surveillance (version 1.12.1)

pit: Non-Randomized Version of the PIT Histogram (for Count Data)

Description

See Czado et al. (2009).

Usage

pit(x, ...)
## S3 method for class 'default':
pit(x, pdistr, J = 10, relative = TRUE, ..., plot = list())

Arguments

x

numeric vector representing the observed counts.

pdistr

either a list of predictive cumulative distribution functions for the observations x, or (the name of) a single predictive CDF used for all x (with potentially varying arguments ...). It is checked that t

J

the number of bins of the histogram.

relative

logical indicating if relative frequency or the density should be plotted.

...

ignored if pdistr is a list. Otherwise, such additional arguments are used in sequential calls of pdistr via mapply(pdistr, x, ...).

plot

a list of arguments for plot.histogram. Otherwise, no plot will be produced.

Value

• an object of class "pit", which inherits from class "histogram" (see hist). It is returned invisibly if a plot is produced.

References

Czado, C., Gneiting, T. & Held, L. (2009): Predictive model assessment for count data. Biometrics, 65, 1254-1261.

Examples

## Simulation example of Czado et al. (2009, Section 2.4)
set.seed(100)
x <- rnbinom(200, mu = 5, size = 2)
pdistrs <- list("NB(5,0)"   = function (x) ppois(x, lambda=5),
                "NB(5,1/2)" = function (x) pnbinom(x, mu=5, size=2),
                "NB(5,1)"   = function (x) pnbinom(x, mu=5, size=1))
## Reproduce Figure 1
op <- par(mfrow = c(1,3))
for (i in seq_along(pdistrs)) {
    pit(x, pdistr = pdistrs[[i]], J = 10, relative = TRUE,
        plot = list(ylim = c(0,2.75), main = names(pdistrs)[i]))
    box()
}
par(op)

## Alternative call using ... arguments for pdistr (less efficient)
stopifnot(identical(pit(x, "pnbinom", mu = 5, size = 2, plot = FALSE),
                    pit(x, pdistrs[[2]], plot = FALSE)))

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