Description Usage Value Author(s) References Examples
Provide the current available distributions that can be used as an innovation process in a fit of the INARS(1) model.
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The above functions return a list of the following components:
Probability mass function function(z, mu, disp)
.
A random generator function(n, mu, disp)
.
The parameters associated with these functions are:
z
: An integer-valued quantile.
mu
: The real-valued mean of the process innovation.
disp
: The positive dispersion parameter of the process innovation.
n
: Numbers of random values to return.
These functions are used to define the innovation process in the inars()
fit.
Rodrigo M. R. Medeiros <rodrigo.matheus@live.com>
Skellam, J. G. (1946). The frequency distribution of the difference between two Poisson variates belonging to different populations. Journal of the Royal Statistical Society: Series A, 109, 296.
Andersson, J., & Karlis, D. (2014). A parametric time series model with covariates for integers in Z. Statistical Modelling, 14, 135–156.
Chakraborty, S., & Chakravarty, D. (2016). A new discrete probability distribution with integer support on (–infty, infty). Communications in Statistics-Theory and Methods, 45, 492–505.
Medeiros, R.M.R., Bourguignon M. (2021) A useful regression model for integers on Z with application to quality of life in prisons. Submitted.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | sdl <- SDL()
skellam <- SK()
dlog <- DLOG()
zvals <- -10:10
## SDL
barplot(sdl$d(zvals, -2, 4), names.arg = zvals)
barplot(sdl$d(zvals, 0, 4), names.arg = zvals)
barplot(sdl$d(zvals, 2, 4), names.arg = zvals)
## Skellam
barplot(skellam$d(zvals, -2, 4), names.arg = zvals)
barplot(skellam$d(zvals, 0, 4), names.arg = zvals)
barplot(skellam$d(zvals, 2, 4), names.arg = zvals)
## Discret logistic
barplot(dlog$d(zvals, -2, 1), names.arg = zvals)
barplot(dlog$d(zvals, 0, 1), names.arg = zvals)
barplot(dlog$d(zvals, 2, 1), names.arg = zvals)
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