from package ggplot2 is returned by default. If the parameter plot is FALSE, the data that would have been melted and used to create the plot is returned. It is a data.table which contains the following columns:

period.until

The timepoint that marks the end (up until and including) of the period to which the data in this row refers.

Number of Repeat Transactions

The number of actual repeat transactions in the period that ends at period.until. Only if transactions is TRUE.

"Name of Model" or "label"

The value of the unconditional expectation for the period that ends on period.until.

Details

prediction.end indicates until when to predict or plot and can be given as either a point in time (of class Date, POSIXct, or character) or the number of periods. If prediction.end is of class character, the date/time format set when creating the data object is used for parsing. If prediction.end is the number of periods, the end of the fitting period serves as the reference point from which periods are counted. Only full periods may be specified. If prediction.end is omitted or NULL, it defaults to the end of the holdout period if present and to the end of the estimation period otherwise.

The first prediction period is defined to start right after the end of the estimation period. If for example weekly time units are used and the estimation period ends on Sunday 2019-01-01, then the first day of the first prediction period is Monday 2019-01-02. Each prediction period includes a total of 7 days and the first prediction period therefore will end on, and include, Sunday 2019-01-08. Subsequent prediction periods again start on Mondays and end on Sundays. If prediction.end indicates a timepoint on which to end, this timepoint is included in the prediction period.

Note that only whole periods can be plotted and that the prediction end might not exactly match prediction.end. See the Note section for more details.

The newdata argument has to be a clv data object of the exact same class as the data object on which the model was fit. In case the model was fit with covariates, newdata needs to contain identically named covariate data.

The use case for newdata is mainly two-fold: First, to estimate model parameters only on a sample of the data and then use the fitted model object to predict or plot for the full data set provided through newdata. Second, for models with dynamic covariates, to provide a clv data object with longer covariates than contained in the data on which the model was estimated what allows to predict or plot further. When providing newdata, some models might require additional steps that can significantly increase runtime.

Examples

Run this code

# NOT RUN {
data("cdnow")

# Fit ParetoNBD model on the CDnow data
pnbd.cdnow <- pnbd(clvdata(cdnow, time.unit="w",
                           estimation.split=37,
                           date.format="ymd"))

# Plot actual repeat transaction, overlayed with the
#  expected repeat transactions as by the fitted model
plot(pnbd.cdnow)

# Plot cumulative expected transactions of only the model
plot(pnbd.cdnow, cumulative=TRUE, transactions=FALSE)

# Plot forecast until 2001-10-21
plot(pnbd.cdnow, prediction.end = "2001-10-21")

# Plot until 2001-10-21, as date
plot(pnbd.cdnow,
     prediction.end = lubridate::dym("21-2001-10"))

# Plot 15 time units after end of estimation period
plot(pnbd.cdnow, prediction.end = 15)

# Save the data generated for plotting
#   (period, actual transactions, expected transactions)
plot.out <- plot(pnbd.cdnow, prediction.end = 15)

# A ggplot object is returned that can be further tweaked
library("ggplot2")
gg.pnbd.cdnow <- plot(pnbd.cdnow)
gg.pnbd.cdnow + ggtitle("PNBD on CDnow")

# }
# NOT RUN {
# }