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, ...)



The fitted spending model to plot


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


Show details about the running of the function.




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

See also

plot for transaction models


# \donttest{ data("cdnow") clv.cdnow <- clvdata(cdnow, date.format="ymd", time.unit = "week", estimation.split = "1997-09-30") <- gg( = clv.cdnow)
#> Starting estimation...
#> Estimation finished!
# Compare empirical to theoretical distribution plot(
if (FALSE) { # Modify the created plot further library(ggplot2) gg.cdnow <- plot( gg.cdnow + ggtitle("CDnow Spending Distribution") } # }