knitr::opts_chunk$set( collapse = TRUE, comment = "#>", fig.align = "center", fig.width = 7, fig.height = 5 )

To illustrate the trend fitting functionality of *i2extras* we will use
the simulated Ebola Virus Disease (EVD) outbreak data from the
outbreaks package.

library(outbreaks) library(i2extras) library(ggplot2) raw_dat <- ebola_sim_clean$linelist

For this example we will restrict ourselves to the first 20 weeks of data:

dat <- incidence( raw_dat, date_index = "date_of_onset", interval = "week", groups = "gender" )[1:20, ] dat plot(dat, angle = 45, border_colour = "white")

We can use `fit_curve()`

to fit the data with either a poisson or
negative binomial regression model. The output from this will be a nested
object with class `incidence2_fit`

which has methods available for both
automatic plotting and the calculation of growth (decay) rates and doubling
(halving) times.

out <- fit_curve(dat, model = "poisson", alpha = 0.05) out plot(out, angle = 45, border_colour = "white") growth_rate(out)

To unnest the data we can use `unnest()`

(a function that has been reexported
from the tidyr package.

```
unnest(out, estimates)
```

`fit_curve()`

also works nicely with grouped `incidence2`

objects. In this
situation, we return a nested tibble with some additional columns that
indicate whether there has been a warning or error during the fitting
or prediction stages.

grouped_dat <- incidence( raw_dat, date_index = "date_of_onset", interval = "week", groups = "hospital" )[1:120, ] grouped_dat out <- fit_curve(grouped_dat, model = "poisson", alpha = 0.05) out # plot with a prediction interval but not a confidence interval plot(out, ci = FALSE, pi=TRUE, angle = 45, border_colour = "white") growth_rate(out)

We provide helper functions, `is_ok()`

, `is_warning()`

and `is_error()`

to
help filter the output as necessary.

out <- fit_curve(grouped_dat, model = "negbin", alpha = 0.05) is_warning(out) unnest(is_warning(out), fitting_warning)

We can add a rolling average, across current and previous intervals, to
an `incidence2`

object with the `add_rolling_average()`

function:

ra <- add_rolling_average(grouped_dat, n = 2L) # group observations with the 2 prior plot(ra, border_colour = "white", angle = 45) + geom_line(aes(x = date_index, y = rolling_average))

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