library("gamlss2")
## simulate count data using the Poisson distribution
set.seed(111)
y <- rpois(1000, lambda = 10)
## create a discretized family using the BCT distribution (with log link for mu)
fam <- discretize(family = BCT(mu.link = "log"))
## fit a count regression model using the discretized family
fit_family(y, family = fam)
## including covariate
x <- sort(runif(1000, -3, 3))
y <- rpois(1000, lambda = exp(sin(x)))
m <- gamlss2(y ~ s(x) | s(x) | s(x) | s(x), family = fam)
## plot effects
plot(m)
## predict 95
p <- quantile(m, probs = 0.95)
plot(x, y, pch = 19, col = adjustcolor(1, 0.3))
lines(p ~ x, lty = 1, col = 4, lwd = 3)Discretize Continuous Distribution Family for Count Regression Models
Description
This function takes any continuous distribution family object and discretizes it, enabling it to be used for the estimation of count regression models. The discretized family can then be used in gamlss2 models that deal with count data.
Usage
discretize(family = NO)
Arguments
family
|
A continuous distribution family object. The family will be discretized for modeling count data, where the distribution is adapted for count outcomes. |
Details
The function discretizes a continuous distribution family by converting its cumulative distribution function (CDF) into a probability mass function (PMF). This is done by computing the difference between the CDF evaluated at adjacent points. The resulting discretized distribution can be used in count regression models to estimate the relationship between count data and explanatory variables.
Value
Returns an object of class “gamlss2.family”, which is a discretized version of the input continuous family object, suitable for use in gamlss2 models for count data.
See Also
gamlss2, gamlss2.family