Plot a multinomial proportions mode

Usage

plot_multinomial(
  modelled = i_multinomial_proportion_model,
  ...,
  mapping = ggplot2::aes(fill = class),
  events = i_events,
  normalise = FALSE
)

Arguments

modelled

the multinomial count data

A dataframe containing the following columns:

time (as.time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`
class (factor) - A factor specifying the type of observation. This will be things like variant, or serotype, for a multinomial model. Any missing data points are ignored.
proportion.0.5 (proportion) - median estimate of proportion (true scale)

Must be grouped by: class (exactly).

No default value.

...

Arguments passed on to geom_events

mapping

a ggplot2::aes mapping. Most importantly setting the colour to something if there are multiple incidence timeseries in the plot

events

Significant events or time spans

A dataframe containing the following columns:

label (character) - the event label
start (date) - the start date, or the date of the event
end (date) - the end date or NA if a single event

No mandatory groupings.

A default value is defined.

normalise

make sure the probabilities add up to one - this can be a bad idea if you know you may have missing values.

Value

a ggplot

Examples

tmp = growthrates::england_covid %>%
  growthrates::proportion_locfit_model(window=21) %>%
  dplyr::glimpse()
#> Rows: 26,790
#> Columns: 12
#> Groups: class [19]
#> $ class                  <fct> 00_04, 00_04, 00_04, 00_04, 00_04, 00_04, 00_04…
#> $ time                   <time_prd> 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, …
#> $ proportion.fit         <dbl> -13.433629, -13.178345, -12.898497, -12.600007,…
#> $ proportion.se.fit      <dbl> 51.598289, 49.954079, 48.024633, 45.878749, 43.…
#> $ proportion.0.025       <dbl> 1.759164e-50, 5.698079e-49, 3.308357e-47, 2.991…
#> $ proportion.0.5         <dbl> 1.465037e-06, 1.891110e-06, 2.501801e-06, 3.371…
#> $ proportion.0.975       <dbl> 1.0000000, 1.0000000, 1.0000000, 1.0000000, 1.0…
#> $ relative.growth.fit    <dbl> 0.24102860, 0.24048966, 0.23901181, 0.23680352,…
#> $ relative.growth.se.fit <dbl> 1.2309119, 1.2257057, 1.2114298, 1.1900979, 1.1…
#> $ relative.growth.0.025  <dbl> -2.1715143, -2.1618494, -2.1353470, -2.0957455,…
#> $ relative.growth.0.5    <dbl> 0.24102860, 0.24048966, 0.23901181, 0.23680352,…
#> $ relative.growth.0.975  <dbl> 2.6535715, 2.6428288, 2.6133706, 2.5693525, 2.5…

plot_multinomial(tmp, normalise=TRUE)+
  ggplot2::scale_fill_viridis_d()