kdglm function

Fitting kDGLM models

Fit a model given its structure and the observed data. This function can be used for any supported family (see vignette).

kdglm(formula, ..., family, data = NULL, offset = NULL, p.monit = NA)

Arguments

• formula: an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted.
• ...: Extra arguments, including extra formulas (multinomial case) or extra parameters (normal and gamma cases).
• family: a description of the error distribution to be used in the model. For kdglm this can be a character string naming a family function or a family function.
• data: an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which glm is called.
• offset: this can be used to specify an a priori known component to be included in the linear predictor during fitting. This should be NULL or a numeric vector of length equal to the number of cases. One or more offset terms can be included in the formula instead.
• p.monit: numeric (optional): The prior probability of changes in the latent space variables that are not part of its dynamic. Only used when performing sensitivity analysis.

Returns

A fitted_dlm object.

Details

This is the main function of the kDGLM package, as it is used to fit all models.

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

For the details about the methodology see \insertCite ArtigokParametrico;textualkDGLM.

For the details about the Dynamic Linear Models see \insertCite WestHarr-DLM;textualkDGLM and \insertCite Petris-DLM;textualkDGLM.

Examples

# Poisson case
fitted.data <- kdglm(c(AirPassengers) ~ pol(2) + har(12, order = 2), family = Poisson)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

##################################################################

# Multinomial case
chickenPox$Total <- rowSums(chickenPox[, c(2, 3, 4, 6, 5)])
chickenPox$Vaccine <- chickenPox$date >= as.Date("2013-09-01")
fitted.data <- kdglm(`< 5 year` ~ pol(2, D = 0.95) + har(12, D = 0.975) + noise(R1 = 0.1) + Vaccine,
  `5 to 9 years` ~ pol(2, D = 0.95) + har(12, D = 0.975) + noise(R1 = 0.1) + Vaccine,
  `10 to 14 years` ~ pol(2, D = 0.95) + har(12, D = 0.975) + noise(R1 = 0.1) + Vaccine,
  `50 years or more` ~ pol(2, D = 0.95) + har(12, D = 0.975) + noise(R1 = 0.1) + Vaccine,
  N = chickenPox$Total,
  family = Multinom,
  data = chickenPox
)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

##################################################################

# Univariate Normal case
fitted.data <- kdglm(corn.log.return ~ 1, V = ~1, family = Normal, data = cornWheat[1:500, ])
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

##################################################################

# Gamma case
Y <- (cornWheat$corn.log.return[1:500] - mean(cornWheat$corn.log.return[1:500]))**2
fitted.data <- kdglm(Y ~ 1, phi = 0.5, family = Gamma, data = cornWheat)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

See Also

auxiliary functions for creating outcomes Poisson, Multinom, Normal, Gamma

auxiliary functions for creating structural blocks polynomial_block, regression_block, harmonic_block, TF_block

auxiliary functions for defining priors zero_sum_prior, CAR_prior

Other auxiliary functions for fitted_dlm objects: coef.fitted_dlm(), eval_dlm_norm_const(), fit_model(), forecast.fitted_dlm(), simulate.fitted_dlm(), smoothing(), update.fitted_dlm()