# fit

##### Fit exponential models to incidence data

The function `fit`

fits two exponential models to incidence data, of the
form: \(log(y) = r * t + b\) where 'y' is the incidence, 't' is time
(in days), 'r' is the growth rate, and 'b' is the origin. The function `fit`

will fit one model by default, but will fit two models on either side of a
splitting date (typically the peak of the epidemic) if the argument `split`

is provided. When groups are present, these are included in the model as main
effects and interactions with dates. The function `fit_optim_split()`

can be
used to find the optimal 'splitting' date, defined as the one for which the
best average R2 of the two models is obtained. Plotting can be done using
`plot`

, or added to an existing incidence plot by the piping-friendly
function `add_incidence_fit()`

.

##### Usage

`fit(x, split = NULL, level = 0.95, quiet = FALSE)`fit_optim_split(
x,
window = x$timespan/4,
plot = TRUE,
quiet = TRUE,
separate_split = TRUE
)

# S3 method for incidence_fit
print(x, ...)

# S3 method for incidence_fit_list
print(x, ...)

##### Arguments

- x
An incidence object, generated by the function

`incidence()`

. For the plotting function, an`incidence_fit`

object.- split
An optional time point identifying the separation between the two models. If NULL, a single model is fitted. If provided, two models would be fitted on the time periods on either side of the split.

- level
The confidence interval to be used for predictions; defaults to 95%.

- quiet
A logical indicating if warnings from

`fit`

should be hidden; FALSE by default. Warnings typically indicate some zero incidence, which are removed before performing the log-linear regression.- window
The size, in days, of the time window either side of the split.

- plot
A logical indicating whether a plot should be added to the output (

`TRUE`

, default), showing the mean R2 for various splits.- separate_split
If groups are present, should separate split dates be determined for each group? Defaults to

`TRUE`

, in which separate split dates and thus, separate models will be constructed for each group. When`FALSE`

, the split date will be determined from the pooled data and modelled with the groups as main effects and interactions with date.- ...
currently unused.

##### Value

For `fit()`

, a list with the class `incidence_fit`

(for a
single model), or a list containing two `incidence_fit`

objects (when
fitting two models). `incidence_fit`

objects contain:

`$model`

: the fitted linear model`$info`

: a list containing various information extracted from the model (detailed further)`$origin`

: the date corresponding to day '0'

The `$info`

item is a list containing:

`r`

: the growth rate`r.conf`

: the confidence interval of 'r'`pred`

: a`data.frame`

containing predictions of the model, including the true dates (`dates`

), their numeric version used in the model (`dates.x`

), the predicted value (`fit`

), and the lower (`lwr`

) and upper (`upr`

) bounds of the associated confidence interval.`doubling`

: the predicted doubling time in days; exists only if 'r' is positive`doubling.conf`

: the confidence interval of the doubling time`halving`

: the predicted halving time in days; exists only if 'r' is negative`halving.conf`

: the confidence interval of the halving time

For `fit_optim_split`

, a list containing:

`df`

: a`data.frame`

of dates that were used in the optimization procedure, and the corresponding average R2 of the resulting models.`split`

: the optimal splitting date`fit`

: an`incidence_fit_list`

object containing the fit for each split. If the`separate_split = TRUE`

, then the`incidence_fit_list`

object will contain these splits nested within each group. All of the`incidence_fit`

objects can be retrieved with`get_fit()`

.`plot`

: a plot showing the content of`df`

(ggplot2 object)

##### See Also

the `incidence()`

function to generate the 'incidence'
objects. The `get_fit()`

function to flatten `incidence_fit_list`

objects to
a list of `incidence_fit`

objects.

##### Examples

```
# NOT RUN {
if (require(outbreaks)) { withAutoprint({
dat <- ebola_sim$linelist$date_of_onset
## EXAMPLE WITH A SINGLE MODEL
## compute weekly incidence
i.7 <- incidence(dat, interval=7)
plot(i.7)
plot(i.7[1:20])
## fit a model on the first 20 weeks
f <- fit(i.7[1:20])
f
names(f)
head(get_info(f, "pred"))
## plot model alone (not recommended)
plot(f)
## plot data and model (recommended)
plot(i.7, fit = f)
plot(i.7[1:25], fit = f)
## piping versions
if (require(magrittr)) { withAutoprint({
plot(i.7) %>% add_incidence_fit(f)
## EXAMPLE WITH 2 PHASES
## specifying the peak manually
f2 <- fit(i.7, split = as.Date("2014-10-15"))
f2
plot(i.7) %>% add_incidence_fit(f2)
## finding the best 'peak' date
f3 <- fit_optim_split(i.7)
f3
plot(i.7) %>% add_incidence_fit(f3$fit)
})}
})}
# }
```

*Documentation reproduced from package incidence, version 1.7.1, License: MIT + file LICENSE*