Description Usage Arguments Value Author(s) See Also Examples
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 pipingfriendly
function add_incidence_fit()
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 
x 
An incidence object, generated by the function

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 
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 ( 
separate_split 
If groups are present, should separate split dates be
determined for each group? Defaults to 
... 
currently unused. 
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 $info
item is a list containing:
r
: the growth rate
r.conf
: the confidence interval of 'r'
pred
: a data.frame
containing predictions of the model,
including the true dates (dates
), their numeric version used in the
model (dates.x
), the predicted value (fit
), and the lower
(lwr
) and upper (upr
) bounds of the associated confidence
interval.
doubling
: the predicted doubling time in days; exists only if 'r' is
positive
doubling.conf
: the confidence interval of the doubling time
halving
: the predicted halving time in days; exists only if 'r' is
negative
halving.conf
: the confidence interval of the halving time
For fit_optim_split
, a list containing:
df
: a data.frame
of dates that were used in the optimization
procedure, and the corresponding average R2 of the resulting models.
split
: the optimal splitting date
fit
: an incidence_fit_list
object containing the fit for each split.
If the separate_split = TRUE
, then the incidence_fit_list
object will
contain these splits nested within each group. All of the incidence_fit
objects can be retrieved with get_fit()
.
plot
: a plot showing the content of df
(ggplot2 object)
Thibaut Jombart thibautjombart@gmail.com, Zhian N. Kamvar zkamvar@gmail.com.
the incidence()
function to generate the 'incidence'
objects. The get_fit()
function to flatten incidence_fit_list
objects to
a list of incidence_fit
objects.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41  if (require(outbreaks)) { withAutoprint({
dat < ebola_sim$linelist$date_of_onset
## EXAMPLE WITH A SINGLE MODEL
## compute weekly incidence
i.7 < incidence(dat, interval=7)
plot(i.7)
plot(i.7[1:20])
## fit a model on the first 20 weeks
f < fit(i.7[1:20])
f
names(f)
head(get_info(f, "pred"))
## plot model alone (not recommended)
plot(f)
## plot data and model (recommended)
plot(i.7, fit = f)
plot(i.7[1:25], fit = f)
## piping versions
if (require(magrittr)) { withAutoprint({
plot(i.7) %>% add_incidence_fit(f)
## EXAMPLE WITH 2 PHASES
## specifying the peak manually
f2 < fit(i.7, split = as.Date("20141015"))
f2
plot(i.7) %>% add_incidence_fit(f2)
## finding the best 'peak' date
f3 < fit_optim_split(i.7)
f3
plot(i.7) %>% add_incidence_fit(f3$fit)
})}
})}

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