Plot Estimates

Allows users to plot the output from estimate_infections easily. In future releases it may be depreciated in favour of increasing the functionality of the S3 plot methods.

Usage

plot_estimates(
  estimate,
  reported,
  ylab = "Cases",
  hline,
  obs_as_col = TRUE,
  max_plot = 10,
  estimate_type = NULL
)

Arguments

estimate

A data.table of estimates containing the following variables: date, type (must contain "estimate", "estimate based on partial data" and optionally "forecast").

reported

A data.table of reported cases with the following variables: date, confirm.

ylab

Character string, defaulting to "Cases". Title for the plot y axis.

hline

Numeric, if supplied gives the horizontal intercept for a indicator line.

obs_as_col

Logical, defaults to TRUE. Should observed data, if supplied, be plotted using columns or as points (linked using a line).

max_plot

Numeric, defaults to 10. A multiplicative upper bound on the\ number of cases shown on the plot. Based on the maximum number of reported cases.

estimate_type

Character vector indicating the type of data to plot. Default to all types with supported options being: "Estimate", "Estimate based on partial data", and "Forecast".

Value

A ggplot2 object

Examples

# \donttest{
# define example cases
cases <- example_confirmed[1:40]

# set up example delays
generation_time <- get_generation_time(
 disease = "SARS-CoV-2", source = "ganyani"
)
incubation_period <- get_incubation_period(
 disease = "SARS-CoV-2", source = "lauer"
)
reporting_delay <- estimate_delay(rlnorm(100, log(6), 1), max_value = 10)
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.
#> Warning: `samples` must be at least 1000. Now setting it to 1000 internally.

# run model
out <- estimate_infections(cases,
  generation_time = generation_time_opts(generation_time),
  delays = delay_opts(incubation_period + reporting_delay)
)
#> Warning: There were 10 divergent transitions after warmup. See
#> https://mc-stan.org/misc/warnings.html#divergent-transitions-after-warmup
#> to find out why this is a problem and how to eliminate them.
#> Warning: Examine the pairs() plot to diagnose sampling problems
# plot infections
plot_estimates(
  estimate = out$summarised[variable == "infections"],
  reported = cases,
  ylab = "Cases", max_plot = 2
) + ggplot2::facet_wrap(~type, scales = "free_y")


# plot reported cases estimated via Rt
plot_estimates(
  estimate = out$summarised[variable == "reported_cases"],
  reported = cases,
  ylab = "Cases"
)


# plot Rt estimates
plot_estimates(
  estimate = out$summarised[variable == "R"],
  ylab = "Effective Reproduction No.",
  hline = 1
)


#' # plot Rt estimates without forecasts
plot_estimates(
  estimate = out$summarised[variable == "R"],
  ylab = "Effective Reproduction No.",
  hline = 1, estimate_type = "Estimate"
)

# }