Poisson {kDGLM}

Poisson outcome for kDGLM models

Description

Creates an outcome with Poisson distribution with the chosen parameter.

Usage

Poisson(lambda, data, offset = as.matrix(data)^0)

Arguments

lambda

character: The name of the linear predictor associated with the rate (mean) parameter of the Poisson distribution. The parameter is treated as unknown and equal to the exponential of the associated linear predictor.

data

numeric: The values of the observed data.

offset

numeric: The offset at each observation. Must have the same shape as data.

Details

For evaluating the posterior parameters, we use the method proposed in Alves et al. (2024).

For the details about the implementation see dos Santos et al. (2024).

Value

A object of the class dlm_distr

References

Mariane Branco Alves, Helio S. Migon, Raíra Marotta, Junior, Silvaneo Vieira dos Santos (2024). “k-parametric Dynamic Generalized Linear Models: a sequential approach via Information Geometry.” 2201.05387.

Junior, Silvaneo Vieira dos Santos, Mariane Branco Alves, Helio S. Migon (2024). “kDGLM: an R package for Bayesian analysis of Dynamic Generialized Linear Models.”

See Also

fit_model

Other auxiliary functions for a creating outcomes: Gamma(), Multinom(), Normal(), summary.dlm_distr()

Examples

data <- c(AirPassengers)

level <- polynomial_block(rate = 1, D = 0.95, order = 2)
season <- harmonic_block(rate = 1, period = 12, D = 0.975)

outcome <- Poisson(lambda = "rate", data = data)

fitted.data <- fit_model(level, season,
  AirPassengers = outcome
)
summary(fitted.data)

plot(fitted.data, plot.pkg = "base")