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.
Value
histcompPIT has no return value; it is called for its side effect
of drawing the PIT histogram on the current graphics device.
qqcompPIT draws the uniform Q-Q plot on the current graphics device
and invisibly returns a list with components sample (the sample PIT
quantiles) and theoretical (the corresponding theoretical uniform
quantiles).
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.