plotPIT: PIT Plots for a 'glarma' Object

In glarma: Generalized Linear Autoregressive Moving Average Models

Description

Two plots for the non-randomized PIT are currently available for checking the distributional assumption of the fitted GLARMA model: the PIT histogram, and the uniform Q-Q plot for PIT.

Usage

histPIT(object, bins = 10, line = TRUE, colLine = "red",
        colHist = "royal blue", lwdLine = 2, main = NULL, ...)
qqPIT(object, bins = 10, col1 = "red", col2 = "black",
      lty1 = 1, lty2 = 2, type = "l", main = NULL, ...)

Arguments

object	An object of class "glarma", obtained from a call to glarma.
bins	Numeric; the number of bins shown in the PIT histogram or the PIT Q-Q plot. By default, it is 10.
line	Logical; if TRUE, the line for displaying the standard uniform distribution will be shown for the purpose of comparison. The default is TRUE.
colLine	Numeric or character; the colour of the line for comparison in PIT histogram.
lwdLine	Numeric; the line widths for the comparison line in PIT histogram.
colHist	Numeric or character; the colour of the histogram for PIT.
col1	Numeric or character; the colour of the sample uniform Q-Q plot in PIT.
col2	Numeric or character; the colour of the theoretical uniform Q-Q plot in PIT.
lty1	An integer or character string; the line types for the sample uniform Q-Q plot in PIT, see par(lty = .).
lty2	An integer or character string; the line types for the theoretical uniform Q-Q plot in PIT, see par(lty = .).
type	A 1-character string; the type of plot for the sample uniform Q-Q plot in PIT.
main	A character string giving a title. For each plot the default provides a useful title.
...	Further arguments passed to plot.default and plot.ts.

Details

The histogram and the Q-Q plot are used to compare the fitted profile with U(0, 1). If they match relatively well, it means the distributional assumption is satisfied.

Author(s)

"David J. Scott" <[e-mail]> and "Cenanning Li" <[e-mail]>

References

Czado, Claudia and Gneiting, Tilmann and Held, Leonhard (2009) Predictive model assessment for count data. Biometrics, 65, 1254–1261.

Jung, Robert.C and Tremayne, A.R (2011) Useful models for time series of counts or simply wrong ones? AStA Advances in Statistical Analysis, 95, 59–91.

Examples

## For examples see example(plot.glarma)

glarma documentation built on May 2, 2019, 6:33 a.m.