Plot an incidence timeseries

Usage

plot_incidence(
  modelled,
  raw = i_incidence_data,
  ...,
  mapping = .check_for_aes(modelled, ...),
  events = i_events
)

Arguments

modelled

An optional estimate of the incidence time series. If modelled is missing then it is estimated from raw using a poisson_locfit_model. In this case parameters window and deg may be supplied to control the fit. modelled can also be the output from normalise_incidence in which case the plot uses the per capita rates calculated by that function. - EITHER: a dataframe with columns:

time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`
incidence.per_capita.fit (double) - an estimate of the incidence per capita rate on a log scale
incidence.per_capita.se.fit (positive_double) - the standard error of the incidence per capita rate estimate on a log scale
incidence.per_capita.0.025 (positive_double) - lower confidence limit of the incidence per capita rate (true scale)
incidence.per_capita.0.5 (positive_double) - median estimate of the incidence per capita rate (true scale)
incidence.per_capita.0.975 (positive_double) - upper confidence limit of the incidence per capita rate (true scale)
population_unit (double) - The population unit on which the per capita incidence rate is calculated
time_unit (lubridate::as.period) - The time period over which the per capita incidence rate is calculated

Any grouping allowed.

OR with columns:

time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`
incidence.fit (double) - an estimate of the incidence rate on a log scale
incidence.se.fit (positive_double) - the standard error of the incidence rate estimate on a log scale
incidence.0.025 (positive_double) - lower confidence limit of the incidence rate (true scale)
incidence.0.5 (positive_double) - median estimate of the incidence rate (true scale)
incidence.0.975 (positive_double) - upper confidence limit of the incidence rate (true scale)

Any grouping allowed.

raw

The raw count data (optional - if given overlaid as points on top of modelled estimate) - a dataframe with columns:

count (positive_integer) - Positive case counts associated with the specified time frame
time (ggoutbreak::time_period + group_unique) - A (usually complete) set of singular observations per unit time as a `time_period`

Any grouping allowed.

...

Named arguments passed on to geom_events

events

Significant events or time spans - a dataframe with 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

Any grouping allowed.

A default value is defined.

Named arguments passed on to poisson_locfit_model

window: a number of data points defining the bandwidth of the estimate, smaller values result in less smoothing, large value in more. The default value of 14 is calibrated for data provided on a daily frequency, with weekly data a lower value may be preferred. - default 14
deg: polynomial degree (min 1) - higher degree results in less smoothing, lower values result in more smoothing. A degree of 1 is fitting a linear model piece wise. - default 2
frequency: the density of the output estimates as a time period such as 7 days or 2 weeks. - default 1 day

Named arguments passed on to geom_truth

true_df

a data frame with a time_period column called time and a value column (name given in true_col). This is optional and will be picked up from the raw parameter if given.

true_col

the column name / expression of the true value

true_fmt

a list of ggplot formatting to apply to the true value timeseries

...

Named arguments passed on to as.time_period

unit: the length of one unit of time. This will be either a integer number of days, or a specification such as "1 week", or another time_period. If x is a time_period, and the unit is different to that of x this will return a rescaled time_period using the new units.
start_date: the zero time date as something that can be coerced to a date. If the x input is already a time_period and this is different to its start_date then x will be recalibrated to use the new start date.
anchor: only relevant if x is a vector of dates, this is a date, or "start" or "end" or a weekday name e.g. "mon". With the vector of dates in x it will use this anchor to find a reference date for the time-series. If not provided then the current defaults will be used. (see set_defaults())

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 with 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

Any grouping allowed.

A default value is defined.

Value

a ggplot object

Examples

# example code

tmp = example_poisson_rt_2class()
tmp2 = tmp %>% poisson_locfit_model()

if(interactive()) {
  plot_incidence(tmp2,tmp,size=0.25)
}