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".

## 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"
)
# }
```