nrPIT: Non-randomized Probability Integral Transform
In mpcmp: Mean-Parametrized Conway-Maxwell Poisson (COM-Poisson) Regression

Description Usage Arguments Details Value References Examples

Functions to produce the non-randomized probability integral transform (PIT) to check the adequacy of the distributional assumption of the COM-Poisson model. The majority of the code and descriptions are taken from Dunsmuir and Scott (2015).

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compPredProb(object)

compPIT(object, bins = 10)

`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.

These functions are used to obtain the predictive probabilities and the probability integral transform for a fitted COM-Poisson model. The majority of the code and descriptions are taken from Dunsmuir and Scott (2015).

compPredprob returns a list with values:

`upper`	the predictive cumulative probabilities used as the upper bound for computing the non-randomized PIT.
`lower`	the predictive cumulative probabilities used as the upper bound for computing the non-randomized PIT.

compPIT returns a list with values:

`conditionalPIT`	the conditional probability integral transformation given the observed counts.
`PIT`	the probability integral transformation.

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.

data(takeoverbids)
M.bids <- glm.cmp(numbids ~ leglrest + rearest + finrest + whtknght 
    + bidprem + insthold + size + sizesq + regulatn, data=takeoverbids)
compPredProb(M.bids)
compPIT(M.bids)