Plot an incidence or proportion versus growth phase diagram

Usage

plot_growth_phase(
  modelled,
  timepoints = NULL,
  duration = max(dplyr::count(modelled)$n),
  interval = 7,
  mapping = if (interfacer::is_col_present(modelled, class)) {
     ggplot2::aes(colour =
    class)
 } else {
     ggplot2::aes()
 },
  cis = TRUE,
  ...
)

Arguments

modelled

Growth rates and incidence / proportion timeseries as the outputs of functions such as proportion_locfit_model, poisson_locfit_model, or similar. - 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
growth.fit (double) - an estimate of the growth rate
growth.se.fit (positive_double) - the standard error the growth rate
growth.0.025 (double) - lower confidence limit of the growth rate
growth.0.5 (double) - median estimate of the growth rate
growth.0.975 (double) - upper confidence limit of the growth rate

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)
growth.fit (double) - an estimate of the growth rate
growth.se.fit (positive_double) - the standard error the growth rate
growth.0.025 (double) - lower confidence limit of the growth rate
growth.0.5 (double) - median estimate of the growth rate
growth.0.975 (double) - upper confidence limit of the growth rate

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`
risk_ratio.0.025 (positive_double) - lower confidence limit of the excess risk ratio for a population group
risk_ratio.0.5 (positive_double) - median estimate of the excess risk ratio for a population group
risk_ratio.0.975 (positive_double) - upper confidence limit of the excess risk ratio for a population group
proportion.fit (double) - an estimate of the proportion on a logit scale
proportion.se.fit (positive_double) - the standard error of proportion estimate on a logit scale
proportion.0.025 (proportion) - lower confidence limit of proportion (true scale)
proportion.0.5 (proportion) - median estimate of proportion (true scale)
proportion.0.975 (proportion) - upper confidence limit of proportion (true scale)
relative.growth.fit (double) - an estimate of the relative growth rate
relative.growth.se.fit (positive_double) - the standard error the relative growth rate
relative.growth.0.025 (double) - lower confidence limit of the relative growth rate
relative.growth.0.5 (double) - median estimate of the relative growth rate
relative.growth.0.975 (double) - upper confidence limit of the relative growth rate

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`
proportion.fit (double) - an estimate of the proportion on a logit scale
proportion.se.fit (positive_double) - the standard error of proportion estimate on a logit scale
proportion.0.025 (proportion) - lower confidence limit of proportion (true scale)
proportion.0.5 (proportion) - median estimate of proportion (true scale)
proportion.0.975 (proportion) - upper confidence limit of proportion (true scale)
relative.growth.fit (double) - an estimate of the relative growth rate
relative.growth.se.fit (positive_double) - the standard error the relative growth rate
relative.growth.0.025 (double) - lower confidence limit of the relative growth rate
relative.growth.0.5 (double) - median estimate of the relative growth rate
relative.growth.0.975 (double) - upper confidence limit of the relative growth rate

Any grouping allowed.

OR with NULL

timepoints

time points (as Date or time_period vector) of dates to plot phase diagrams. If multiple this will result in a sequence of plots as facets. If NULL (the default) it will be the last time point in the series

duration

the length of the growth rate phase trail

interval

the length of time between markers on the phase plot

mapping

a ggplot2::aes() mapping

cis

logical; should the phases be marked with confidence intervals?

...

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 ggplot2::facet_wrap

facets

A set of variables or expressions quoted by vars() and defining faceting groups on the rows or columns dimension. The variables can be named (the names are passed to labeller).

For compatibility with the classic interface, can also be a formula or character vector. Use either a one sided formula, ~a + b, or a character vector, c("a", "b").

nrow,ncol

Number of rows and columns.

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")?

space

If "fixed" (default), all panels have the same size and the number of rows and columns in the layout can be arbitrary. If "free_x", panels have widths proportional to the length of the x-scale, but the layout is constrained to one row. If "free_y", panels have heights proportional to the length of the y-scale, but the layout is constrained to one column.

shrink

If TRUE, will shrink scales to fit output of statistics, not raw data. If FALSE, will be range of raw data before statistical summary.

labeller

A function that takes one data frame of labels and returns a list or data frame of character vectors. Each input column corresponds to one factor. Thus there will be more than one with vars(cyl, am). Each output column gets displayed as one separate line in the strip label. This function should inherit from the "labeller" S3 class for compatibility with labeller(). You can use different labeling functions for different kind of labels, for example use label_parsed() for formatting facet labels. label_value() is used by default, check it for more details and pointers to other options.

as.table

The as.table argument is now absorbed into the dir argument via the two letter options. If TRUE, the facets are laid out like a table with highest values at the bottom-right. If FALSE, the facets are laid out like a plot with the highest value at the top-right.

switch

By default, the labels are displayed on the top and right of the plot. If "x", the top labels will be displayed to the bottom. If "y", the right-hand side labels will be displayed to the left. Can also be set to "both".

drop

If TRUE, the default, all factor levels not used in the data will automatically be dropped. If FALSE, all factor levels will be shown, regardless of whether or not they appear in the data.

dir

Direction: either "h" for horizontal, the default, or "v", for vertical. When "h" or "v" will be combined with as.table to set final layout. Alternatively, a combination of "t" (top) or "b" (bottom) with "l" (left) or "r" (right) to set a layout directly. These two letters give the starting position and the first letter gives the growing direction. For example "rt" will place the first panel in the top-right and starts filling in panels right-to-left.

strip.position

By default, the labels are displayed on the top of the plot. Using strip.position it is possible to place the labels on either of the four sides by setting strip.position = c("top", "bottom", "left", "right")

axes

Determines which axes will be drawn in case of fixed scales. When "margins" (default), axes will be drawn at the exterior margins. "all_x" and "all_y" will draw the respective axes at the interior panels too, whereas "all" will draw all axes at all panels.

axis.labels

Determines whether to draw labels for interior axes when the scale is fixed and the axis argument is not "margins". When "all" (default), all interior axes get labels. When "margins", only the exterior axes get labels, and the interior axes get none. When "all_x" or "all_y", only draws the labels at the interior axes in the x- or y-direction respectively.

Value

a ggplot

Examples


data = ggoutbreak::test_poisson_rt_2class
tmp2 = data %>% poisson_locfit_model()

timepoints = as.Date(tmp2$time[c(40,80,120,160)])

if(interactive()) {
  plot_growth_phase(tmp2, timepoints, duration=108)
}