Gamma function

Gamma outcome for kDGLM models

Creates an outcome with gamma distribution with the chosen parameters (can only specify 2).

Gamma(
  phi = NA,
  mu = NA,
  alpha = NA,
  beta = NA,
  sigma = NA,
  data,
  offset = as.matrix(data)^0
)

Arguments

• phi: character or numeric: Name of the linear predictor associated with the shape parameter of the gamma distribution. If numeric, this parameter is treated as known and equal to the value passed. If a character, the parameter is treated as unknown and equal to the exponential of the associated linear predictor. It cannot be specified with alpha.
• mu: character: Name of the linear predictor associated with the mean parameter of the gamma distribution. The parameter is treated as unknown and equal to the exponential of the associated linear predictor.
• alpha: character: Name of the linear predictor associated with the shape parameter of the gamma distribution. The parameter is treated as unknown and equal to the exponential of the associated linear predictor. It cannot be specified with phi.
• beta: character: Name of the linear predictor associated with the rate parameter of the gamma distribution. The parameter is treated as unknown and equal to the exponential of the associated linear predictor. It cannot be specified with sigma.
• sigma: character: Name of the linear predictor associated with the scale parameter of the gamma distribution. The parameter is treated as unknown and equal to the exponential of the associated linear predictor. It cannot be specified with beta.
• data: numeric: Values of the observed data.
• offset: numeric: The offset at each observation. Must have the same shape as data.

Returns

An object of the class dlm_distr

Details

For evaluating the posterior parameters, we use the method proposed in \insertCite ArtigokParametrico;textualkDGLM.

For the details about the implementation see \insertCite ArtigoPacote;textualkDGLM.

Examples

structure <- polynomial_block(mu = 1, D = 0.95)

Y <- (cornWheat$corn.log.return[1:500] - mean(cornWheat$corn.log.return[1:500]))**2
outcome <- Gamma(phi = 0.5, mu = "mu", data = Y)
fitted.data <- fit_model(structure, corn = outcome)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

References

\insertAllCited

See Also

fit_model

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