PIT Plots for a glarma Object

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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

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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" <d.scott@auckland.ac.nz> and "Cenanning Li" <cli113@aucklanduni.ac.nz>

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

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## For examples see example(plot.glarma)