`R/f_s3generics_clvfittedspending_plot.R`

`plot.clv.fitted.spending.Rd`

Compares the density of the observed average spending per transaction (empirical distribution) to the model's distribution of mean transaction spending (weighted by the actual number of transactions).

# S3 method for clv.fitted.spending plot(x, n = 256, verbose = TRUE, ...) # S4 method for clv.fitted.spending plot(x, n = 256, verbose = TRUE, ...)

x | The fitted spending model to plot |
---|---|

n | Number of points at which the empirical and model density are calculated. Should be a power of two. |

verbose | Show details about the running of the function. |

... | Ignored |

An object of class `ggplot`

from package `ggplot2`

is returned by default.

Colombo R, Jiang W (1999). “A stochastic RFM model.” Journal of Interactive Marketing, 13(3), 2–12.

Fader PS, Hardie BG, Lee K (2005). “RFM and CLV: Using Iso-Value Curves for Customer Base Analysis.” Journal of Marketing Research, 42(4), 415–430.

Fader PS, Hardie BG (2013). “The Gamma-Gamma Model of Monetary Value.” URL http://www.brucehardie.com/notes/025/gamma_gamma.pdf.

`plot`

for transaction models

# \donttest{ data("cdnow") clv.cdnow <- clvdata(cdnow, date.format="ymd", time.unit = "week", estimation.split = "1997-09-30") est.gg <- gg(clv.data = clv.cdnow)#>#>