plot.dlm_coef function

Visualizing latent states in a fitted kDGLM model

## S3 method for class 'dlm_coef'
plot(
  x,
  var = rownames(x$theta.mean)[x$dynamic],
  cutoff = floor(t/10),
  pred.cred = 0.95,
  plot.pkg = "auto",
  ...
)

Arguments

• x: dlm_coef object: The coefficients of a fitted DGLM model.
• var: character: The name of the variables to plot (same value passed while creating the structure). Any variable whose name partially match this variable will be plotted.
• 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 estimated values are not reliable.
• pred.cred: numeric: The credibility value for the credibility interval.
• 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
)

model.coef <- coef(fitted.data)

plot(model.coef)$plot

See Also

fit_model,coef

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