Visualizing latent states in a fitted kDGLM model

Visualizing latent states in a fitted kDGLM model

Usage

# S3 method for 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.

Value

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

See also

fit_model,coef

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

model.coef <- coef(fitted.data)

plot(model.coef)$plot
#> NULL