glarma (version 1.7-1)

PIT: Non-randomized Probability Integral Transformation

Description

Functions to produce the non-randomized probability integral transform (PIT) to check the adequacy of the distributional assumption of the GLARMA model.

Usage

glarmaPredProb(object)
glarmaPIT(object, bins = 10)

Value

glarmaPredProb 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 lower bound for computing the non-randomized PIT.

glarmaPIT 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 lower bound for computing the non-randomized PIT.

conditionalPIT

the conditional probability integral transformation given the observed counts.

PIT

the probability integral transformation.

Arguments

object

An object of class "glarma", obtained from a call to glarma.

bins

Numeric; the number of bins used in the PIT.

Author

"David J. Scott" <[e-mail]> and "Cenanning Li" <[e-mail]>

Details

These functions are used for the assessment of predictive distributions in discrete data. They obtain the predictive probabilities and the probability integral transformation for a fitted GLARMA model.

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? Advances in Statistical Analysis, 95, 59--91.

Examples

### Example from Davis, Dunsmuir Wang (1999)
## MA(1,2,5), Pearson Residuals, Fisher Scoring
data(Polio)
y <- Polio[, 2]
X <- as.matrix(Polio[, 3:8])
glarmamod <- glarma(y, X, thetaLags = c(1,2,5), type = "Poi", method = "FS",
                    residuals = "Pearson", maxit = 100, grad = 2.22e-16)
glarmaPredProb(glarmamod)
glarmaPIT(glarmamod)