Visualizing a fitted kDGLM model

Calculate the predictive mean and some quantile for the observed data and show a plot.

Usage

# S3 method for fitted_dlm
plot(
  x,
  outcomes = NULL,
  latent.states = NULL,
  linear.predictors = NULL,
  pred.cred = 0.95,
  lag = NA,
  cutoff = floor(x$t/10),
  plot.pkg = "auto",
  ...
)

Arguments

x

fitted_dlm object: A fitted DGLM.

outcomes

character: The name of the outcomes to plot.

latent.states

character: The name of the latent states to plot.

linear.predictors

character: The name of the linear predictors to plot.

pred.cred

numeric: The credibility value for the credibility interval.

lag

integer: The number of steps ahead to be used for prediction. If lag<0, the smoothed distribution is used and, if lag==0, the filtered interval.score is used.

cutoff

integer: The number of initial steps that should be skipped in the plot. Usually, the model is still learning in the initial steps, so the predictions are not reliable.

plot.pkg

character: A flag indicating if a plot should be produced. Should be one of 'auto', 'base', 'ggplot2' or 'plotly'.

...

Extra arguments passed to the plot method.

Value

ggplot or plotly object: A plot showing the predictive mean and credibility interval with the observed data.

See also

fit_model

Other auxiliary visualization functions for the fitted_dlm class: plot.dlm_coef(), summary.fitted_dlm(), summary.searched_dlm()

Examples

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)

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

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