kDGLM (version 1.2.0)

fit_model: Fitting kDGLM models

Description

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

Usage

fit_model(
  ...,
  smooth = TRUE,
  p.monit = NA,
  condition = "TRUE",
  metric = "log.like",
  lag = 1,
  pred.cred = 0.95,
  metric.cutoff = NA,
  save.models = FALSE,
  silent = FALSE
)

Value

A fitted_dlm object.

Arguments

...

dlm_block or dlm_distr objects or named values: The structural blocks of the model (dlm_block objects), alongside the model outcomes (dlm_distr object). If at least one block is undefined, the user must also provide its value in this argument (see last example).

smooth

boolean: A flag indicating if the smoothed distribution for the latent states should be calculated.

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.

condition

character: A character defining which combinations of undefined hyper parameter should be tested. See example for details. Only used when performing sensitivity analysis.

metric

character: The name of the metric to use for model selection. One of log-likelihood for the one-step-ahead prediction ("log.like"), Mean Absolute Scaled Error ("mase") masekDGLM or Interval Score ("interval.score") interval_scorekDGLM. Only used when performing sensitivity analysis.

lag

integer: The number of steps ahead used for the prediction when calculating the metrics. If lag<0, predictions are made using the smoothed distribution of the latent states. Only used when performing sensitivity analysis.

pred.cred

numeric: A number between 0 and 1 (not included) indicating the credibility interval for predictions. If not within the valid range of values, 0.95 will be used. Only used when performing sensitivity analysis.

metric.cutoff

integer: The number of observations to ignore when calculating the metrics. Default is 1/10 of the number of observations (rounded down). Only used when performing sensitivity analysis.

save.models

boolean: A flag indicating if all evaluated models should be saved. If FALSE, only the best model (according to the chosen metric) will be saved. Only used when performing sensitivity analysis.

silent

boolean: A flag indicating if a progress bar should be printed. Only used when performing sensitivity analysis.

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 ArtigoPacote;textualkDGLM.

For the details about the methodology see ArtigokParametrico;textualkDGLM.

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

See Also

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

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(), forecast.fitted_dlm(), simulate.fitted_dlm(), smoothing(), update.fitted_dlm()

Examples

# Poisson case
data <- c(AirPassengers)

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

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

# Multinomial case
structure <- (
  polynomial_block(p = 1, order = 2, D = 0.95) +
    harmonic_block(p = 1, period = 12, D = 0.975) +
    noise_block(p = 1, R1 = 0.1) +
    regression_block(p = chickenPox$date >= as.Date("2013-09-01"))
  # Vaccine was introduced in September of 2013
) * 4

outcome <- Multinom(p = structure$pred.names, data = chickenPox[, c(2, 3, 4, 6, 5)])
fitted.data <- fit_model(structure, chickenPox = outcome)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

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

# Univariate Normal case
structure <- polynomial_block(mu = 1, D = 0.95) +
  polynomial_block(V = 1, D = 0.95)

outcome <- Normal(mu = "mu", V = "V", data = cornWheat$corn.log.return[1:500])
fitted.data <- fit_model(structure, corn = outcome)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

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

# Bivariate Normal case
structure <- (polynomial_block(mu = 1, D = 0.95) +
  polynomial_block(V = 1, D = 0.95)) * 2 +
  polynomial_block(rho = 1, D = 0.95)

outcome <- Normal(
  mu = c("mu.1", "mu.2"),
  V = matrix(c("V.1", "rho", "rho", "V.2"), 2, 2),
  data = cornWheat[1:500, c(4, 5)]
)
fitted.data <- fit_model(structure, cornWheat = outcome)
summary(fitted.data)
plot(fitted.data, plot.pkg = "base")

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

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

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

##################################################################
# \donttest{
# Sensitivity analysis
data <- c(AirPassengers)

level <- polynomial_block(rate = 1, order = 2, D = "D.level")
season <- harmonic_block(rate = "sazo.effect", order = 2, period = 12, D = "D.sazo")

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

fit_model(level, season, outcome,
  sazo.effect = c(0, 1),
  D.level = c(seq.int(0.8, 1, l = 11)),
  D.sazo = c(seq.int(0.95, 1, l = 11)),
  condition = "sazo.effect==1 | D.sazo==1"
)
# }