Description Usage Arguments Details Value Author(s) See Also Examples
View source: R/estimate_peak.R
This function can be used to estimate the peak of an epidemic curve stored as
incidence
, using bootstrap. See bootstrap for more information
on the resampling.
1  estimate_peak(x, n = 100, alpha = 0.05)

x 
An 
n 
The number of bootstrap datasets to be generated; defaults to 100. 
alpha 
The type 1 error chosen for the confidence interval; defaults to 0.05. 
Input dates are resampled with replacement to form bootstrapped datasets; the peak is reported for each, resulting in a distribution of peak times. When there are ties for peak incidence, only the first date is reported.
Note that the bootstrapping approach used for estimating the peak time makes the following assumptions:
the total number of event is known (no uncertainty on total incidence)
dates with no events (zero incidence) will never be in bootstrapped datasets
the reporting is assumed to be constant over time, i.e. every case is equally likely to be reported
A list containing the following items:
observed
: the peak incidence of the original dataset
estimated
: the mean peak time of the bootstrap datasets
ci
: the confidence interval based on bootstrap datasets
peaks
: the peak times of the bootstrap datasets
Thibaut Jombart thibautjombart@gmail.com, with inputs on caveats from Michael HÃ¶hle.
bootstrap for the bootstrapping underlying this
approach and find_peak to find the peak in a single
incidence
object.
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  if (require(outbreaks) && require(ggplot2)) { withAutoprint({
i < incidence(fluH7N9_china_2013$date_of_onset)
i
plot(i)
## one simple bootstrap
x < bootstrap(i)
x
plot(x)
## find 95% CI for peak time using bootstrap
peak_data < estimate_peak(i)
peak_data
summary(peak_data$peaks)
## show confidence interval
plot(i) + geom_vline(xintercept = peak_data$ci, col = "red", lty = 2)
## show the distribution of bootstrapped peaks
df < data.frame(peak = peak_data$peaks)
plot(i) + geom_density(data = df,
aes(x = peak, y = 10 * ..scaled..),
alpha = .2, fill = "red", color = "red")
})}

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