PIT Plots for a CMP Object

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

histcompPIT(
  object,
  bins = 10,
  line = TRUE,
  colLine = "red",
  colHist = "royal blue",
  lwdLine = 2,
  main = NULL,
  ...
)

qqcompPIT(
  object,
  bins = 10,
  col1 = "red",
  col2 = "black",
  lty1 = 1,
  lty2 = 2,
  type = "l",
  main = NULL,
  ...
)

Arguments

object

an object class "cmp", obtained from a call to glm.cmp.

bins

numeric; the number of bins shown in the PIT histogram or the PIT Q-Q plot.

line

logical; if TRUE (default), the line for displaying the standard uniform distribution will be shown for the purpose of comparison.

colLine

numeric or character: the colour of the line for comparison in PIT histogram.

colHist

numeric or character; the colour of the histogram for PIT.

lwdLine

numeric; the line widths for the comparison line in PIT histogram.

main

character string; a main title for the plot.

...

other arguments passed to plot.default and plot.ts.

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

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

1-character string; the type of plot for the sample uniform Q-Q plot in PIT.

Details

The histogram and the Q-Q plot are used to compare the fitted profile with a standard uniform distribution. If they match relatively well, it means the CMP distribution is appropriate for the data.

The gg_histcompPIT and gg_qqcompPIT functions would provide the same two plots but in ggplot format.

References

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

Dunsmuir, W.T.M. and Scott, D.J. (2015). The glarma Package for Observation-Driven Time Series Regression of Counts. Journal of Statistical Software, 67, 1--36.

See also

gg_histcompPIT, gg_qqcompPIT, plot.cmp and autoplot.

Examples

## For examples see example(plot.cmp)