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Visualize the stability of HMM states and thresholds

Source: R/create_loop_plots.R
create_loop_plots.Rd

Generates plots to visualize how HMM state parameters and resulting QUEST thresholds evolve as the model is refitted over different periods (cutoff dates).

Usage

create_loop_plots(list_results, print = FALSE)

Arguments

list_results

An object of class epiquest_loop produced by run_loop_thresholds().

print

A logical. If TRUE, all generated plots are printed to the active graphics device.

Details

It is important to assess HMM and threshold stability, i.e., that adding or removing a few weeks of data does not meaningfully change the results. The 'cutoff date' on the horizontal axis represents the end of the data window used for that specific model fit.

#' @return An object of class epiquest_plot_list, a named list of ggplot2 objects containing:

  • thresholds: Threshold values plotted against cutoff dates, with the raw surveillance signal shown in the background for context.

  • states_facet: A faceted plot showing the evolution of the estimated mean (and standard deviation) for each hidden state.

  • states: A plot of the mean values for each state over time, including shaded ribbons representing ±1 and ±2 standard deviations to visualize state overlap and uncertainty.

See also

generate_palette_thresholds, generate_palette_hmm

Examples

if (FALSE) { # \dontrun{
# Stability analysis takes time as it refits the model repeatedly
fit_loop <- loop_thresholds(df_sari_be, n_states = 2)

# Look at results
summary(fit_loop)

# Visualize results
create_loop_plots(fit_loop)
} # }