PIT_Plot: PIT Plots for a CMP Object

In mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Description

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.

Usage

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)

Example output

mpcmp documentation built on Oct. 26, 2020, 9:07 a.m.