plot.fitted_dlm function

Visualizing a fitted kDGLM model

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

## S3 method for class '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.

Returns

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

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")

See Also

fit_model

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