cocoPit {coconots} | R Documentation |
Probability Integral Transform Based Model Assessment Procedure
Description
Computes the probability integral transform (PIT) and provides the non-randomized PIT histogram for assessing absolute performance of a fitted model as proposed by Czado et al. (2009).
Usage
cocoPit(coco, J = 10, conf.alpha = 0.05, julia = FALSE)
Arguments
coco |
An object of class coco |
J |
Number of bins for the histogram (default: 10) |
conf.alpha |
Significance level for the confidence intervals (default: 0.05) |
julia |
if TRUE, the PIT is computed with julia (default: FALSE) |
Details
The adequacy of a distributional assumption for a model is assessed by
checking the cumulative non-randomized PIT distribution for uniformity.
A useful graphical device is the PIT histogram, which displays this
distribution to J equally spaced bins. We supplement the graph by
incorporating approximately 100(1 - \alpha)\%
confidence intervals obtained
from a standard chi-square goodness-of-fit test of the null hypothesis that
the J bins of the histogram are drawn from a uniform distribution.
For details, see Jung, McCabe and Tremayne (2016).
Value
an object of class cocoPit. It contains the probability integral transform values, p-value of the chi-square goodness of fit test and information on the model specifications.
Author(s)
Manuel Huth
References
Czado, C., Gneiting, T. and Held, L. (2009) Predictive model assessment for count data. Biometrics 65, 1254–61.
Jung, R. C., McCabe, B.P.M. and Tremayne, A.R. (2016). Model validation and diagnostics. In Handbook of Discrete Valued Time Series. Edited by Davis, R.A., Holan, S.H., Lund, R. and Ravishanker, N.. Boca Raton: Chapman and Hall, pp. 189–218.
Jung, R. C. and Tremayne, A. R. (2011) Convolution-closed models for count time series with applications. Journal of Time Series Analysis, 32, 3, 268–280.
Examples
lambda <- 1
alpha <- 0.4
set.seed(12345)
data <- cocoSim(order = 1, type = "Poisson", par = c(lambda, alpha), length = 100)
fit <- cocoReg(order = 1, type = "Poisson", data = data)
#PIT R implementation
pit_r <- cocoPit(fit)
plot(pit_r)